INTRODUCTION
Background of tower crane for high-rise construction in Vietnam
A renowned construction company in Vietnam has strategically chosen tower cranes manufactured by Manitowoc's Potain brand to support their ambitious expansion endeavors [1] Their extensive evaluation has identified the Potain MCT 205 tower crane as an ideal solution for their specific requirements This recognition has led the company to integrate a fleet comprising more than 40 Potain cranes, encompassing the MR 418 luffing jib cranes and MCT 205 tower cranes, into their ongoing and upcoming projects
These Potain cranes have assumed a pivotal role in constructing Landmark 81, a monumental structure that proudly stands as Vietnam's tallest building and ranks among the tallest in Asia [2] Situated in the vibrant city of Ho Chi Minh, this architectural masterpiece has indelibly reshaped the city's skyline, effectively redefining its modern landscape By harnessing the capabilities of Potain cranes, the construction company has exemplified their commitment to delivering exceptional construction projects that contribute to the city's progressive growth and cultural identity
The profound impact of collaboration with Potain is visible in the transformation of Ho Chi Minh City's skyline These iconic structures, made possible through Potain cranes, are enduring symbols of Vietnam's architectural prowess and emergence as a dynamic global hub
In addition to a leading Vietnamese engineering and construction conglomerate, tower cranes have been effectively used for numerous notable projects, showcasing their expertise in the field One remarkable collaboration occurred with a construction company, where seven Liebherr tower cranes were successfully employed for the prestigious construction of a tower in Hanoi This awe-inspiring project, designed by an acclaimed architecture firm based in the UK, highlights exceptional construction capabilities and stands as Vietnam's tallest tower, a testament to the country's progress and urban development [3]
The strategic deployment of tower cranes within the VietinBank Towers project underscores their indispensable role in constructing iconic and ambitious structures representing Vietnam's growth Tower cranes, renowned for their towering height and robust lifting capacity, enable the efficient movement of heavy materials and equipment during construction Their precise positioning and adaptability contribute to improved construction timelines, enhanced safety, and cost-effectiveness
By harnessing the power of tower cranes, the construction industry in Vietnam has exemplified the significance of these advanced construction tools in shaping the country's urban landscape The utilization of tower cranes in various projects not only underscores their commitment to innovation and excellence but also contributes to Vietnam's architectural legacy and economic advancement These towering structures stand as testaments to the country's progress and serve as iconic symbols of its growth and development
One key aspect that sets tower cranes apart is their unparalleled lifting capacity and height capabilities These towering giants can hoist and transport heavy construction materials and equipment to great heights with utmost precision and efficiency This exceptional lifting capacity empowers construction companies to
3 undertake ambitious and grand projects that were once deemed impossible, enabling the creation of iconic landmarks that symbolize Vietnam's progress and prosperity Moreover, tower cranes offer significant advantages in terms of time and cost savings during construction Their strategic placement at construction sites optimizes workflow and maximizes productivity Their ability to reach towering heights eliminates the need for multiple cranes, reducing equipment and labor costs Additionally, their adaptability and versatility allow a seamless integration into diverse construction projects, catering to various architectural designs and structural requirements This adaptability, combined with their robustness and stability, ensures a safe and secure working environment for construction workers, minimizing the risk of accidents and injuries
Tower cranes also contribute to advancing sustainable practices within the construction industry By enabling the construction of taller buildings, they promote vertical expansion rather than horizontal sprawl, effectively utilizing limited land resources This vertical growth helps preserve valuable green spaces and promotes compact urban development, reducing the environmental impact associated with urbanization [4] Furthermore, their precision and accuracy in lifting and placing materials minimize material wastage, contributing to efficient resource management and reducing construction waste, an essential step toward sustainable development Consequently, the presence of tower cranes has elevated the overall competency and professionalism of the construction industry in Vietnam.
Purpose and significance of the research topic
The thesis aim to optimize the problem of crane allocation because while it may seem that there are limited positions to choose from on construction sites, the placement of tower cranes can significantly impact the efficiency and cost-effectiveness of high- rise construction projects in HCMC, Vietnam Tower cranes play a critical role in vertical material and equipment transportation, and their proper layout planning is vital for project success
Although there may not be an infinite number of positions for tower crane placement, the available options still present a complex search space Factors such as building height, project layout, proximity to critical areas, and safety considerations all influence the optimal placement of tower cranes Consequently, determining the best configuration among these various factors becomes a challenging optimization problem
By employing MOCBO, the research aims to address the intricacies of this crane layout planning problem effectively MOCBO allows the consideration of multiple objectives, such as minimizing operation time and cost simultaneously This approach enables the exploration of various trade-offs and potential solutions that can lead to significant improvements in project efficiency
Moreover, the research intends to identify the tools and techniques currently used by contractors for tower crane layout planning and evaluate their effectiveness
By doing so, it can provide a comprehensive understanding of the existing practices and challenges faced in TCLP, which can further highlight the significance of developing an optimization model
Overall, even though the physical positions for tower cranes may seem limited, the complexity lies in finding the most suitable arrangement that aligns with multiple objectives while adhering to site constraints and safety requirements Therefore, the chosen optimization problem holds substantial practical implications for enhancing construction efficiency and cost-effectiveness in HCMC and potentially other regions facing similar challenges in tower crane layout planning.
Objectives of the thesis
This thesis focuses on optimizing TCLP for high-rise construction projects using MOCBO The specific objectives of the study are outlined below:
Objective 1: Assessing the current TCLP practices in high-rise construction projects
This objective entails evaluating the existing practices employed by contractors when determining the layout of tower cranes in high-rise construction projects in Ho Chi Minh City The assessment includes a comprehensive examination of the current methods, techniques, and decision-making processes By conducting this analysis, the thesis aims to identify the strengths and weaknesses of the current practices and pinpoint potential areas for improvement
Objective 2: Minimizing the operation time of tower cranes in high-rise construction projects
The second objective concentrates on reducing the operation time of tower cranes in high-rise construction projects By optimizing the layout of tower cranes, the goal is to minimize the time required for crane movements, repositioning, and material handling This optimization aims to enhance overall project efficiency by streamlining crane operations and reducing unnecessary delays or disruptions
Objective 3: Minimizing the cost associated with tower crane operations in high-rise construction projects
The third objective focuses on cost minimization in tower crane operations for high-rise construction projects It considers various factors such as crane rental costs, fuel consumption, maintenance expenses, and labor costs associated with tower crane operations The objective seeks to optimize the tower crane layout to minimize these costs, ultimately improving project profitability and financial performance
By leveraging the MOCBO technique, the thesis aims to achieve a balance between the second and third objectives, optimizing both the operation time and cost associated with tower crane activities in high-rise construction projects in Ho Chi Minh City, Vietnam The MOCBO approach will enable the exploration of multiple solutions that optimize different objectives simultaneously, providing valuable insights and decision support for stakeholders involved in TCLP.
The thesis scope and limitation
The study uses a multi-objective colliding bodies optimization approach to planning the tower crane layout for high-rise construction projects in Ho Chi Minh City, Vietnam Therefore, it is essential to note that the findings may not directly apply to other regions or countries with different construction practices, regulations, and contextual factors It is crucial to consider the specific characteristics of each construction environment before generalizing the results
The limitations of this study include the following:
1 Deterministic Assumptions: The study assumes that all parameters and inputs have deterministic values, meaning they are fixed and known with certainty However, in real-world construction projects, uncertainties and variations are inevitable Material availability, equipment performance, and labor productivity can fluctuate, leading to deviations from the assumed deterministic values It is crucial to account for such uncertainties when applying the findings in practical scenarios
2 Static Supply Points: The study assumes fixed supply points for construction activities, implying that they remain constant throughout the project duration In dynamic construction sites, however, the locations of supply points may change over time due to evolving project requirements, site conditions, or logistical considerations This dynamic nature of supply points can impact the effectiveness and efficiency of TCLP strategies
3 External Factors: The study does not consider the influence of weather conditions and other external factors that can significantly affect the performance of tower cranes in construction projects Weather elements like wind, rain, or extreme temperatures can impact crane operations, productivity, and safety Other external factors like project dependencies, regulatory changes, or unforeseen events may also affect the overall optimization strategies
Overall, while the thesis provides valuable insights into the optimization problem of tower cranes in high-rise construction projects, it is essential to interpret the findings within the study's limitations Future studies should address these limitations to enhance the comprehensiveness and practical relevance of tower crane optimization strategies.
Outline of the thesis chapters
The thesis outline can be summarized as follows:
Chapter 1 – Introduction: This section provides background information on TCLP for high-rise construction, discusses the purpose and significance of the research topic, outlines the objectives of the thesis, and describes the scope and limitations of the research It also presents an overview of the chapters in the thesis Chapter 2 – Literature Review: This chapter reviews the existing literature on TCLP in high-rise construction It also examines studies on multi-objective optimization techniques and their application in construction Additionally, it explores the suitability of colliding bodies optimization for TCLP and identifies research gaps and issues to be addressed
Chapter 3 – Methodology: This section describes the multi-objective colliding bodies optimization algorithm and the development of a mathematical model for TCLP It explains the decision variables and constraints incorporated into the model and discusses the integration of construction-specific parameters and considerations Chapter 4 – Case study, Results and Discussion: This chapter presents and analyzes the optimized tower crane layouts derived from a case study It begins by explaining the data collection process and sources and provides comprehensive details on the simulation and implementation of the optimization model Furthermore, it discusses the trade-offs and compromises observed in the optimized layouts The chapter goes on to compare the results with alternative multi-objective optimization algorithms, highlighting any significant differences or similarities
Chapter 5 – Conclusion and Recommendation: The conclusion chapter summarizes the research findings, discusses the achievement of research objectives, and explores the implications and contributions of the study It also highlights the limitations of the research and identifies areas for future research.
In the second section of the research paper, a comprehensive literature review is conducted to provide an overview of the current knowledge and research related to TCLP in high-rise construction The literature review focuses on various aspects, including methods, techniques, and optimization approaches used in TCLP This section aims to build a foundation for the subsequent research by summarizing the existing studies, identifying research gaps, and highlighting issues that need to be addressed
2.1 TCLP methods and techniques in high-rise construction
High-rise construction is a complex process that involves a range of challenges, including the efficient planning and positioning of tower cranes The layout and planning of tower cranes play a crucial role in the success of high-rise construction projects, as they are responsible for the safe and efficient movement of materials and equipment throughout the construction site Over the years, researchers and construction management professionals have sought innovative methods and techniques to address the complex challenges associated with TCLP in high-rise construction
Artificial neural networks have become a powerful tool for modeling non-linear operations in various industries, including high-rise public housing construction Tower cranes play a crucial role in ensuring efficient and safe construction projects Researchers have explored innovative approaches such as augmented reality (AR), virtual reality (VR), and optimization models to address challenges in facility layout design and planning.
LITERATURE REVIEW
TCLP methods and techniques in high-rise construction
High-rise construction is a complex process that involves a range of challenges, including the efficient planning and positioning of tower cranes The layout and planning of tower cranes play a crucial role in the success of high-rise construction projects, as they are responsible for the safe and efficient movement of materials and equipment throughout the construction site Over the years, researchers and construction management professionals have sought innovative methods and techniques to address the complex challenges associated with TCLP in high-rise construction
Artificial neural networks have become a powerful tool for modeling non-linear operations in various industries, including high-rise public housing construction Tower cranes play a crucial role in ensuring efficient and safe construction projects Researchers have explored innovative approaches such as augmented reality (AR), virtual reality (VR), and optimization models to address challenges in facility layout design and planning
In a notable study conducted in 2003, artificial neural networks were employed to model the intricate non-linear operations of tower cranes in high-rise public housing construction [5] This research played a significant role in optimizing the performance of tower cranes by gaining a deep understanding of the complex dynamics involved By comprehending these dynamics, construction managers and engineers can enhance the efficiency and effectiveness of tower crane operations Choosing the appropriate position for tower cranes is another crucial factor in construction management In 2014, a highly effective method was proposed that leverages the power of augmented reality to assist in determining the optimal position [6] By superimposing virtual information onto the physical construction site, this technology enhances decision-making processes and improves efficiency in tower crane placement Construction professionals can now make more informed choices based on real-time data and visualizations, resulting in better project outcomes Abdelmegid addressed the tower crane location problem in 2015 by developing an optimization model that takes into account various factors such as site constraints, workflow optimization, and resource allocation [7] The optimization of tower crane placement has a direct impact on productivity and cost-effectiveness in construction projects By strategically positioning tower cranes based on the optimization model, construction teams can maximize productivity while minimizing operational costs
In 2016, a method was presented to optimize the layout of single tower cranes and identify the most suitable locations for supply materials [8] This research contributes to improved project management by identifying the optimal configuration and strategic placement of tower cranes and supply materials With this knowledge, project managers can create well-organized construction sites that promote efficient workflow and minimize disruptions caused by material handling
Nguyen proposed a novel modeling approach in 2019 for evacuation strategies in tall towers, using the Lotus Tower as a case study [9] By leveraging advanced simulation techniques, this research provides valuable insights into the development of effective evacuation strategies, leading to layout changes in the Lotus Tower to enhance safety and efficiency The findings of this study can be applied to other tall
11 towers, contributing to the overall improvement of emergency preparedness in high- rise buildings
In the realm of high-rise modular construction, Zhang focused on developing a user-friendly VR-supported tool in 2019 for effective lift planning [10] By integrating virtual reality technology, this research facilitates accurate and efficient lift planning for constructing high-rise modular buildings This tool enables construction professionals to visualize and simulate the lift process, identifying potential challenges and optimizing the workflow Ultimately, the use of this tool enhances the safety, precision, and speed of modular construction projects
In 2019, Cheung delved into the application of virtual reality to improve construction logistics in high-rise modular integrated construction [11] Their study revolved around the development of a virtual reality model that simulates the construction of a high-rise residential building within a confined site This innovative approach significantly enhances visualization, collaboration, and decision-making throughout the construction process By immersing stakeholders in a virtual environment, they can gain a comprehensive understanding of the project, identify potential challenges, and make informed decisions to optimize logistics and resource allocation
In recent years, the focus on safety and efficiency in high-rise modular construction has grown significantly In 2021, Zhang proposed the TCPL (Tower Crane Placement and Layout) framework, which incorporates multi-criteria decision- making techniques [12] This framework, supported by virtual reality tools, provides valuable assistance to main contractors in selecting the optimal tower crane layout plan, consequently enhancing project outcomes
The utilization of artificial neural networks, augmented reality, virtual reality, and optimization models has played a pivotal role in enhancing multiple facets of high-rise construction By incorporating tower crane modeling and placement optimization, layout design, and evacuation strategies, these cutting-edge methodologies have brought about significant improvements in safety, efficiency, and decision-making within construction projects This research approach delves into the application of optimization models enhanced by metaheuristic techniques, specifically emphasizing the utilization of multi-objective optimization methods By further investigating this area, the study aims to uncover the wide-ranging advantages and notable effectiveness of optimization models within the realm of high-rise construction.
Metaheuristics and multi-objective optimization in TCLP
Optimizing the location of tower cranes involves addressing multiple objectives simultaneously, such as maximizing productivity, minimizing interference, and ensuring safe operations Traditional optimization methods often struggle to handle the complexity of this multi-objective problem However, the integration of metaheuristic techniques with multi-objective optimization provides a powerful approach for tackling TCLP challenges
Metaheuristics are iterative optimization algorithms that search for near-optimal solutions in complex problem spaces Unlike exact methods, which guarantee optimality but struggle with large-scale problems, metaheuristics offer efficient and scalable solutions Several metaheuristic techniques have shown promise in TCLP Genetic Algorithms (GA) are computational models that mimic the process of natural evolution in order to search for optimal or near-optimal solutions By employing operators such as selection, crossover, and mutation, GA iteratively generates new solutions In the domain of TCLP, numerous studies have successfully utilized GA for optimization purposes
For instance, Tam et al conducted a study that explored and analyzed the correlation between key supply areas and tower cranes They developed a genetic algorithm model that aimed to optimize these facilities, taking into account the optimal layout of construction materials at different floor levels [13] Their research demonstrated the effectiveness of GAs in optimizing TCLP
In a similar vein, another study employed the GA optimization technique to determine the optimal layout of tower cranes and the type of crane base, whether fixed or mobile [14] By leveraging the capabilities of GA, this study achieved improved efficiency and performance in tower crane placement
Particle Swarm Optimization (PSO) is another optimization technique inspired by the collective behavior of bird flocks PSO simulates the movement and interaction of particles within a search space to optimize solutions Researchers have applied PSO in the context of tower crane placement to achieve desirable outcomes
Lien and Cheng utilized the particle swarm optimization algorithm to determine the optimal location for a tower crane while considering the optimization of material supply and demand [15] By integrating the principles of PSO, this approach effectively addressed the complex interplay between material logistics and tower crane placement
In another study, researchers employed the particle bee algorithm (PBA), which combines the PSO and artificial bee colony (ABC) algorithms, to optimize the number, type, and locations of tower cranes [16] This hybrid swarm algorithm demonstrated promising results in achieving efficient and effective TCLP
Multi-objective optimization (MOO) aims to find a set of solutions that represents a trade-off between conflicting objectives In TCLP, MOO is crucial due to the existence of multiple objectives that need to be simultaneously satisfied Traditionally, construction projects have emphasized single-objective optimization, such as minimizing costs or maximizing time efficiency
Several research papers have addressed the development of such models for TCLP For example, a study proposed a mathematical model that considers the hire cost as the objective function to optimize the layout of a single tower crane and supply material locations [17] The model aims to determine the optimal placement of the tower crane and material supply points to minimize the overall cost
Another research paper formulated the tower crane layout design problem as a mixed-integer linear program to minimize the total operating cost [18] The model integrates the placement of a single tower crane and associated material supply points into the optimization process
Construction site layout and TCLP are both complex tasks that involve multiple objectives and factors Previous research has mainly focused on treating these problems as single objective optimization problems, which do not fully capture the reality of the situation
In reality, construction site layout and TCLP are MOO problems, as they require balancing various objectives and considerations Factors such as project size, site constraints, and construction schedule influence the optimal layout of tower cranes
To address the multi-objective nature of these problems, researchers have employed MOO techniques like Pareto-based genetic algorithms (e.g., NSGA-II, SPEA2) and particle swarm optimization (e.g., MOPSO) to solve TCLP problems These algorithms aim to find a set of solutions that represent the trade-offs between different objectives, allowing decision-makers to choose the most suitable layout based on their preferences and priorities.
Research gaps and issues
Recent studies in the field of construction have highlighted a growing focus on optimizing tower crane locations for modular integration construction (MiC), with limited attention given to traditional high-rise buildings The existing research on traditional high-rise buildings has primarily concentrated on utilizing multi-objective optimization techniques for optimizing tower crane scheduling rather than location selection It is important to note that previous studies on optimizing tower crane location have exclusively relied on single objective optimization approaches Therefore, this research seeks to address these gaps by investigating and developing
15 methodologies for multi-objective optimization in tower crane locations specifically in traditional high-rise buildings
The significance of this research lies in the increasing popularity of MiC in the construction industry MiC involves the use of prefabricated modules that are manufactured off-site and then assembled on-site, offering advantages such as reduced construction time, improved quality control, and minimized labor requirements As a result, there has been a surge of interest in optimizing tower crane locations for efficient MiC implementation However, the majority of existing studies have predominantly focused on MiC, neglecting the unique challenges posed by traditional high-rise buildings
Traditional high-rise buildings present distinct characteristics and complexities that necessitate tailored optimization approaches for tower crane locations Factors such as the building's height, structural considerations, and constraints imposed by the surrounding environment need to be carefully evaluated By developing methodologies for multi-objective optimization in tower crane locations specifically for traditional high-rise buildings, this research aims to provide valuable insights and practical guidance for construction professionals involved in such projects
Furthermore, the research community has predominantly approached the optimization of tower crane locations using single objective optimization techniques While single objective optimization can be effective in achieving a specific goal, it often fails to consider multiple conflicting objectives simultaneously This limitation can lead to suboptimal solutions and overlook crucial trade-offs In contrast, multi- objective optimization techniques enable decision-makers to consider various objectives simultaneously and identify trade-offs between them, resulting in more comprehensive and well-balanced solutions
The proposed research will delve into the development of methodologies for multi-objective optimization in tower crane locations, taking into account various factors that impact the efficiency and effectiveness of traditional high-rise building construction These factors include crane utilization, construction schedule, material handling, and site constraints By considering these multiple objectives and evaluating trade-offs, the research aims to enhance the decision-making process for tower crane location selection, ultimately leading to improved project outcomes
In conclusion, the current research landscape in construction has witnessed a significant focus on optimizing tower crane locations for MiC, neglecting the specific challenges associated with traditional high-rise buildings Furthermore, previous studies have predominantly utilized single objective optimization approaches, overlooking the benefits of considering multiple conflicting objectives simultaneously To address these gaps, this research aims to develop methodologies for multi-objective optimization in tower crane locations specifically for traditional high-rise buildings By doing so, it strives to provide valuable insights and practical guidance for construction professionals, leading to more efficient and effective construction processes.
Colliding bodies optimization
Colliding Bodies Optimization (CBO) is a highly promising metaheuristic optimization algorithm that draws inspiration from the fundamental principles of momentum and energy involved in collisions between solid bodies [19] With its ability to efficiently solve optimization problems, CBO has gained recognition as a valuable tool in various domains
CBO has demonstrated remarkable success in tackling a wide range of optimization problems, encompassing both mathematical and engineering realms For instance, it has been effectively employed in the design optimization of diverse structures, including welded beams and cylindrical pressure vessels (see TABLE 2.1) Additionally, CBO has proven instrumental in minimizing the weight of tension and compression springs, highlighting its versatility in addressing different engineering challenges [20]
Despite its successes, the utilization of CBO in the context of TCLP remains relatively unexplored While numerous studies have applied other metaheuristic
17 algorithms such as PSO to tackle TCLP problems, there have been only a few investigations into the potential of CBO in this specific domain One notable study conducted by Kaveh and Vazirinia in 2018 compared the performance of PSO with four newly developed metaheuristic algorithms, namely CBO, Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS) The researchers sought to evaluate the effectiveness of these algorithms in addressing a practical TCLP problem [21] It is important to note that these studies primarily focused on single objective optimization
TABLE 2.1 Applications and extensions of the colliding bodies optimization algorithm
To further expand on the advancements of CBO and its applicability to TCLP, this thesis proposes the extension of CBO into a multi-objective optimization framework By leveraging the inherent strengths of CBO and introducing a multi-
2014 Design of trusses Colliding bodies optimization (CBO) is a population-based evolutionary algorithm which uses an analogy of the laws of collision between objects Enhanced colliding bodies optimization (ECBO) based on CBO was proposed in 2014
2015 Design of steel frames with semi-rigid connections
Chaotic enhanced colliding bodies optimization (CECBO) algorithm was proposed for the design of steel frames with semi-rigid connections
Mahdavi [23] 2015 Size and topology optimization of truss structures
Application of Colliding Bodies Optimization (CBO) for size and topology optimization of truss structures
[24] 2019 Design of trusses Multi-objective colliding bodies optimization (MOCBO) algorithm was proposed for the multi-objective optimization problems of truss structures objective perspective, the proposed approach aims to enhance the effectiveness and versatility of the algorithm in resolving TCLP challenges The introduction of multi- objective optimization enables the consideration of multiple conflicting objectives simultaneously, facilitating a more comprehensive evaluation of crane layout plans.
This chapter describes the approach used for TCLP, which includes creating a mathematical model and employing a multi-objective colliding bodies optimization algorithm
Site boundary, limit zones Site information
Sit e l ayout & c rane m ode lling
Supply, demand points Total quantity of works Specific number of cranes
Define decision variables and objective function MOCBO
FIGURE 3.1 Process of construction TCLP model
METHODOLOGY
Data collection
According to TCXDVN 323: 2004 "High-rise Residential Buildings - Design Standards", a high-rise structure is classified as a building with an architectural height ranging from 9 to 40 floors (buildings with more than 40 floors are commonly referred to as super high-rise buildings) By utilizing this widely accepted definition, the selection process for high-rise buildings remains consistent and aligns with industry standards
To gather the necessary information, the thesis collects the following data from the layout of construction plan:
1 Temporary and permanent structure layout plan: This information helps identify the spatial arrangement and distribution of structures, aiding in the overall analysis and optimization process
2 Site layout drawings: Detailed drawings of the site layout provide a visual representation of the construction area, enabling accurate modeling and simulation
3 The tower crane's current position and related specifications: The position of the tower crane and associated details, such as length jib, load capacity, speed of hoisting, slewing, and trolleying, are essential for understanding the capabilities and limitations of the construction equipment involved
4 Construction schedules and volumes of work: By obtaining construction schedules and workload volumes, the optimization model can factor in time constraints and resource requirements, leading to improved project planning and resource allocation
The TABLE 3.1 presents essential input parameters, including building and site information, crane specifications, supply and demand point locations, as well as corresponding output metrics such as optimal crane selection, placement, total cost, and operating time
The first crucial stage in the process of modeling construction sites involves the creation of a comprehensive computer model within Cartesian coordinate space This model serves as a representation of the objects found within the construction site However, when dealing with objects that possess non-regular shapes, an additional
Site Layout (facilities, storage, access road, entrances, etc.), dimensions
Crane Number of cranes, min distance b/w cranes max, hook height, movement velocities, max radius, rental cost, fixed cost
Supply point Set location of supply points
Demand point Set location of demand points Output Crane Optimum number of cranes, optimum location, optimum selection, total cost, operating time step becomes necessary to ensure accurate modeling: the simplification of these objects into convex polygon counterparts, each possessing equivalent areas
Convex polygons are geometric shapes characterized by their properties and structural integrity, making them an ideal choice for representing objects in construction site models By definition, a convex polygon is a closed shape where all interior angles are less than 180 degrees, and any line segment connecting any two points within the polygon lies completely inside the shape This key characteristic ensures that the resulting model maintains a high level of accuracy and facilitates efficient analysis
One of the primary reasons for utilizing convex polygons is their simplicity and ease of calculation The straightforward nature of convex polygons allows for efficient algorithms and computational techniques to be employed during the modeling process These algorithms can accurately calculate essential parameters such as area, perimeter, centroid, and moment of inertia, which are crucial for conducting various analyses and simulations
Additionally, the representation of objects as convex polygons enhances the overall visual aesthetics of the construction site model Convex polygons exhibit a more regular and uniform appearance, aligning with the expectations of visual clarity and simplicity This characteristic facilitates the interpretation of the model by stakeholders, enabling effective communication and decision-making processes during the construction project
In order to efficiently service each floor using a crane, it is essential to divide the floor into specific work zones and allocate a proportionate workload to each zone This crucial step in the modeling process accurately represents the practical functioning of cranes in real-life scenarios, where materials required for a task are not transported all at once but rather in a staged manner
While the process of creating the crane model remains largely consistent among various researchers, one notable distinction lies in how objects within the model are referenced Alkriz and Mangin (2005) as well as Abdelmegid et al (2015) utilize the centroid coordinates to refer to each object [7], [25] Although there is a discrepancy
23 in the referencing method, it bears minimal significance as it does not affect the accuracy of the obtained results
By dividing the floor into work zones, the crane model accounts for the practical constraints of material transportation This approach acknowledges that materials needed for a task are typically moved in multiple stages rather than all at once This ensures a more realistic representation of crane operations and enhances the model's effectiveness in simulating real-life scenarios
The division of the floor into work zones allows for a systematic allocation of the workload, ensuring that each zone receives an appropriate share of the total work
By distributing the workload proportionally, the model reflects the practical considerations of crane operations, optimizing efficiency and resource utilization While researchers may differ in their approaches to referencing objects within the model, such as using centroid coordinates or top-left coordinates, this discrepancy does not impact the accuracy of the results Regardless of the referencing method employed, the fundamental principles of the crane model remain intact, and its ability to simulate real-life scenarios is preserved
The third crucial area is ensuring that the positions of objects on the site are carefully examined to prevent conflicts and overlaps By doing so, potential disruptions and safety hazards can be minimized To achieve this, a widely employed strategy involves modeling the base of the structure and the base of the tower crane as convex polygons when assessing proposed locations for the tower crane
The utilization of convex polygons offers several advantages in collision detection and collision response algorithms within the construction site model The primary benefit lies in the simplification of these algorithms, enabling faster and more reliable results Specifically, the use of convex polygons allows collision detection algorithms to employ efficient methods like the separating axis theorem
Development of the mathematical model for TCLP
This section presents the development of a mathematical model for TCLP The model is designed to optimize the arrangement of tower cranes on construction sites, considering factors such as site geometry, crane capacities, and operational constraints By formulating the problem mathematically, this model provides a systematic approach to determine the most efficient and effective layout configuration, thereby minimizing cost and improving construction productivity
3.2.1 Several factors can influence crane travel time
There are some factors that exert influence on the travel time of tower cranes involved in the construction of high-rise buildings One of the primary variables impacting hoisting times is the length of the object being lifted It is evident that larger objects pose greater challenges in terms of maneuverability and susceptibility to wind effects
In addressing this challenge, the paper refers to the groundbreaking work of Zhang et al (1999) and their travel time equations [28] These equations build upon the research conducted by Rodriguez-Ramos and Francis (1983) in their paper
"Single Crane Location Optimization" [29] The researchers meticulously explored
22 variables they deemed instrumental in explaining hoisting times
A pivotal finding from their regression analysis is the prominent role played by the hoisting height variable, which emerges as the most influential factor in both the supply and return journeys The height at which materials are hoisted significantly affects the overall travel time Furthermore, the researchers identify another noteworthy variable termed "simultaneous movement." This variable encapsulates the capacity of an individual to engage in both lifting and moving tasks with a crane, considering factors such as experience, on-site environment, and climate conditions Notably, Zhang et al (1995) effectively combine both of these essential elements into their formulas, thereby effectively modeling the duration of travel
To summarize, this section underscores the utmost significance of considering a multitude of factors that impact crane travel time The research highlights the necessity for accurate equations to model and optimize travel time for cranes involved in construction projects By incorporating variables such as object dimensions, hoisting height, and simultaneous movement, researchers can enhance the precision of their models, ultimately contributing to improved efficiency in construction processes
3.2.2 Calculation of crane travel time
The travel time equations utilized in this paper build upon the research conducted by Leung & Tam (1999) and Zhang et al (1999) These equations consider a multitude of variables that influence the duration it takes for a load to traverse from the origin to the destination By comprehensively accounting for these factors, the equations provide a more accurate estimation of travel time
To determine the time required for load transportation, the travel time equations consider three distinct movements executed by a crane during the process These movements encompass the entire travel duration, and their cumulative sum yields the total travel time
Travel distance between the supply, demand points and tower cranes can be calculated by the Eqs (3.1)-(3.3) are as referring to FIGURE 3.5:
𝐿𝐿 = ��𝑆𝑆 𝑖𝑖 𝑥𝑥 − 𝐷𝐷 𝑗𝑗 𝑥𝑥 � 2 + �𝑆𝑆 𝑖𝑖 𝑦𝑦 − 𝐷𝐷 𝑗𝑗 𝑦𝑦 � 2 (3.3) Hook movement time is a crucial metric for assessing the overall efficiency of material transportation utilizing a tower crane To comprehensively evaluate this time, it is divided into horizontal and vertical paths, providing a more accurate reflection of the associated operating costs By incorporating an appropriate cost-time factor, the split paths enable a more nuanced analysis FIGURE 3.5 present visual representations of the distinct movement paths observed in various directions This comprehensive breakdown of hook movement time enhances our understanding of
29 the crane's performance and facilitates informed decision-making in construction projects
FIGURE 3.5 Radial, tangent movements (left) and vertical movement (right) of the hook [21]
The accuracy of the equations used lies in their effectiveness in capturing real- life conditions and the variables that have been identified as the most influential on the speed of operations [30] A significant factor that has been found to impact hoisting times is the occurrence of simultaneous movements [28] To account for this effect, Zhang et al (1999) introduced two parameters, namely α and β, which are determined based on the researchers' discretion and factors such as site conditions and the experience of the operator The parameter α specifically represents the degree of coordination between the movement of the hook in both radial and tangential directions within the horizontal The times for horizontal and vertical hook movements can be calculated from Eqs (3.6), (3.7), respectively
According to Zhang et al [24], the parameter β serves as a measure of coordination in both the X and Y axis It signifies the degree to which these movements are synchronized within a building structure As highlighted by Tam et al [5], the value of β is closely linked to the building's height Specifically, as the working height increases, so does the value of β Consequently, in such cases, β should be set to a fixed value of 1, implying a lack of coordination between horizontal and vertical motions
This reasoning is based on the premise that the physical limitations of high-rise construction prevent simultaneous coordination of horizontal and vertical movements
By setting β to 1, it is acknowledged that these movements occur independently rather than in unison, reinforcing the understanding of the challenges posed by the vertical clearance requirement in high-rise projects
The total travel time of tower crane at location k between supply point i and demand point j, 𝑇𝑇 𝑖𝑖,𝑗𝑗 𝑘𝑘 , can be calculated using Eq (3.8) by specifying the continuous type of parameter β
Specific parameters are subject to researcher discretion in crane operations In contrast, others are determined by the crane itself and its capacity A key parameter affected by the load is the vertical lift speed (𝑉𝑉 ℎ ) When the load reaches the maximum capacity of the crane, the speed of lifting can be gradually decreased This relationship is illustrated by the hoisting speed data of a specific crane model, the SCM-C6018 (FIGURE 3.6)
The data reveals that when increasing the weight of the item being carried, the maximum hoisting velocity reduces The crane under examination has a full hoisting speed of 100 m/min However, this speed can only be achieved when lifting loads below 2.5 tons
FIGURE 3.6 Specification of tower crane SCM-C6018 [31]
One crucial factor that must be considered is the influence of the object's position along the crane's saddle jib on its overall effect The laws of mechanics come into play as an object travels farther away from the building, causing a reduction in the crane's maximum lift capacity This limitation is determined by the load-radius data depicted in FIGURE 3.7 While the crane may have the capability to carry the item from the supply location, surpassing the crane's lift capacity limit becomes a possibility as the item is carried farther from the building [4]
For instance, when an object is lifted at a radius of 13.9m, its maximum permissible weight is 10 tons However, given that the distance from the tower crane to the desired point of delivery is 60m, the highest achievable lift capacity is 1.8 tons Consequently, even if the object can be safely lifted from the supply point, it cannot be delivered to the demand point Therefore, when delivering a load at a distance of
30m, the maximum weight that can be accommodated is limited to 4.38 tons, regardless of the crane's maximum load capacity at the supply location [15]
FIGURE 3.7 Load-Radius diagram of the SCM-C6018 [31]
Multi-objective Colliding Bodies Optimization
The Colliding Bodies Optimization (CBO) algorithm is a powerful metaheuristic optimization technique inspired by the principles of momentum and energy conservation in one-dimensional collisions [19] Building upon this foundation, this section aims to present the multi-objective variant of the CBO algorithm, extending its applicability to tackle complex optimization problems with multiple objectives simultaneously By introducing the multi-objective aspect, this enhanced version of CBO provides a robust and efficient solution for real-world optimization scenarios, enabling decision-makers to optimize diverse objectives concurrently
The MOCBO algorithm had been proposed also by A Kaveh and V R Mahdavi [24] However, there is no indication of whether the algorithm was available online or developed solely by the author To address this gap, the thesis’ author has implemented a Python code of the MOCBO algorithm based on the provided concept and theory The implementation seeks to bridge the information gap and make the algorithm more accessible to the community, fostering further research and development in this area
3.3.1 Basic definitions for multi-objective optimization
In the realm of optimization, a challenging task arises when multiple conflicting objectives need to be addressed simultaneously, known as a Multi-Objective Optimization (MOO) problem [32] To grasp the fundamentals of these formulations, let's delve into the following definition
Definition 1 Multi-objective optimization: A General Multi-Objective
Optimization Problem involves M objectives, where a set of tradeoff solutions can be formally expressed as follows:
X represents a vector of variables with n unknowns, 𝑙𝑙 𝑗𝑗 denotes the j th constraint from m inequality constraints, and 𝐹𝐹 𝑖𝑖 (𝑋𝑋) signifies the ith objective function Additionally, 𝑋𝑋 𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛 and 𝑋𝑋 𝑖𝑖𝑖𝑖𝑎𝑎𝑥𝑥 correspond to the lower and upper bounds of the design variable vector, respectively
MOO problems inherently involve optimizing objectives where improving one objective may lead to a deterioration in another Consequently, the primary objective of multi-objective optimization algorithms is to discover vectors that offer acceptable solutions, minimizing all objective values as much as possible
Definition 2 Pareto dominance [33]: Let two vectors 𝑙𝑙�⃗ = (𝑙𝑙1, 𝑙𝑙2, … , 𝑙𝑙𝑖𝑖) 𝑇𝑇 , 𝑙𝑙�⃗ = (𝑙𝑙 1 , 𝑙𝑙 2 , … , 𝑙𝑙 𝑖𝑖 ) 𝑇𝑇 and 𝑙𝑙�⃗, 𝑔𝑔⃗ ∈ Ω The 𝑙𝑙�⃗ is said to dominate another 𝑔𝑔⃗, noted by 𝑙𝑙�⃗ ≺ 𝑔𝑔⃗ if: ∀𝑖𝑖 ∈ (1,2, … , 𝑚𝑚), 𝑙𝑙 𝑖𝑖 ≤ 𝑔𝑔 𝑖𝑖 𝑉𝑉𝐷𝐷𝑑𝑑 𝑙𝑙�⃗ ≠ 𝑙𝑙�⃗
Pareto dominance is a concept used in multi-objective optimization to compare and rank different solutions or vectors In simpler terms, 𝑙𝑙�⃗ dominates 𝑔𝑔⃗ if it is better than or equal to 𝑔𝑔⃗ in all objectives and is strictly better in at least one objective The concept of Pareto dominance allows us to identify solutions that are not dominated by any other solutions, known as Pareto optimal or non-dominated solutions
Definition 3 Pareto optimal [33]: Solution 𝑒𝑒 ∈ Ω is Pareto optimal with respect to Ω if there is no 𝑒𝑒 ′ ∈ Ω for which 𝑔𝑔⃗ = {𝑙𝑙 1 (𝑒𝑒 ′ ), 𝑙𝑙2(𝑒𝑒 ′ ), … , 𝑙𝑙𝑖𝑖(𝑒𝑒 ′ ) dominate 𝑙𝑙�⃗ {𝑙𝑙1(𝑒𝑒), 𝑙𝑙2(𝑒𝑒), … , 𝑙𝑙𝑖𝑖(𝑒𝑒)
Pareto optimality is a fundamental concept in multi-objective optimization, where the goal is to find solutions that achieve a good trade-off among multiple conflicting objectives The Pareto optimal solutions represent the best possible compromises among these objectives, as no other solution can simultaneously improve all objectives without sacrificing the performance in at least one objective
3.3.2 Concept of Colliding Bodies Optimization
The concept behind the development of the MOCBO algorithm draws inspiration from the fundamental natural occurrence of collisions between two bodies In the context of an isolated environment, the collision dynamics can be visualized as depicted in FIGURE 3.8
During such a collision, an important principle comes into play: the conservation of total momentum This principle states that the combined momentum of the bodies involved remains constant throughout the collision process In other words, the net momentum of the bodies before the collision is equal to the momentum of all bodies after the collision
FIGURE 3.8 Principle of collision between two bodies [34]
By harnessing this principle of momentum conservation, the CBO algorithm aims to optimize multiple objectives simultaneously It achieves this by modeling the
39 interactions between the bodies, mimicking the way in which physical bodies interact during collisions Through iterative processes, the algorithm strives to find the most favorable solution that fulfills various objectives efficiently and effectively
Consider two bodies of mass 𝑚𝑚 1 and 𝑚𝑚 2 moving with velocities 𝑔𝑔 1 and 𝑔𝑔 2 , respectively When these bodies collide, the law of conservation of momentum states that the total momentum before the collision is equal to the total momentum after the collision Mathematically, this conservation of momentum can be expressed as:
𝑚𝑚 1 𝑔𝑔 1 + 𝑚𝑚 2 𝑔𝑔 2 = 𝑚𝑚 1 𝑔𝑔 1 ′ + 𝑚𝑚 2 𝑔𝑔 2 ′ (3.22) After the collision, the bodies achieve velocities 𝑔𝑔 1 ′ and 𝑔𝑔 2 ′ , respectively These velocities can be determined using the following equations:
The 𝜖𝜖 given by Eq (3.25) represents the coefficient of restitution (CR) This coefficient indicates the ratio of the relative velocity of separation to the relative velocity of approach:
Based on the values of the coefficient of restitution, two cases of collision can be identified:
1 Perfectly Elastic Collision (∈= 1): In this type of collision, there is no loss of energy After the collision, both bodies experience a high velocity of separation
2 Inelastic Collision (∈< 1): In this scenario, there is a loss of energy, as some of the energy gets converted into other forms After the collision, the velocities of separation for both bodies are low
3.3.3 Details of the proposed algorithm
The CBO algorithm, initially designed as a single-objective method, faces limitations when applied to problems with multiple objective functions Nevertheless, by incorporating certain modifications, it can be adapted into a versatile multi-objective algorithm The key transformation lies in replacing conventional sorting based on function values with the utilization of a non-dominated sorting approach This approach distinguishes and ranks solutions based on their superiority over others, ensuring a more comprehensive evaluation of trade-offs between conflicting objectives MOCBO algorithm provides an effective framework for finding optimal solutions in scenarios where multiple conflicting objectives need to be considered simultaneously
Step 1 Initialization: Let's consider Z as the number of colliding bodies (CB), each possessing a dimension D, representing the number of variables in the function
To establish their initial positions, a 𝑍𝑍 × 𝐷𝐷 matrix is created These positions are randomized by assigning random numbers within the range [𝑒𝑒 𝑖𝑖𝑖𝑖𝑛𝑛 , 𝑒𝑒 𝑖𝑖𝑎𝑎𝑥𝑥 ] are given by:
𝑒𝑒 𝑖𝑖 0 = 𝑒𝑒 𝑖𝑖𝑖𝑖𝑛𝑛 + 𝐶𝐶𝑉𝑉𝐷𝐷𝑑𝑑 × (𝑒𝑒 𝑖𝑖𝑎𝑎𝑥𝑥 − 𝑒𝑒 𝑖𝑖𝑖𝑖𝑛𝑛 ), 𝑖𝑖 = 1, 2, 3, … , 𝑍𝑍 (3.26) where 𝑒𝑒𝑖𝑖𝑖𝑖𝑛𝑛 and 𝑒𝑒𝑖𝑖𝑎𝑎𝑥𝑥 denote the minimum and maximum values of the variables in the search space, respectively
The MOCBO algorithm initiates by creating an initial population of colliding bodies These bodies represent potential solutions to the optimization problem at hand The population size is determined in advance and can be adjusted to suit the complexity of the problem A larger population allows for a more diverse exploration of the solution space
Step 2 Fitness evaluation: In order to assess the quality of each colliding body, a fitness evaluation process is employed This evaluation is based on objective functions that capture the desired outcomes of the problem The specific objective functions are defined based on the nature of the optimization problem being tackled
Technique for Order of Preference by Similarity to Ideal Solution
TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a decision-making method commonly used to select a solution from a Pareto front It was originally developed by Ching-Lai Hwang and Yoon in 1981 and further improved by Yoon in 1987, as well as by Hwang, Lai, and Liu in 1993 [37], [38] This method enables the effective evaluation of alternatives by measuring their similarity to an ideal solution TOPSIS is a straightforward and intuitive method for selecting a solution from a Pareto front The Pareto front represents a set of non- dominated solutions, where improving one objective comes at the cost of degrading another Here's why TOPSIS can be a good choice for selecting the best solution:
Handling Multiple Objectives: Multi-objective optimization problems involve optimizing several conflicting objectives simultaneously TOPSIS is designed to handle multiple criteria, making it suitable for scenarios where there are multiple objectives to consider in tower crane layout planning, such as minimizing construction costs, maximizing worker safety, and minimizing project duration
Aggregating Objectives: TOPSIS calculates a score for each solution based on its distance to both the ideal (best) and anti-ideal (worst) solutions By aggregating the multiple objectives into a single score, TOPSIS allows to rank and select solutions efficiently This helps find the solution that is closest to the ideal solution and furthest from the anti-ideal solution simultaneously
Intuitive and Transparent: TOPSIS is relatively easy to understand and interpret compared to other methods It provides a clear rank order of solutions, making it straightforward for decision-makers to comprehend and make informed choices
Balancing Trade-offs: In a Pareto front, no solution dominates all others, and trade-offs between objectives are inevitable TOPSIS accounts for these trade-offs, making it well-suited for situations where a balance between different objectives is required
No Prior Assumptions on Objectives: TOPSIS does not impose any particular mathematical relationships between objectives It can handle both linear and non- linear relationships between objectives, providing flexibility in modeling complex problems
Sensitivity Analysis: TOPSIS allows to perform sensitivity analysis easily By adjusting the weights of different objectives, you can explore how the final solution selection changes and gain insights into the impact of each objective on the overall decision-making process
To sum up, TOPSIS is a method for selecting a solution from a Pareto front in multi-objective optimization problems due to its ability to handle multiple criteria, aggregate objectives into a single score, intuitive nature, ability to balance trade-offs, flexibility in modeling, and ease of sensitivity analysis When applied to tower crane layout planning, TOPSIS can help to make well-informed decisions that strike the best balance between various conflicting objectives
Here's an overview of how TOPSIS works:
Step 1: Create a matrix consisting of M alternatives and N criteria This matrix is usually called an “evaluation matrix”
As an example: M will be the number of solutions retrieved from Pareto front, while N, the number of objective metrics (time, cost)
Each metric j for each solution i is normalized to be in between 0 and 1 The higher its value the better the metric
Step 3: Calculate the weighted normalized decision matrix It is important to note that each criterion should have its own weight so that all of them will sum up to
1 The weights can be derived randomly (not recommended) or based on expert knowledge (industry standard)
Step 4: Determine the best and the worst alternative for each criterion:
Step 5: Calculate the Euclidean distance between the target alternative and the best/worst alternative:
Step 6: For each alternative calculate the similarity to the worst alternative The results are the TOPSIS scores
Step 7: Rank alternatives according to the TOPSIS score by descending order
The solution with metrics closest to the best will obtain the highest score and therefore will be at the top of the ranking
In this chapter, the thesis will thoroughly examine the evaluation of the proposed MOCBO algorithm, specifically focusing on its effectiveness in addressing specific problems To illustrate its practical application, the attention will be directed towards
"The Opera Residence" project situated in District 2, HCMC, a construction site that utilizes two tower cranes To facilitate the implementation of the algorithm, the author will develop it in Python, and the computational process will be carried out using Google Colab
This initial case study has two objectives Firstly, it is used to address research questions Secondly, it is utilized to test and refine the algorithms employed in this thesis Furthermore, the MOCBO algorithm must be calibrated to ensure its efficiency and convergence This chapter’s second goal is to complete the testing, development, and comparison with other well-known algorithms before applying it to other chosen projects
The Opera Residence is the third masterpiece of The Metropole Thu Thiem project located in Division 1, Thu Thiem New Urban Area, District 2, Ho Chi Minh City It is a luxury residential-commercial complex located at the Central Core of Thu Thiem New Urban, including luxury apartments, dining area, high-class commercial and international standard office The project is developed by SonKim Land and designed by DP Architects The construction started in 2020 and is expected to be handed over in 20232.
CASE STUDY 1 - TWO TOWER CRANES MODEL
Project information
The Opera Residence is the third masterpiece of The Metropole Thu Thiem project located in Division 1, Thu Thiem New Urban Area, District 2, Ho Chi Minh City It is a luxury residential-commercial complex located at the Central Core of Thu Thiem New Urban, including luxury apartments, dining area, high-class commercial and international standard office The project is developed by SonKim Land and designed by DP Architects The construction started in 2020 and is expected to be handed over in 20232
CASE STUDY 1 - TWO TOWER CRANES MODEL
Site layout information and modelling
The Opera Residence project spans an area of approximately 11,400 m2, enclosed by Street D5, N12, D6, and Bridge Road Thu Thiem 2 The absence of nearby structures creates an environment that permits unrestricted crane operation and offers flexibility in positioning entry and exit points Given that the site is completely bordered by roads, a single gate has been constructed on Street D6 to facilitate both entry and exit access
FIGURE 4.1 The site layout of "The Opera Residence" project in District 2, HCMC
Based on FIGURE 4.1, the gate depicted serves multiple functions within the construction site Firstly, it acts as the designated point for concrete delivery, enabling efficient transportation of concrete to the site Additionally, it serves as the main entrance for staff and visitors, providing convenient access to the site Moreover, the gate facilitates the delivery of reinforcements, ensuring a streamlined process for transporting them to their respective usage areas
The layout of the site is designed with efficiency in mind The reinforcement working area is conveniently located adjacent to the reinforcement gate, allowing for quick and easy access to reinforcements when needed Similarly, the concrete delivery point is situated near the main tower's pump, optimizing the flow of concrete during construction activities
Furthermore, the construction site features various facilities that serve as shared resources for all three buildings The main offices are situated in the northeast, providing a central hub for administrative and project management functions On the northwest side, there is a residential block, accommodating the housing needs of personnel working on the project Additionally, an eastern-side reinforcement workshop is available to support reinforcement-related tasks
To better visualize the site layout, FIGURE 4.1 illustrates the placement of both temporary and permanent structures It also indicates the positions of the two tower cranes, which play a crucial role in lifting and transporting heavy materials Furthermore, the figure highlights the various supply points, denoted by letters E and
F, further enhancing the understanding of the site's logistics and material flow
Having a construction site occupying a sizable plot of land offers the advantage of flexible supply point relocation This flexibility proves particularly advantageous when it comes to reusing construction materials, such as plywood and aluminum formwork Unlike reinforcing steel, which is typically transported from predetermined locations and does not return, plywood and aluminum formwork can be reused multiple times These materials are commonly employed to create temporary molds for shaping concrete structures Given the availability of ample space at the construction site, it becomes feasible to transport the formwork to a
51 designated area for cleaning This facilitates proper maintenance and inspection of the materials, ensuring they are in optimal condition for future reuse This efficient reuse of formwork not only contributes to cost savings but also enhances sustainability in construction practices
The most significant aspect of the project is the superstructure, which consists of a podium comprising five floors and two towers, each with 20 floors To facilitate efficient workflow, according to the recommendations of the contractor and the consultant, each of the five podium floors is divided into ten work zones Similarly, each floor of the towers is divided into two zones The transportation times are calculated based on the central location of each work zone
FIGURE 4.2 Ten work zones of the 5 podium floors
The construction process has been significantly influenced by two distinct types of formworks: plywood formwork for beams and slabs and aluminum formwork for vertical components like shear walls and columns These formwork systems have been crucial in ensuring efficient and effective project completion
The utilization of aluminum formwork for vertical members brings forth numerous advantages This innovative system offers enhanced strength and durability, enabling the construction of tall structures with improved stability and structural integrity Furthermore, the lightweight nature of aluminum formwork facilitates easy handling and quick assembly, thereby expediting the construction progress Its reusable properties also contribute to sustainability by reducing material waste and minimizing environmental impact
While both aluminum and plywood formwork possess their own merits, it is worth noting that, during the tower construction phases, the exclusive use of aluminum formwork has been adopted (see FIGURE 4.3) This strategic decision ensures consistency and uniformity in the construction process, optimizing efficiency and minimizing the potential for errors By implementing a standardized formwork system, construction teams can streamline their workflow, enhance productivity, and ultimately achieve higher quality outcomes
The use of aluminum formworks in construction projects involves a systematic process to ensure efficient operation Prior to placement, these formworks undergo meticulous washing and lubrication while still on the ground This preparatory step not only ensures cleanliness but also optimizes functionality during the construction process In contrast, steel reinforcement are typically transported directly to the desired location without the need for pre-use treatment or subsequent return to the ground
Once the aluminum formworks have served their purpose, they are carefully brought back to the ground, emphasizing their sustainable and reusable nature This distinctive practice sets them apart from other construction materials, enabling repeated utilization and minimizing waste generation
FIGURE 4.3 Formwork system application plan
It is important to note that the total travel time for the round trip of the aluminum formworks remains unaffected by the direction of movement Therefore, to calculate the overall travel time, it suffices to double the duration required to lift the formwork to the desired height.
Crane data and modelling
The original site plan is currently serviced by two SCM-C6018 cranes, each equipped with a 60m jib length and a maximum capacity of 10 tons The tip of the crane has a maximum capacity of 1.8 tons This thesis aims to optimize not only the location but also the specifications of the tower crane Therefore, three additional types of tower cranes are considered in the data collection: SCM-C6018 (55m), SCM-C5015 (50m, 45m) The recommended base dimensions for the crane are 3m x 3m Moreover, the crane has a maximum service height of 240m, which exceeds the height of the building
FIGURE 4.4 Load-Radius diagram of SCM-C6018 tower crane with jib length 60m, 55m
FIGURE 4.5 Load-Radius diagram of SCM-C5015 tower crane with jib length 50m, 45m
In order to determine the crane's maximum capacity at different points along the jib, a process will be used as mentioned called interpolation This process is explained in detail in Section 0 of the thesis By referring to FIGURE 4.4 and FIGURE 4.5, which display the Load-Radius table extracted from the owner's manual, the closest data points to the desired point can be found along the jib
The manufacturer's manual specifies that the crane has a maximum hoisting speed of 100 m/min when carrying a load of 2.5 tons However, as the load is raised to 5 tons, the maximum speed decreases to 50 m/min Furthermore, when the load is increased to 10 tons, the maximum speed drops to 25 m/min We employ the same interpolation approach to represent this information to model the crane's capacity accurately
The trolleying speed of the hook block plays a crucial role in the efficient operation of the tower crane It encompasses a range of speeds, from 0 to 58 m/min, although the crane operator has specified that it never exceeds 55 m/min to ensure safety and stability Notably, this speed remains constant regardless of the weight of the lifted load, facilitating consistent performance When considering the speed at which a trolley moves, it is essential to consider the lifted object's proximity to buildings or other objects on the site The presence of nearby structures or obstacles can significantly impact the speed at which the trolley can safely maneuver
If there are buildings or objects close to the trolley's path, it may need to reduce its speed to ensure it does not collide with anything or cause damage Factors such as the lifted object's size and weight, the surrounding space's dimensions, and the trolley operator's skill and experience can also influence the appropriate speed for the task Therefore, this trolleying speed factor is considered during the modeling process, where an average speed of 29 m/min is often employed to simplify calculations and ensure realistic representations
In addition to the trolleying speed, another significant parameter to incorporate is the speed slewing in the tower crane model This refers to the rotational movement of the crane around its vertical axis The crane can achieve slewing speed at 0.7 rad/min, which is the maximum, consistently maintained throughout all operations, as confirmed by the crane operator It is worth noting that the slewing speed remains consistent and cannot be changed by the weight of the transported object, thereby ensuring smooth and efficient maneuverability of the crane in various tasks.
Model summary
The essential information for the optimization process is collected into a excel file with multiple sheets and is summarized in the table below:
TABLE 4.1 Summary of The Opera Residence project’s input
The files specified in the table above are imported into Python to construct a computer model that faithfully reflects the site conditions.
Optimization process and results
The result accuracy and convergence were evaluated by testing various values for the number of colliding bodies, maximum number of iterations According to the testing, a maximum of 300 bodies is suggested; however, increasing the number of particles did not yield any improvements in the results and instead intensified the computational load Initially, the algorithm generates bodies that adhere to the predefined constraints By commencing the search within the feasible region, the algorithm effectively reduces computational power and saves time TABLE 4.2 provides a summary of the parameters to be utilized in the MOCBO algorithm
A reasonable level of accuracy was attained using the aforementioned parameters, as demonstrated by the presented results The FIGURE 4.6 illustrates the Pareto front obtained through the MOCBO algorithms, while TABLE 4.3 displays the optimal locations
Site Boundary Describes construction site boundary
Off-limit Areas The boundary of areas where tower cranes cannot be placed Demand Points X, Y coordinates and load demand of demand points
Supply Points Ground points for load lifting
Floor Heights Z coordinates of each floor
FIGURE 4.6 Pareto front obtained by MOCBO algorithm
TABLE 4.3 The Opera Residence optimal locations result
The optimal locations for two tower cranes were determined by optimizing for minimum travel time The SCM-C6018 60m crane was found to be best positioned at coordinates (126292, 64226), while the SCM-C6018 55m crane was determined to
126292, 64226 24066 867,061,706 0.80 be most effective at (42046, 53372) In these locations, the travel time and operation cost were calculated to be 24066 minutes and 867,061,706 VND, respectively
Alternatively, when optimizing for the best cost, slight differences were observed in the optimal locations, primarily due to changes in crane type and radius
In this case, two SCM-C5015 cranes with a radius of 45m were positioned at (126363, 63230) and (42115, 55324) Although the travel time increased to 34358 minutes, the cost decreased significantly to 793,885,155 VND, resulting in a savings of 73,176,551 VND
In addition to considering travel time and cost, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) score was also evaluated as a criterion for determining the optimal locations of the tower cranes Among the solutions analyzed, Solution 3 achieved the highest TOPSIS score of 0.88
FIGURE 4.7 Tower crane layout with the best time
FIGURE 4.8 Tower crane layout with the best cost
This solution involved placing an SCM-C6018 55m crane at coordinates (126364, 65748) and another SCM-C6018 55m crane at (45985, 54245) Although the travel time for this solution was 24980 minutes, the associated cost was 821,161,254 VND Solution 4 achieved the second highest TOPSIS score of 0.80 It involved positioning an SCM-C6018 60m crane at (126292, 64226) and an SCM-C6018 55m crane at (43546, 53372) The travel time for this solution was slightly lower at 23699 minutes, while the cost was higher at 867,061,706 VND compared to Solution 3
It is important to note that the TOPSIS score represents a measure of the overall performance of each solution It takes into account multiple criteria and provides a ranking of the solutions based on their similarity to the ideal solution In this case, Solution 3 obtained the highest TOPSIS score, indicating its relative superiority compared to the other solutions
FIGURE 4.9 Tower crane layout with the best TOPSIS score (most optimal option)
Therefore, if the TOPSIS score is given high importance in the decision-making process, Solution 3 would be considered the most favorable option During an initial site visit, the contractor firmly asserted that the selected locations for the main tower cranes was the only viable choice These assertions made by the contractor were backed by their expertise and experience Therefore, it was expected that the optimization process would confirm the optimal nature of the chosen location
TABLE 4.4 The Opera Residence’s original tower cranes locations
FIGURE 4.10 The original tower crane layout
Consequently, the results of the MOCBO have successfully validated the contractor's claims regarding the chosen location's optimality (see FIGURE 4.10) This outcome reinforces the contractor's initial predictions and solidifies the reliability of their decision-making process It also highlights the effectiveness of employing quantitative methods, such as optimization techniques, to inform and support construction-related decisions
In summary, emphasizing the TOPSIS and utilizing optimization techniques substantiates the conclusion that Solution 3 is the most favorable option The contractor's accurate prediction further underscores the validity of their decision- making process, which relies on objective analysis and empirical evidence
The first goal of the thesis has been met The MOCBO algorithm functioned as expected To further evaluate its effectiveness, the next step involves conducting a comparative analysis with other renowned algorithms to assess its performance.
Comparative analysis
In this section, the performance of MOCBO and two well-known meta-heuristic algorithms Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi- Objective Particle Swarm Optimization (MOPSO) are compared in terms of their effectiveness in resolving a practical TCLP The meta-heuristics are used to optimize the location of the tower crane
Based on the central limit theorem, as the sample size increases, the distribution of the sample mean approaches a normal distribution Therefore, to ensure reliable results, a sample size of 30 or more is required In this study, 30 independent experimental runs were conducted for each problem and algorithm, spanning across
Before using the NSGA-II and MOPSO algorithms to optimize a function, it is important to tune the parameters to ensure convergence, efficiency and to guarantee the lowest possible value is found The importance of tuning can be illustrated by testing the NSGA-II and MOPSO algorithms on a set of benchmark functions that were created for this purpose
With the NSGA-II algorithm, the selection of the Crossover Rate and Mutation Rate is crucial in tower crane layout planning optimization A high Crossover Rate of 90% has been chosen to exploit good solutions by combining beneficial features from different parent layouts, explore trade-offs between competing objectives, and ensure convergence towards a diverse set of Pareto-optimal solutions On the other hand, a moderate Mutation Rate of 70% has been selected to explore novel solutions, maintain diversity within the layout population, and achieve computational efficiency
By striking a balance between exploitation and exploration, this parameter configuration efficiently identifies a variety of optimized crane layout options that cater to different project objectives [39]
In the context of MOPSO, determining the values of c1 and c2 plays a crucial role in influencing the accuracy and convergence of the particles, as discussed previously Initially, Kennedy & Eberhart (1995) used a single weighting parameter, denoted as “𝐷𝐷” [40] However, in later versions, they introduced separate parameters for both local and global searches In the original version by the authors, the values were set to 𝐷𝐷1 = 𝐷𝐷2 = 𝐷𝐷 = 0 Nonetheless, in subsequent versions of the PSO algorithm, 𝐷𝐷 1 and 𝐷𝐷2 were given values within the range of [0,4], following a general rule that 𝐷𝐷1 + 𝐷𝐷2 ≤ 4 [41]
Given that the performance of MOCBO, NSGA-II and MOPSO is influenced by their respective control parameters, various tests were performed to determine the optimal parameter settings for achieving finite-time performance The selected parameter configurations for the algorithms are outlined in TABLE 4.5
TABLE 4.5 Parameter settings of the algorithms
TABLE 4.6 The comparison result of algorithms
Firstly, let focus on the "Best cost" metric, which represents the lowest operation cost achieved by each algorithm Among the three algorithms, MOCBO stands out with the lowest best cost of 744,266,880 VND MOPSO follows closely with a slightly higher value of 747,989,780 VND, while NSGA-II trails behind with a best cost of 781,470,260 VND This indicates that MOCBO excels at minimizing the operation cost for tower crane activities when compared to the other algorithms When considering the "Mean cost" metric, which represents the average operation cost over all the experimental runs, MOCBO also has a slightly better value of 854,270,720 VND In contrast, NSGA-II and MOPSO show worse performance in terms of cost optimization, with mean costs of 873,425,140 VND and 870,384,830 VND, respectively Thus, while MOCBO also may have achieved the lowest cost in some runs, on average
Shifting our focus to the "Best duration" metric, which represents the shortest operation time achieved by each algorithm, NSGA-II takes the lead with a duration of 23,312 minutes MOCBO and MOPSO follow closely with durations of 23,733 and 23,521 minutes, respectively Thus, NSGA-II proves to be the most efficient algorithm when it comes to minimizing the duration of tower crane operations
Algorithms MOCBO NSGA-II MOPSO
Best cost 744,266,880 781,470,260 747,989,780 Mean cost 854,270,720 873,425,140 870,384,830 Worst cost 918,440,320 922,218,800 923,983,940
Considering the "Mean duration" metric, which indicates the average operation time across all experimental runs, MOPSO demonstrates the longest average duration of 25,348 minutes NSGA-II performs better than MOPSO with a mean duration of 24,684 minutes, while MOCBO showcases the shortest mean duration of 24,052 minutes These findings suggest that, on average, MOCBO outperforms MOPSO and NSGA-II in terms of reducing the duration of tower crane operations
Examining the "Worst duration" metric, which represents the highest operation time among all experimental runs, MOPSO again exhibits the weakest performance with a worst duration of 27,969 minutes NSGA-II and MOCBO show relatively lower worst durations of 27,379 and 29,287 minutes, respectively, indicating their comparative strengths in operation time optimization
Lastly, let's consider the "Process time (s)/run" metric, which measures the computational time required for each algorithm to complete a single run In this aspect, MOCBO stands out as the most computationally efficient algorithm, with the shortest process time per run at 246 seconds NSGA-II follows with a process time of 325 seconds, while MOPSO requires the longest processing time per run at 451 seconds
To summarize, the results suggest that MOCBO performs well in terms of minimizing the operation cost and process time On the other hand, NSGA-II excels in reducing the duration of tower crane operations However, MOPSO demonstrates weaker performance in both cost and duration optimization These findings are crucial when deciding which algorithm to employ for tower crane operations, as the choice depends on the specific priorities and trade-offs desired in terms of cost, duration, and computational efficiency.
In this chapter, the thesis will place significant emphasis on the second case study, which revolves around the "Nguyen Kim Complex Building Center" project A notable aspect of this project is the utilization of a total of four tower cranes in its construction layout The proposed MOCBO algorithm will be thoroughly evaluated in the context of this specific scenario, showcasing its effectiveness in addressing the challenges posed by the complex operation involving multiple cranes This case study provides valuable insights into the real-world application of the MOCBO algorithm in a construction setting with a substantial crane fleet, offering practical solutions and implications for similar projects
5.1 Site layout information and modelling
The Nguyen Kim Complex Building Center is a prominent construction project situated on a 14,615 square meters site The scope of the project includes the development of a modern multi-functional complex with 2 basement levels, 5 podium floors, and 2 towers, each consisting of 14 floors
The project's basement area is 14,400 square meters per basement, providing ample space for essential utilities, parking facilities, and storage needs The basements will serve as a strong foundation for the rest of the structure
CASE STUDY 2 – FOUR TOWER CRANES MODEL
Site layout information and modelling
The Nguyen Kim Complex Building Center is a prominent construction project situated on a 14,615 square meters site The scope of the project includes the development of a modern multi-functional complex with 2 basement levels, 5 podium floors, and 2 towers, each consisting of 14 floors
The project's basement area is 14,400 square meters per basement, providing ample space for essential utilities, parking facilities, and storage needs The basements will serve as a strong foundation for the rest of the structure
The podium floors offer a total area of 9,000 square meters per floor These spacious podium levels are intended to house various commercial and recreational amenities, providing a lively and accessible space for visitors and occupants alike
CASE STUDY 2 – FOUR TOWER CRANES MODEL
The complex is further enriched by the presence of two elegant towers, Tower
A and Tower B Tower A spans 2,300 square meters per floor, while Tower B offers a floor area of 2,200 square meters Each tower boasts 14 floors, providing ample space for commercial offices, residential units, or other facilities, depending on the project's intended use
The typical height of each floor is 3.3 meters, contributing to an optimal and comfortable vertical space within the complex
FIGURE 5.1 The site layout of "Nguyen Kim Building" project
The most significant aspect of the project is the superstructure, which consists of a podium comprising five floors and two towers, each with 14 floors To facilitate efficient workflow, according to the recommendations of the contractor and the consultant, each of the five podium floors is divided into 15 work zones (~630m 2 each) Similarly, each floor of the towers is divided into three zones (~750m 2 each) The transportation times are calculated based on the central location of each zone
FIGURE 5.2 15 work zones of the 5 podium floors
FIGURE 5.3 Three work zones at each tower floor
The construction process has undergone significant changes in the 2 nd case study, with the exclusive use of plywood with adhesive coating for all formwork applications (see FIGURE 5.4) This type of plywood, featuring a water-resistant adhesive coating, has become the preferred choice due to its cost-effectiveness and practicality Contractors now rely on this material to reduce overall material investment while still maintaining a high level of performance
Plywood with adhesive coating is designed to replace traditional plywood with a film coating, and it offers a lifespan of 4-6 uses While it may not match the durability and strength of aluminum formwork, its reusability allows for multiple applications, making it a viable option for various construction projects
Previously, in the 1 st case study, the construction process relied on two primary formwork systems: plywood formwork for beams and slabs, and aluminum formwork for vertical components like shear walls and columns However, with the transition to using only plywood with adhesive coating, the reliance on aluminum formwork for vertical members has been reduced
The use of plywood with adhesive coating comes with its advantages, such as its cost-effectiveness, ease of handling, and quick assembly While it may not be as durable as aluminum formwork, the reuse of this material helps reduce material waste and promotes sustainability in construction practices
During the tower construction phases, the decision to exclusively use aluminum formwork has been replaced by the implementation of standardized plywood formwork systems This strategic shift aims to ensure consistency and uniformity in the construction process, optimizing efficiency and minimizing potential errors
To maintain the efficiency of the construction process, plywood formworks are subject to meticulous washing and lubrication before placement This preparatory step guarantees cleanliness and optimal functionality during construction Conversely, steel reinforcement typically does not require pre-use treatment and can be transported directly to the desired location
Once the plywood formworks with adhesive coating have served their purpose, they are carefully brought back to the ground, emphasizing their sustainable and reusable nature This practice sets them apart from other construction materials and allows for repeated utilization, reducing waste generation
Overall, the exclusive use of plywood with adhesive coating in formwork applications marks a significant change in the construction process, promoting cost- effectiveness, sustainability, and practicality in construction projects
FIGURE 5.4 Formwork system application plan
Crane data and modelling
The original site plan is currently serviced by four SCM-C6018 cranes, each equipped with a 60m jib length and a maximum capacity of 10 tons The tip of the crane has a maximum capacity of 1.8 tons This thesis aims to optimize not only the location but also the specifications of the tower crane Therefore, three additional types of tower cranes are considered in the data collection: SCM-C6018 (55m), SCM-C5015 (50m, 45m) The recommended base dimensions for the crane are 3m x 3m Moreover, the
71 crane has a maximum service height of 240m, which exceeds the height of the building.
Model summary
The essential information for the optimization process is collected into a excel file with multiple sheets and is summarized in the table below:
TABLE 5.1 Summary of The Nguyen Kim project’s input
The files specified in the table above are imported into Python to construct a computer model that faithfully reflects the site conditions.
Optimization process and results
Due to the transition from a 2-tower crane layout to a 4-tower crane layout, the number of colliding bodies has increased to 500 The evaluation of result accuracy and convergence involved testing different values for the number of colliding bodies and the maximum number of iterations Based on the testing, it is recommended to use a maximum of 500 bodies, as increasing the number of particles beyond this threshold did not lead to any improvements in the results but instead added to the computational load
Site Boundary Describes construction site boundary
Off-limit Areas The boundary of areas where tower cranes cannot be placed Demand Points X, Y coordinates and load demand of demand points
Supply Points Ground points for load lifting
Floor Heights Z coordinates of each floor
To efficiently handle the increased complexity, the algorithm initializes by generating bodies that conform to predefined constraints This approach allows the algorithm to commence the search within the feasible region, thereby reducing computational power requirements and saving time The summary of the parameters to be employed in the MOCBO algorithm is shown in TABLE 4.2
A reasonable level of accuracy was attained using the parameters, as demonstrated by the presented results The FIGURE 5.5 illustrates the Pareto front obtained through the MOCBO algorithms, while TABLE 5.3 displays the optimal locations
FIGURE 5.5 Pareto front obtained by MOCBO algorithm
TABLE 5.3 The optimal locations result
Solution 1 has the shortest time, with 14,882 minutes (approximately 10.36 days), while Solution 4 requires the longest time, with 21,907 minutes (approximately 15.23 days) Among the four solutions, Solution 2 and Solution 3 have intermediate time durations, 16,438 minutes (approximately 11.44 days) and 18,748 minutes (approximately 13.05 days) respectively
The cost is measured in Vietnamese Dong (VND) and represents the financial investment required for each solution Solution 1 is the most expensive option, with a cost of 1,781,594,390 VND Solution 2 has a lower cost of 1,562,965,996 VND Solution 3 is the third most expensive, costing 1,516,639,416 VND Finally, Solution
4 is the least expensive choice, with a cost of 1,500,300,888 VND
FIGURE 5.6 Tower crane layout with the best time
FIGURE 5.7 Tower crane layout with the best cost
The TOPSIS score is a measure of the overall performance or suitability of each solution compared to the ideal solution It ranges between 0 and 1, where 1 indicates the most favorable solution Among the four options, Solution 2 has the highest TOPSIS score of 0.90, indicating it is relatively close to the ideal solution Solution
1 follows with a score of 0.69, showing a decent performance On the other hand, Solution 3 has a TOPSIS score of 0.53, indicating it is less favorable than the first two solutions Solution 4 has the lowest TOPSIS score of 0.31, suggesting it is the least preferred solution
FIGURE 5.8 Tower crane layout with the best TOPSIS score
In terms of time, Solution 1 offers the shortest construction duration, which might be beneficial if the project needs to be completed quickly However, this option comes at the highest cost, making it less attractive from a financial perspective
Solution 4, with the lowest cost, might be appealing for projects with budget constraints However, it comes with the longest construction duration, potentially leading to delays
Solution 2 seems to strike a good balance between time and cost, as it has a relatively short construction duration and a reasonable cost Moreover, it has the highest TOPSIS score, indicating its proximity to the ideal solution, making it an attractive option overall
Solution 3 falls in the middle in terms of both cost and time While it is not the most time-efficient or cost-effective, it may still be a suitable choice depending on other project-specific considerations
In conclusion, the decision on which solution to choose depends on the project's priorities If time is critical and cost is not a major concern, Solution 1 might be the best option If cost-effectiveness is the top priority, Solution 4 would be preferred However, if a well-balanced solution is desired, Solution 2 appears to be the most suitable choice, given its high TOPSIS score, reasonable cost, and intermediate construction duration Solution 3 could also be a viable alternative if it aligns better with specific project requirements.
Comparative analysis
In this section, we once again compare the performance of MOCBO with two well-known meta-heuristic algorithms, NSGA-II and MOPSO, in terms of their effectiveness in resolving a practical Tower Crane Location Problem (TCLP) The parameter configurations selected for the algorithms are similar to those used in case study 1, with the exception of an increased population size, as outlined inTABLE 5.4
TABLE 5.4 Parameter settings of the algorithms
TABLE 5.5 The comparison result of algorithms
The data is presented in terms of different metrics such as cost, duration, and process time Here's a breakdown of the analysis:
Best cost: This represents the best cost achieved by each algorithm Lower values are better, as they indicate better performance MOCBO has the lowest best cost of 1,385,583,680 VND, followed by NSGA-II with 1,454,844,314 VND, and then MOPSO with 1,392,514,513 VND
Mean cost: This represents the average cost across multiple runs of each algorithm Again, lower values are better MOCBO has the lowest mean cost of
Algorithms MOCBO NSGA-II MOPSO
Best cost (VND) 1,385,583,680 1,454,844,314 1,392,514,513 Mean cost (VND) 1,590,375,173 1,626,034,494 1,620,374,422 Worst cost (VND) 1,709,838,167 1,716,872,472 1,720,158,591 Best duration (mins) 17,755 21,181 17,596 Mean duration (mins) 17,994 18,467 18,963 Worst duration (mins) 21,910 20,483 20,924
1,590,375,173 VND, followed by NSGA-II with 1,626,034,494 VND, and then MOPSO with 1,620,374,422 VND
Worst cost: This represents the highest cost achieved by each algorithm As before, lower values are better MOCBO has the lowest worst cost of 1,709,838,167 VND, followed by NSGA-II with 1,716,872,472 VND, and then MOPSO with 1,720,158,591 VND
Best duration: This represents the shortest time taken by each algorithm in a single run Lower values are better MOCBO has the lowest best duration of 17,755 mins, followed by MOPSO with 17,596 mins, and then NSGA-II with 21,181 mins Mean duration: This represents the average time taken by each algorithm across multiple runs Lower values are better MOCBO has the lowest mean duration of 17,994 mins, followed by NSGA-II with 18,467 mins, and then MOPSO with 18,963 mins
Worst duration: This represents the longest time taken by each algorithm in a single run Lower values are better MOCBO has the lowest worst duration of 21,910 mins, followed by MOPSO with 20,924 mins, and then NSGA-II with 20,483 mins
This metric indicates the average time taken by each algorithm for a single run in seconds Lower values are better MOCBO has the lowest process time per run of
410 seconds, followed by MOPSO with 542 seconds, and then NSGA-II with 752 seconds
Overall, from the presented data, it appears that MOCBO generally outperforms the other two algorithms in terms of both cost and duration NSGA-II performs worse than the other two algorithms in most cases, having higher costs and longer durations
This thesis aims to contribute novel and effective solutions to the complex problem of tower crane layout optimization, which inherently involves multiple objectives In order to address this challenge, a set of new multi-objective algorithms, collectively known as MOCBO, has been developed These algorithms build upon the success of the CBO metaheuristic algorithm, which has shown promising results in various optimization problems
The foundation of the MOCBO algorithms lies in the principles of energy and momentum laws of physics, specifically applied to one-dimensional collision scenarios By leveraging these fundamental concepts, the algorithms are able to intelligently explore and evaluate different layout configurations, considering factors such as spatial constraints, resource allocation, and operational efficiency
To assess the performance of the proposed algorithms, a comprehensive case study focusing on tower crane location optimization was conducted The case study involved real-world scenarios and considered various practical considerations that site managers typically encounter By comparing the results obtained from the MOCBO algorithms with those achieved by the NSGA-II and MOPSO algorithms, well-established benchmarks in the field, the superiority of the proposed algorithms was demonstrated
The MOCBO algorithms exhibited significant progress in discovering improved Pareto fronts, which represent the trade-off between conflicting objectives Through their advanced search mechanisms and innovative heuristics, the algorithms were
CONCLUSION AND RECOMMENDATION
Conclusion
This thesis aims to contribute novel and effective solutions to the complex problem of tower crane layout optimization, which inherently involves multiple objectives In order to address this challenge, a set of new multi-objective algorithms, collectively known as MOCBO, has been developed These algorithms build upon the success of the CBO metaheuristic algorithm, which has shown promising results in various optimization problems
The foundation of the MOCBO algorithms lies in the principles of energy and momentum laws of physics, specifically applied to one-dimensional collision scenarios By leveraging these fundamental concepts, the algorithms are able to intelligently explore and evaluate different layout configurations, considering factors such as spatial constraints, resource allocation, and operational efficiency
To assess the performance of the proposed algorithms, a comprehensive case study focusing on tower crane location optimization was conducted The case study involved real-world scenarios and considered various practical considerations that site managers typically encounter By comparing the results obtained from the MOCBO algorithms with those achieved by the NSGA-II and MOPSO algorithms, well-established benchmarks in the field, the superiority of the proposed algorithms was demonstrated
The MOCBO algorithms exhibited significant progress in discovering improved Pareto fronts, which represent the trade-off between conflicting objectives Through their advanced search mechanisms and innovative heuristics, the algorithms were
CONCLUSION AND RECOMMENDATION able to identify layouts that outperformed the solutions generated by the MOPSO algorithm This breakthrough represents a major step forward in the field of tower crane layout optimization
The outcomes of this research provide invaluable insights and tools for site managers and decision-makers in the construction industry By utilizing the MOCBO algorithms, they can effectively evaluate and compare different layout alternatives, ultimately selecting the most optimal solution for a given project This empowers project managers to enhance productivity, safety, and overall efficiency of tower crane operations
In conclusion, this thesis introduces novel multi-objective algorithms, the MOCBO, which are specifically designed to tackle the tower crane layout optimization problem By leveraging the principles of energy and momentum laws and incorporating innovative heuristics, the algorithms outperform existing approaches such as the MOPSO algorithm The findings of the case study demonstrate the substantial progress made in discovering improved Pareto fronts and achieving superior layouts The practical implications of this research extend to site managers, who can utilize the MOCBO algorithms as a valuable decision-making tool, enabling them to select the most optimal layouts from a range of alternatives.
Recommendations
While the presented thesis on tower crane layout optimization and the MOCBO algorithms contributes valuable insights and advancements to the field, there are still some potential gaps in research that can be addressed in future studies Here are a few recommendations:
1 Incorporating Real-Time Data: The thesis focuses on optimizing tower crane layout based on predefined scenarios and constraints However, real construction sites often experience dynamic changes and uncertainties Future research could explore the integration of real-time data, such as weather
81 conditions, material availability, and worker schedules, into the optimization process This would make the algorithms more adaptable and capable of responding to unforeseen events, leading to improved decision-making
2 Consideration of Multiple Construction Phases: Tower crane layout optimization is a complex problem that becomes even more challenging when considering multiple construction phases Each phase may have different requirements and constraints, and the optimal layout may vary accordingly Future research could investigate how the MOCBO algorithms can be extended to handle multi-phase construction projects, considering the sequencing and coordination of activities across different stages
3 Multi-Site Optimization: In some cases, construction projects involve multiple sites that need to be optimized collectively to achieve the best overall results Extending the MOCBO algorithms to handle multi-site optimization would enable project managers to consider the interdependencies and resource sharing between sites, leading to more efficient and cost-effective layouts
4 Uncertainty and Risk Analysis: Construction projects inherently involve uncertainties and risks Future research could explore methods for incorporating uncertainty analysis and risk assessment into the tower crane layout optimization process This would allow decision-makers to evaluate the robustness of different layouts and select ones that are less vulnerable to potential disruptions or delays
5 Integration of Building Information Modeling (BIM): Building Information Modeling (BIM) is increasingly being used in construction projects to create digital representations of buildings and infrastructure Integrating BIM data into the optimization process can provide richer information about the physical characteristics of the project, allowing for more accurate and detailed optimization Research could explore how the MOCBO algorithms can leverage BIM data to enhance the optimization outcomes
6 Consideration of Additional Objectives: The thesis focuses on addressing multiple objectives related to spatial constraints, resource allocation, and operational efficiency However, there may be other important objectives to consider, such as environmental impact, energy consumption, and worker safety Future research could explore the inclusion of these additional objectives in the MOCBO algorithms, enabling a more comprehensive and holistic optimization approach
7 Validation on Larger-Scale Projects: While the thesis conducted a comprehensive case study, it is worth considering the scalability and applicability of the MOCBO algorithms to larger-scale construction projects Further research could validate the algorithms on projects with a higher number of tower cranes and more complex spatial constraints to ensure their effectiveness and efficiency in handling larger-scale optimization problems
By addressing these research gaps, future studies can further advance the field of tower crane layout optimization and provide even more robust and practical solutions for construction industry professionals
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CRANE TRAVEL TIME & COST CALCULATION
In this appendix, the author demonstrated the detailed calculation of total crane travel time and cost for one specific circumstance The 3 rd solution shown in TABLE A.1 was used as an example The crane data parameter was also shown in section 4.3
TABLE A.1 The 3 rd solution of the Opera Residence optimal locations
The implementing of site layout modelling had been described in section 4.2 TABLE A.2 showed the demand points information of the Opera Residence case study
Label: This column shows the label of each demand point, identified as D_1, D_2, D_3, and so on up to D_16
X: The X-coordinate of each demand point, indicating its position on the horizontal axis
Y: The Y-coordinate of each demand point, representing its position on the vertical axis
Floors: This column shows the range of floors for each demand point, given as 2-6 for D_1 to D_10, and 7-26 for D_11 to D_16 This range might represent the number of floors in the Opera Residence buildings associated with each demand point
Plywood FW Load (ton): The load for Plywood formwork for each demand point, measured by divided total quantity of plywood formwork into each floor and each demand point
Aluminum FW Load (ton): The load for Aluminum Firewalls for each demand point, measured by divided total quantity of aluminum formwork into each floor and each demand point
Rebar Load (ton): The load for rebar at each demand point, again measured by dividing total quantity of rebar into each floor and each demand point
Similarly, TABLE A.3 showed the supply points information of the Opera Residence case study
Label: A unique identifier or code for each supply point Examples: S-RB-1, S-RB-2, S-FW-1, S-FW-2
X: The X-coordinate of the supply point's location, which represents its horizontal position on a coordinate system
Y: The Y-coordinate of the supply point's location, which represents its vertical position on a coordinate system
Z: The Z-coordinate of the supply point's location, which represents its elevation or height above a reference level (in this case, it is given as 0, which means they are at ground level)
Supply: The type of supply provided at each point
Firstly, travel distance between the supply, demand points and tower cranes can be calculated by the Eqs (3.1)-(3.3) Let’s choose demand point D_1, S-RB-1 and Crane 1 for rebar supplement
𝐿𝐿 = �(𝑆𝑆 1 𝑥𝑥 − 𝐷𝐷 1 𝑥𝑥 ) 2 + �𝑆𝑆 1 𝑦𝑦 − 𝐷𝐷 1 𝑦𝑦 � 2 = 50048.09 (𝑚𝑚𝑚𝑚) With the largest distance between the tower crane and the demand point or supply point being 36,716.65 mm (36.7 m), the maximum load the crane can lift ranges from 3.63 tons (at 35 m) to 3.39 tons (at 37 m), as shown in TABLE 3.2
Through interpolation, the maximum load is estimated to be 3.424 tons, resulting in a hoisting speed of 81.52 m/mm
The times for horizontal and vertical hook movements can be calculated from Eqs (3.6), (3.7), respectively
81.52 = 76.055 (𝑚𝑚𝑖𝑖𝐷𝐷𝐷𝐷) The travel time of tower crane can be calculated using Eq (3.8) by specifying the continuous type of parameter 𝛽𝛽 = 1
𝑇𝑇 1,1 1 = max{𝑇𝑇 ℎ , 𝑇𝑇 𝑣𝑣 } + 𝛽𝛽 × min{𝑇𝑇ℎ, 𝑇𝑇 𝑣𝑣 } = 78.937 (𝑚𝑚𝑖𝑖𝐷𝐷𝐷𝐷) Due to the maximum load being 3.424 tons and the task requiring the transfer of 46.95 tons of rebar, it is necessary to divide the load into several trips The calculation for the total number of trips can be expressed as follows:
𝐶𝐶 𝑗𝑗 F.92 3.424 ≈ 14 𝑑𝑑𝐶𝐶𝑖𝑖𝑝𝑝𝐷𝐷 The total travel time of tower crane at location 1 between supply point S-RB-1 and demand point D_1 is:
Continue to calcalute for the second crane, for each supply point, demand point and for each floor, the total travel time of the model can be achieved
For the cost of tower crane oparation: