ABSTRACT In a dynamic shift toward the digitalization of the construction industry, this research heralds a novel data-centric methodology that merges the temporal capabilities of 4D sim
INTRODUCTION
General Introduction
In the fast-paced world of construction, the timely completion of projects is often a crucial objective, not only for the immediate community affected by the construction activities but also for the construction companies and their clients Quick completions can significantly benefit local residents by reducing the disruptions caused by construction noise, dust, and traffic changes [1] Moreover, construction firms stand to decrease their operational costs, as prolonged projects tend to inflate expenses daily [2] Similarly, clients eagerly anticipate the prompt delivery of their projects for early utilization or investment returns
The responsibility of bringing a construction project to fruition rests with the construction company, which relies on detailed planning to guide project execution However, a study conducted among construction industry professionals revealed that planning inaccuracies, specifically deviations from the initial plan, often lead to increased project costs [3] The solution proposed to mitigate these issues emphasizes the importance of realistic project planning
Project planning is fundamentally rooted in the knowledge and experience of the construction process [4, 5] This expertise is derived from the historical execution of projects, where past experiences shape the expectations and strategies for future projects Companies aim to refine their planning processes by closely monitoring ongoing projects to identify and address potential bottlenecks However, despite awareness of inefficiencies within certain processes, there is a gap in effectively addressing these issues due to a lack of precise knowledge on what exactly requires improvement This leads to a scenario where decisions are based more on intuition than on solid evidence, hindering optimal problem- solving
To enhance their operational efficiency, many construction companies are now turning towards Building Information Modelling (BIM) and LEAN methodologies [6] These approaches focus on streamlining processes to elevate customer satisfaction, grounded in a deep understanding of one's operations Despite these advancements, it is highlighted that most monitoring systems still rely heavily on manually compiled notes, making the process time-consuming, costly, and error prone Consequently, data collection remains sporadic and underutilized
The necessity for efficient process monitoring in construction, coupled with the inadequacies of current observational tools, underscores the need for further research in this area Process mining emerges as a promising solution within this context, offering insights into process improvements across various application domains This technique focuses on analyzing event data to extract process-related information, thereby facilitating a factual rather than speculative understanding of process issues and improvements
The digital transformation, characterized by an "explosion" of data, has bridged the gap between the digital and physical realms [7], particularly with technologies such as RFID, GPS, and sensor networks This evolution has gradually found its way into the traditionally conservative construction industry, enabling the integration of new technologies with BIM to enhance project management The ability to digitally record and analyze physical events as event logs opens up new possibilities for process optimization through process mining, providing a means to streamline operations, anticipate problems, and enforce policies more effectively
However, the construction industry faces challenges in collecting and structuring event logs due to the diversity of data sources and the unstructured nature of monitoring data [8] BIM processes, which require structured data protocols, offer a potential framework for overcoming these challenges Nevertheless, the integration of process data within BIM is often overlooked, underscoring the need for a more concerted effort to recognize and exploit the value of this data
While research on the application of process mining in construction is still in its nascent stages, early studies indicate its potential to enhance communication and process efficiency in construction projects [8] This exploration into the intersection of process mining, BIM, and construction processes not only highlights the potential for operational improvements but also sets the stage for a comprehensive study aimed at shortening project durations through technological advancements The proposed research will delve into both theoretical and practical aspects, outlining the prerequisites for integrating process mining with BIM and evaluating its effectiveness through case studies This approach aims to forge a path toward more efficient, timely, and cost-effective construction project management, benefiting all stakeholders involved.
Problem Statement
The inception and successful execution of construction projects remain critical in ensuring timely delivery, cost efficiency, and meeting stakeholders' expectations [9, 10] The complex nature of construction projects, characterized by intricate processes and the involvement of numerous stakeholders, necessitates precise planning and monitoring to mitigate the risk of project delays, budget overruns, and suboptimal resource utilization This complexity is compounded by the dynamic nature of construction environments, where unforeseen circumstances often necessitate adjustments to the project plan Traditional methodologies for construction project management, while foundational, are increasingly proving inadequate in addressing the nuanced requirements of modern construction projects [11, 12] This is particularly evident in the reliance on manual processes for monitoring construction progress, which are not only time-consuming but also prone to inaccuracies and inefficiencies
The advent of Building Information Modeling (BIM) and data mining techniques presents an opportunity to revolutionize construction project management by enabling a more data-driven, precise, and efficient approach to planning, monitoring, and executing construction projects BIM facilitates a collaborative environment where all project data is centralized, allowing for enhanced visualization, improved coordination among stakeholders, and better decision-making throughout the project lifecycle [13] On the other hand, process mining offers the potential to extract insightful data from event logs [14, 15], thereby enabling the identification of bottlenecks, deviations from the plan, and opportunities for process optimization
Despite the promise held by these technologies, their integration into the construction project management workflow is not without challenges The generation of high-quality event logs, a prerequisite for effective process mining, is often hindered by the lack of structured data capture mechanisms on construction sites Additionally, the construction industry's fragmented nature poses significant barriers to the widespread adoption of BIM and process mining techniques, as stakeholders must navigate varying degrees of technological readiness and willingness to embrace change
This research aims to bridge the gap between the theoretical potential of BIM and process mining and their practical application in construction project management By investigating the barriers to the adoption of these technologies and exploring methodologies for generating actionable insights from construction process data, this research seeks to establish a framework for integrating BIM and process mining into the construction project management paradigm Specifically, it will examine how these technologies can be leveraged to improve project planning accuracy, enhance real-time monitoring of construction progress, and facilitate a more adaptive and efficient project execution strategy In doing so, this research will contribute to the body of knowledge on construction project management and offer practical recommendations for industry practitioners seeking to harness the benefits of BIM and process mining in the face of the sector's evolving technological landscape.
Research Objectives
The aim of this research is to drive digital transformation in the construction industry by developing a framework that integrates temporal representation and data mining techniques for insightful event log analysis The study focuses on enhancing 4D simulation through the incorporation of real-world data from IoT devices and BIM models, establishing a robust diagnostic mechanism for identifying planning deviations, and creating predictive analytics models to forecast future construction challenges The research also seeks to validate this approach through a practical case study in Sứborg, ensuring its feasibility and effectiveness
Range of study: Sứborg, Denmark.
Research Targets
This thesis focuses on BIM specialists, data scientists and construction project managers
• BIM specialists are at the forefront of this integration They possess deep expertise in managing and manipulating digital representations of physical and functional characteristics of places Their role is crucial in ensuring that the digital models accurately reflect the planned and actual states of construction projects, thereby providing a solid foundation for analysis and decision-making
• Data scientists, on the other hand, play a pivotal role in extracting meaningful insights from vast amounts of data generated during construction projects By applying advanced analytics to construction data, they uncover patterns and insights that can lead to more informed decision-making, prediction of potential issues, and recommendations for process optimization
• Construction project managers are the linchpin in this trinity With their comprehensive oversight of project planning, execution, and closure, they ensure that the insights provided by BIM specialists and data scientists are effectively translated into actionable strategies Their ability to integrate these technological advancements into day-to-day operations is key to realizing enhanced efficiency, cost savings, and adherence to project timelines.
Research Scopes
To handle the research problem, this study limits its scope by highlighting the followings: a Integrate 4D simulation techniques into the construction project management b Implement advanced process mining algorithms for analysis of construction event logs c Deploy diagnostic tools to identify bottlenecks, inefficiencies and potential risks in the construction process d Develop predictive models for anticipating challenges
In addition, some parameters have also been illustrated to clarify the scopes:
(CFA = 8500 m 2 , building height = 56.642 m, number of floors = 17)
• Time-line consideration: one year (from January 2019 to December 2019)
• Research duration: four months (from January 2024 to May 2024)
Research Methodology
The following technique was used in this study to achieve the study's objectives: a Literature study
The initial phase of the research involves a thorough literature review to establish a theoretical foundation and identify gaps in existing knowledge This review encompasses academic journals, industry reports, and case studies related to 4D simulation, process mining, and construction project management b Approach development
A data-driven approach is developed through a thorough examination of existing research, aiming to apply the Deming cycle [16] into the construction industry The main objective of this proposed data-centric approach is to produce a modified concept that diagnose the current status of construction project and forecast the number of finish task in the near future in order to optimize the project quality and duration a Validation of the developed approach
To illustrate the validity and reliability of the findings, it was then put into a case study from Denmark A real-world construction project that has implemented 4D visualization and process mining framework serves as the focal point of this examination Data collection in this phase employs a combination of direct observations and analysis of project documentation and digital outputs Quantitative data are subjected to statistical analysis to quantify the benefits of the implementation This will involve the collection of event logs and BIM data, which will be analyzed using process mining tools to identify patterns, bottlenecks, and deviations from project plans Statistical methods will be applied to assess the impact of 4D illustration and process mining on project outcomes, such as time savings and improvements in resource allocation.
Structure of the study
The thesis includes five chapters:
▪ Chapter 3: Architecture of the proposed approach
LITERATURE REVIEW
Definitions and concepts
Time, cost and quality are three key factors playing an important role in planning and controlling construction projects [17] As for decades, it has been universally acknowledged that construction projects generate a significant impact on societies Despite the beneficial outcomes it contributes to the development of each nation and region, the drawbacks it creates are also noticeable [18, 19] According to Sanna et al., most construction projects are behind schedule and in turn it leads to financial issues such as cost overruns [20] Therefore, handling these limitations is a challenging task when managers apply the traditional methods [21]
2.1.1 Construction planning and monitoring o Planning:
The preparatory or planning activities are usually in the very first phase of each project, which necessitates securing a permit Typically, a project initiator such as the real estate agent acquires land and engages architects or structural engineers to create the building’s drawings Numerous organizations have involved in construction projects; however, public agencies predominantly have been responsible for issuing various agreements, certificates and permits To illustrate the complication of this process, Stanislaw et al detailed a variety of departments frequently consulted for construction permits, including area and heritage planners, industrial lands planners, and so on [22] Specific requirements are imposed by each department on the design, making the validation process with such extensive criteria challenging As a result, project initiators often appoint a project manager to oversee and facilitate the process of obtaining a construction permit Once the allowance is granted by the government, the phase of preparation concludes then advance to the phase of construction execution
During the construction execution phase, construction project planners play a crucial role within construction organizations as they ensure the processes of estimating and tendering must be grounded in a thorough understanding of the necessary methods, timing and spatial requirements for carrying out tasks, while also considering the associated risks for each project [23] Project plans can be developed using a variety of techniques that mainly fall into two primary categories: activity-based and location-based methodologies [24] The dominant scheduling technique in the construction industry which is activity-based scheduling has been prevalent since its initial development in the 1950s, and later built upon the methodologies developed by Taylor et al during the twentieth century Gantt charts had been first published that were then integrated into the Critical Path Method (CPM) which was first named by Kelley and Walker in 1959 [25] CPM, a deterministic scheduling method, provides a graphical representation of projects, forecasts completion times, and identifies critical activities essential for maintaining the schedule For projects that are complex yet routine with minimal completion time uncertainty, CPM is suitable; however, for less predictable projects, other methods such as Program Evaluation and Review Technique (PERT) are preferable PERT, which accounts for randomness in activity durations, helps in predicting the expected completion times, calculating the probability of event completions, and identifying critical and non-critical activities, thereby aiding resource allocation within project management Nonetheless, its application can be challenging due to limited experiential data on activity durations
Figure 2.1 Example of a Gantt chart (Tory et al., 2013)
Both the CPM and the PERT operate on a logical framework wherein discrete tasks are connected through defined relationships, forming a sequence where each task is linked to its predecessors and successors within an activity model These methods, termed activity-based planning, are highly regarded for their precision and effectiveness, significantly boosting project outcomes in the construction industry, as Kenley have noted [24] In contrast, location-based scheduling concentrates on maintaining the continuity of work crews assigned to specific tasks This approach, with roots stretching back to the early 20th century and exemplified by its use in the construction of the Empire State Building in 1929, has traditionally seen limited application in commercial construction However, the integration of CPM with location-based scheduling techniques in modern software platforms, such as Navisworks or Synchro, has started to change this, broadening their usability and popularity Methods based on location, like the flowline and the line of equilibrium, prioritize the logistical flow of resources across various project sites, focusing on task execution rather than repetitive activities Although it could be appropriate to describe these methods as task-based, they are referred to as location-based to differentiate them from activity-based methods This terminology helps prevent confusion and highlights their main advantage—providing clear graphical representations of production rates and task durations, thereby enabling efficient resource flow and project management
Figure 2.2 Example of a PERT network (Soroush, 1994) o Monitoring:
Saidi et al highlighted that efficient gathering of data, rapid data evaluation, and successful dissemination of well-interpreted are critical issues for construction companies [26] In other words, these elements are essential for managing the vast amounts of information generated in construction projects and ensuring that decisions are made based on accurate and current data
On the other hand, Kopsida et al provided a detailed analysis of the advancements in construction monitoring technologies [27], categorizing them into four main areas: data acquisition, involving technologies for capturing as-built scenes; information retrieval, which extracts necessary data from the as-built information; progress estimation, comparing the as-built with the as-planned models to assess progress; and visualization of these results The study reviews several technologies such as laser scan (e.g LIDAR), RFID, vision-based reconstruction (e.g Computer Vision, AR, VR) and vision static images (e.g SfM), noting that the choice of the most suitable monitoring method depends on the specific circumstances of each project Recommendations for the most appropriate technologies are based on the type of activity, the construction environment, and the specific needs of the inspection, with an illustrative overview provided in Table 2.1
Figure 2.3 Flowline illustrating four tasks and demonstrating the impact of delays
Mobile AR systems are highlighted as generally advantageous due to their affordability and ease of use across different environments However, they entail additional costs and time for installation and maintenance, such as setting up geo-spots, utilizing Wi-
Fi networks, and their lack of real-time performance While model-based AR algorithms have been developed for comparing as-planned and as-built models, their effectiveness in real-time operations on sites remains untested
The application of computer vision, laser scan, and image processing techniques has been prominent in recent findings aimed at automating progress estimation Laser scanning is noted for its high accuracy in capturing as-built data but is costly and labor- intensive when detailed information on every object of a scheme is required A different method involving picture evaluation can generate points collection in space using SfM method to detect progress discrepancies Nonetheless, this method does not adequately
Table 2.1 Performance of progress monitoring solutions (Kopsida et al., 2015) capture interior elements and tasks, resulting in a low level of automation for indoor progress monitoring Consequently, manual comparisons between as-planned and as-built models are necessary
In project management, the schedule serves as an essential tool, commonly utilized to meticulously plan out each phase of a project Essentially, a schedule outlines the timing for every task within a project, enabling project managers to systematically analyze each activity and its interrelations This pre-construction "paper build" of the project helps in establishing the start and end dates, as well as the duration of each activity, which are crucial for budgeting and resource allocation Accurate duration estimates are particularly significant as they directly influence project costs For example, inaccurately estimating the time needed for an activity that requires rented equipment can quickly erode expected profits Thus, proper and effective scheduling is vital for adhering to deadlines and maximizing profitability
The scheduling process typically involves the following steps:
1 Identification of all project activities
3 Estimation of the duration for each activity
5 Revision and adjustments as necessary
Various methods are employed to schedule construction projects, with a few notable examples being:
The CPM identifies the sequence of project activities that have the longest cumulative duration, representing the shortest timeframe in which the project can be completed [28] Any delay in these critical activities invariably extends the overall project duration CPM can be implemented in two formats: Activity on Arrow (AOA) and Activity on Node (AON) In the AOA approach, activities are represented by arrows, while in AON, activities are denoted by nodes or boxes Due to its more straightforward representation,
AON is generally preferred over AOA [29] Illustrations below demonstrate the scheduling techniques used for AON and AOA respectively
2.1.2.2 Program Evaluation and Review Technique (PERT)
PERT is a statistical tool used to plan and control project schedules, particularly suited for complex projects with uncertain elements where activity durations are not clearly defined To implement PERT, three-time estimates are assigned to each activity: the
Figure 2.5 Activity on Node (Tarigan, 2021)
Figure 2.4 Activity on Arrow (Tarigan, 2021) optimistic time estimate (To), which assumes everything goes as well as it possibly can; the most likely or normal time estimate (Tm), which is based on the most probable duration under normal conditions; and the pessimistic time estimate (Tp), which considers the worst-case scenario [30] The expected time for each activity is then calculated using these three estimates, allowing for a comprehensive analysis that accommodates uncertainties in project scheduling:
Standard deviation is a statistical measure used in project management to assess the probability of completing a project within the expected timeframe It provides insights into the variability or uncertainty in project duration estimates To calculate the standard deviation for project completion times, the formula for variance, which is a precursor to finding the standard deviation, is used first The standard deviation is then derived from the square root of the variance:
2.1.3 Implementation of BIM and IT systems in construction management
Research gap
Construction projects are often marked by fragmentation, necessitating the repetition of learning curves Traditionally, these projects rely on manual processes and conventional communication methods like phone calls, faxes, and emails (Dave et al [75] in 2015) Despite widespread discussion over time, this issue remains unresolved Presently, there's a growing adoption of IT systems across all stages of construction projects While construction process data may exist within these systems, it's often in unstructured formats, containing tasks devoid of detailed information beyond timing
With the increasing adoption of BIM, planning information can be linked to building elements, rendering process-related data accessible through archives and dataset that are linked directly to real data on site Progress can be recorded thanks to computerized tracking gadgets, and techniques for creating timetables from data-source based on event are heading to automation development Nonetheless, there hasn't been much focus on integrating surveillance information with databases based on recordings
Kassem et al [76] in 2015 proposed development of advanced data-sources in fourth dimension could enhance on site management as well as advocate for further research in this area To address this, the potential of utilizing event-based databases to expedite construction projects through autonomously acquired process data is being explored.
ARCHITECTURE OF PROPOSED APPROACH
Conceptual framework
A data-driven approach is proposed to establish a closed-loop connection between the physical and cyber realms, leveraging vast amounts of IoT data from BIM-enabled construction projects Figure 3.1 illustrates the conceptual architecture of the framework, designed to operate seamlessly across the construction phase for intelligent construction monitoring and management Notably, this framework integrates BIM with real-time data gathered by IoT devices, alongside knowledge extraction from data analytics, marking a relatively novel advancement The workflow can be outlined as follows:
Initially, an as-planned model is constructed in the cyber world Then tower crane or UAV equipped with LiDAR technology is deployed to provide IoT services from elevated vantage points above the construction site The support tool captures 3D point clouds to perceive and respond to the current as-built environment, enabling real-time operational monitoring Following this, the inspection data is transmitted to the BIM cloud system for storage To maximize the utility of the point cloud data, it undergoes comparison with the as-planned IFC model using a tool called RAAMAC within the BIM server The developed tool is tasked with identifying and communicating disparities between actual and planned performance, culminating in the creation of an as-built IFC model for automated construction progress monitoring
Nevertheless, the IFC format, used for saving the digital building description, is stored in a plain text file format, which is not readable by data mining algorithms To address this issue, another pre-existing tool called IFC Logger is utilized to automatically extract relevant data from the IFC files, including construction tasks, worker information, time data, and other pertinent details This tool generates event logs in a format that is easily understandable for computers To bolster data quality, data cleaning methods are employed to eliminate noise
As illustrated in Figure 3.1, the freshly collected data obtained through IoT devices in the previous step present opportunities for the upcoming stage as it can provide a contemporary outlook on the implementation of the as-planned model into constructing a real-world entity In other words, by going through the diagnosis task it can be adopted
Figure 3.1 Proposed approach inspired by Deming cycle insights into workflow and collaboration Furthermore, potential bottlenecks that emerge in the actual process can be readily identified, enabling preemptive measures to prevent these unnecessary delays from occurring Then, time series analysis is conducted to intelligently gauge and forecast the successive construction progress from a future perspective Finally, the feedback can be promptly relayed back to the physical side to dynamically adjust construction scheduling and worker arrangements.
Difference between proposed approach and Deming cycle
In the proposed framework, the value accrued by the construction project is not boundless; it has its limitations Unlike the Deming cycle, where value increment occurs with each complete round and remains finite without constraints, the proposed framework exhibits a trajectory of increasing value until it reaches a pivotal moment in the future timeline At this juncture, the accumulated value assimilates all entities traversing through the framework, rendering it unsuitable for further use Consequently, necessitating the establishment of a new approach capable of accommodating the evolving value dynamics Conversely, the workflow within the Deming cycle remains steadfast, impervious to external fluctuations, retaining its predetermined path regardless of changing conditions
In the proposed framework, the structural backbone supporting the workflow is akin to a container, a space that remains impervious to destruction This standard acts as the bedrock, analogous to space housing stars and planets in orbit In contrast, the standard within the Deming cycle serves as a stabilizing force, preventing workflow regression and subsequent degradation of value or quality Within the Deming cycle, these components— standard, workflow, and value—exist as distinct entities However, in the proposed framework, the standard assumes paramount importance, resembling a vast expanse that encompasses both workflow and value Here, while value may fluctuate, and workflows may dissolve and reemerge, the standard persists indefinitely, impervious to destruction.
MODEL IMPLEMENTATION AND VALIDATION
Model Implementation
4.1.1 Case study – Vindspor Hứjde Residence
The case study focuses on a substantial structural process comprising several activities The aim of this case study is to assess the suitability of the model for a real- world project that involves a large number of activities and multiple interdependencies among them The aim is to determine whether the model can effectively handle the intricacies and challenges posed by such a project scenario
Project’s information is shown as follow:
Project name: Vindspor Hứjde Residence
(CFA = 8500 m 2 , building height = 56.642 m, number of floors = 17)
Execution phase started in January 2019 and finished in July 2020
Figure 4.1 Real-world project captured in February 2024
Figure 4.2 As-designed 3D model captured in Enscape
Figure 4.4 As-deigned architectural drawing of typical floor plan Figure 4.3 As-designed architectural drawing of ground floor plan
Figure 4.5 As-designed architectural drawing of elevation front view and side view
Figure 4.6 As-designed architectural drawing of main cross-section.
Model validation
First and foremost, as-designed IFC exported from Revit and the construction schedule plan generated with the use of MS Project are utilized as input for the generation of as-planned IFC These two components are then processed in Synchro 4D software by connecting every element to activities which have been decided specifically in the planning As Synchro facilitates the exportation to IFC format, the final output is expected to be an as-planned IFC model The detailed workflow is illustrated in Figure 4.7
The as-planned models were initially generated based on a schedule crafted at the project's outset However, as the project progressed, this schedule became partially outdated, resulting in data outputs that were only partially reusable Additionally, there
Figure 4.7 Workflow demonstration in planning phase
Figure 4.8 As-planned IFC models from week 22 to week 24 in 2019 (demonstrated by Synchro 4D) were issues with the IFC exporting function of Synchro, leading to alterations in the object tree within the IFC during the creation of the as-planned IFC model While this did not pose a problem for this study due to the development of a tool tailored to the altered IFC object tree, it could become problematic if one enterprise seeks to implement the anticipated IFC and then want to utilize this approach more widely In such cases, preserving the original structure of the IFC model would be imperative
During the construction execution phase, the LIDAR technology is employed to monitor the progress on site by equipping it to tower crane and UAV Multiple drone flights were conducted in the morning to ensure safety according to local regulations, while a tower crane was also operated simultaneously to gather real-time data on site Data of three weeks from week 22 to week 24 are used within this case study The detailed workflow of the monitoring task is depicted in Figure 4.9
RAAMAC created the D4AR tool to automate the collection, processing, and communication of construction progress monitoring data [77] Their tool was developed with the vision that regular construction site point clouds would serve as input to track progress The assumption was that point clouds are routinely captured and accumulated into a database on nearly every construction site Nevertheless, the final step entails aligning the as-planned IFC with the as-happened point cloud Hence, the position and coordination of two entities are mapped using method for absolute orientation utilizing unit
Figure 4.9 Workflow demonstration in monitoring phase quaternions in a closed-form solution [78] If the models are misaligned due to procedural errors, then it can be manually adjusted for alignment by adding control points Figure 4.10 illustrates the representation of the as-built scene comparing with the as-planned IFC
D4AR examines the clarity and possession of two assemblies using a combined view that includes the as-planned IFC and the as-built point cloud A method based on monitoring machine learning is used in unison with this evaluation to ascertain whether or not an object has been constructed Consequently, elements are classified as either built, highlighted in green, or not built, highlighted in red
Once a week, the assessment is performed in this case study Based on scanning techniques, an entity collection has been generated including any missing ones In other words, each missing item in the collection is behind schedule Table 4.1 illustrates an example of the missing entity collection
Figure 4.10 Comparison of as-planned IFC and as-happened point cloud
The collection of missing items from week 22 to week 24 was briefly described below:
In week 22, there were 4 missing objects which were partition walls
In week 23, there were 10 missing objects which were windows
In week 24, there were 22 missing objects which were windows
The scanning task was deployed in just three separate days in the whole continuous three weeks Therefore, the process was recorded for three actual scenarios Since getting point clouds from the construction site was much easier than in previous decades thanks to the advancement of technologies (e.g LIDAR), in this case study it could not be conceivable to analyze deeply into the problem Nevertheless, it has been demonstrated that given the level of technology today assessing planned discrepancies of an operation using this kind of proposed approach is feasible
Moreover, it may be possible to entirely automate the workflow of collecting point clouds, mapping them with as-planned IFC, and generating missing entity collections The amount of data in the collected set could also be much larger as it could be available to scan working field frequently To conclude, employing automation in this procedure is advised for further study
Another thing should be considered is that there was some outdatedness to the utilized timetable To be precise, the effect of covid-19 pandemic and disruption of world supply chain meant that the resources for constructing wall entities were believed yet late in arriving and those reasons were also partly true if considering for the window entities It needs to be essential that the as-planned IFC comes from the most recent release if the approach is utilized in real life
Table 4.1 Collection of missing entities in accordance with comparison of as-planned IFC and as-happened point cloud in week 22
There is also a point should be discussed that the scheduling discrepancy has been addressed by utilizing technologies Nonetheless, the reasons why this problem was about to happen are not examined in this research and it might be identified by using as-happened point cloud data to check what exactly is happening on the construction site at some certain time As it can spot a variation at a specific time, it is advisable to further study in order to apply such an approach
4.2.3 Diagnosis of current state of construction progress
The first condition should be considered is an event log must be taken into account for using data mining techniques to diagnose the current state of the construction progress That is to say, the as-planned IFC has been transformed to an event log thanks to the aid of BIMserver Eventlog service (see more the workflow of how it operates in the Annex B in the appendices) The scanning data is then incorporated into the event log to create an as- happened log Hence, the diagnosis of the current state of the construction progress can be proceeded with this input data Table 4.2 illustrates a portion of more than 16000 occurrences that are included in the case study’s as-planned event log
The outcomes of the scanning task must be incorporated into the as-scheduling log to produce an as-happened log Excel is used to meticulously integrate two files The event log has an additional column labeled ‘Punctuality’ as a result of the as-happened indicator Table 4.3 illustrates a portion of the log that adds one additional column which assesses the punctuation of each entity Furthermore, when scrutinizing the log, there are more than
Table 4.2 A portion of an as-scheduled log generated from as-scheduled IFC
Table 4.3 A portion of an as-happened log with additional attribute of punctuation
To have a deeper understanding of the construction progress, an as-happened log is produced Nevertheless, this record merely indicates whether or not scheduled activities were completed on time It does not explain tasks that were carried out but were not scheduled Consequently, it is likely that a couple of occurrences are overlooked It is acceptable because this finding is exploratory and focuses on the approach as a whole Nonetheless, adding unscheduled activities to the as-happened record and comparing scheduled and unscheduled activities may make intriguing for future study
DISCUSSION AND RECOMMENDATION
Discussion
A proposed procedure is suggested to allow for ongoing learning loops This approach is most likely just one of many strategies used to achieve the more ambitious goal of cutting execution duration Nonetheless, this project has demonstrated the value of the workflow and technologies employed It is important to stress that what matters is how the concept of continuous improvement is ingrained in the corporate culture, not which technology is employed Technology is only a tool to help reach the goal So, the process mining appears to have significant value in the construction industry
It makes sense to say that process mining on a bigger scale will be even more beneficial given that this research has only been used on one project and it has already shown benefits But those analytics will not be available without data Thus, documenting processes is a crucial step Does this mean that all contractors must thereafter use cutting- edge capturing technology? It is evident that technology will be used more and more to handle daily operations
When selecting new technology, a contractor may consider how applicable data reuse is and whether the technology can contribute to an ongoing culture of data-driven learning within the organization And process mining applications will be particularly helpful in complex projects where managing the big picture is challenging It can be suspected when businesses claim they handle everything that tracking advancement is not important Process mining will be useful to find improvement areas inside details for construction organizations, if it was considered to be elite athletes who measure every aspect of its performance to excel and become the greatest
This research makes an effort to keep track of the construction progress However, individuals being monitored may have privacy issues as a result of the installation of technologies such as intelligent camera systems To be clear, in this study the issue of privacy was ignored; still, it should never be the intention of process improvement to denigrate any individual It is reassuring to acknowledge that data anonymization is always feasible and should not have been a major concern Therefore, that is merely an assumption which may require further investigation
The software suggested in this research is far from ideal because some of them-such as the Eventlog Service- are standard products created specifically for this study Nonetheless, the study pointed out new research strategies might be made possible by a combination of tools and technologies This might encourage academics and business to investigate how process mining and BIM work together to optimize execution phrase With both research areas growing in popularity, it will be able to support one another in years to come
The time series data is surprisingly rich in hidden information about activities that can provide insight into the nature of project evolution It can be then used to directly observe characteristics of completed activities, providing supervisors in project management with firsthand evidence In other words, more trades can work as a building climbs through its floors In addition to hiring more personnel, it is likely that using more advanced methods and fostering closer teamwork can also speed up the project’s completion Workers’ familiarity with their coworkers and the duties at hand will progressively increase as the project moves forward Furthermore, according to Yue Pan and Limao Zhang in 2021, an intricate time-series model does not necessarily imply superiority [79].
Research contribution
Firstly, the integration of 4D simulation and process mining provides a powerful tool for construction project managers to simulate, diagnose, and predict real-world execution which in turn leads to more informed decision-making This not only enhances project planning but also facilitates the identification and mitigation of potential bottlenecks, delays, and inefficiencies in construction processes Next, the research also offers a data-driven approach that can be directly applied in construction project management that enables practitioners to harness the benefits of emerging technologies for improved project outcomes The proposed methodology contributes to more accurate project scheduling, resource allocation, and risk management In addition, the developed tools and frameworks have the potential to enhance communication and collaboration among project stakeholders by providing a transparent and visual representation of the construction process.
Research recommendation
Process mining elaborated BIM can be implemented at suppliers and sub- contractors in addition to main contractors If this occurs, integrating those event logs will allow supply chain alignment to be set up Process mining will therefore be made feasible not only at the project and company scales but also at the industry level Examining this may be worthwhile
As-built BIM models will probably be needed for every construction project due to customer demands and regulations governing quality control The method by which the as- built BIM was created might be included in an event log approach It might be possible to do research on the suitability of event log approach based on BIM
Process mining is only one area of study within the discipline of data science There is a growing desire for more fascinating data science fields Given the increasing volume of data generated by the construction sector, it makes sense to investigate the potential applications of computational intelligence approaches Artificial neural networks, simulated annealing, colony optimization, genetic algorithm, or swarm intelligence are a few examples of methods which could be intriguing to investigate in the construction sector.
Conclusion
Through the integration of 4D visualization and data-mining technique, a comprehensive workflow comprising a physical entities, a cyber model and connection data is established towards a smart construction service The created loop is important because it makes construction project management more intelligent and automated which may reduce the likelihood of cognitive errors made by humans To be more precise, IoT devices are used to gather data in real-time regarding the true state of the construction project with minimal human intervention The physical-and-virtual-synchronization is based on the rich information supply from the IoT devices For model interoperability, this information must be mapped with the as-planned IFC, and the event log must then be kept for data analysis
To perform continuous process analysis, forecast and optimization, a case study in Denmark is elaborated In particular, an IoT device which captures point cloud during real- time operational monitoring plays a major role in sustaining the loop between the physical and cyber models The data gathered from IoT device is constantly synchronized to the cloud storage system, which serves as a repository The server then converts those data into the appropriate forms
Most notably, this updated data may be transferred to the virtual environment which is useful for conducting knowledge discovery for tactical decision making and automatically creating the cyber model linked with physical entities In other words, these digital duplicates are significantly more valuable thanks to the combined effects of process mining algorithms and the IoT Here, the cyber model is constructed in 4D visualization format with the same level of accuracy
Moreover, two data-mining approaches included in the cyber environment are the process mining algorithms such as fuzzy miner and ARIMA (1, 1, 1) predictive model To enable efficient communication between the physical and cyber environments, it thoroughly investigates massive record information from both present and future viewpoints Firstly, it is simple to identify impediments in the current process that are generating delays Secondly, it is possible to forecast how many activities could be completed These forecasts of future workload can result in early risk alerts and performance evaluation for optimization purposes As a result, recommendations can be generated dynamically to control the physical progress, which can even adapt to modifications made on site Hence, with workflows, workloads and labor forces well- organized, managers may create more sensible timetables with the goal of enhancing cooperation and operational efficiency in the real-world execution progress as soon as possible
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ANNEX A: Construction schedule of case study
The construction schedule of the case study was illustrated by Synchro 4D and was officially published on: https://1drv.ms/f/s!AsYjug0WmiBOnO8FftXELgwHJRw2wg?e=hartDO
* Notice that this documentation is published in the author’s personal repository and may be deleted or relocated to different locations as time passes.
ANNEX B: Workflow of event-log-generation
There are likely multiple methods for creating event log from IFC An overview of event log service employed algorithm may be found in this section The referenced algorithm utilized in the service’s source code which is published on: https://github.com/opensourceBIM/DemoPlugins/blob/master/DemoPlugins/src/org/bims erver/demoplugins/service/EventLogService.java
* Notice that this source code is published on an open platform and may be deleted or relocated to different locations by repository’s creator or by any contributors as time passes
The users will be asked to provide the material and the location of object type while utilizing this tool The material data and object type parameters have been determined and set out in this algorithm as below:
3 Create spreadsheet with columns: a GUID b IFC Class c Object Type d Material e Resource f Task ID g Task Name h Task Start i Task Finish
4 Collect all objects in IFC according to its GUID
6 Get GUID1 export to column named GUID in spreadsheet
7 Find GUID1 a Search: IFCRELDEFINESBYPROPERTIES b Get #number ifcPropertySetDefinition
8 Find #number ifcPropertySetDefinition a Search IFCPROPERTYSET b Get ‘HasProperties’
9 Find all ‘HasProperties’ a Object filter i Get IFCLABEL ii Fill in column IFC Class b [RevitProperties]Layer i Get IFCLABEL ii Fill in column Object Type c [RevitProperties]Building material i Get IFCLABEL ii Fill in column Material d Name i Take IFCLABEL ii Fill in column Resource
10 Find GUID1 a Search IFCRELASSIGNTOPROCESS b Get #number RelatingProcessSelect i Search IFC Task
2 Fill in column Task ID
4 Fill in column Task Name ii Get #number IFC Task c Find #number IFC Task i Search IFCRELASSIGNTASKS ii Get #number TimeForTasks d Find #number TimeForTasks i Search IFCSCHEDULETIMECONTROL ii Get #number ScheduleStart
1 Find #number ScheduleStart a Search IFCDATEANDTIME b Get #number DateComponent
2 Find #number DateComponent a Search IFCCALENDARDATE b Get DayComponent,MnthComponent,YearComponent c Fill in column Task Start iii Get #number ScheduleFinish
1 Find #number ScheduleFinish a Search IFCDATEANDTIME b Get #number DateComponent
2 Find #number DateComponent a Search IFCCALENDARDATE b Get DayComponent,MnthComponent,YearComponent c Fill in column Task Finish e For every activity based on GUID1 object, generate empty row and insert data into the relating column f When accomplishing get and set information of GUID1, continue the process as illustrated earlier for the remaining objects g Rearrange output data based on TaskStart time h Finish.
ANNEX C: Detailed illustration of process map
The process map or just workflow of each activity, or the interconnection among workers of the case study was demonstrated by Disco by Fluxicon and was officially published on: https://1drv.ms/f/s!AsYjug0WmiBOnO8COcraRQBXxapZnw?e=7G8Z0e
* Notice that this documentation is published in the author’s personal repository and may be deleted or relocated to different locations as time passes.