Kết hợp bim và thuật toán tiến hóa đa mục tiêu (evolution algorith ea) nhằm tổ chức mặt bằng công trường và quản lý an toàn lao động trong dự án xây dựng

ĐẠI HỌC QUỐC GIA TP.HCM TRƯỜNG ĐẠI HỌC BÁCH KHOA HUỲNH PHÚ HẢI KẾT HỢP BIM VÀ THUẬT TOÁN TIẾN HÓA ĐA MỤC TIÊU (EVOLUTION ALGORITH – EA) NHẰM TỔ CHỨC MẶT BẰNG CƠNG TRƯỜNG VÀ QUẢN LÝ AN TỒN LAO ĐỘNG TRONG DỰ ÁN XÂY DỰNG Chuyên ngành Mã số : QUẢN LÝ XÂY DỰNG : 8580302 LUẬN VĂN THẠC SĨ TP HỒ CHÍ MINH, tháng 07 năm 2023 CƠNG TRÌNH ĐƯỢC HỒN THÀNH TẠI TRƯỜNG ĐẠI HỌC BACH KHOA – ĐHQG -HCM Cán hướng dẫn khoa học 1: PGS.TS TRẦN ĐỨC HỌC Chữ ký: Cán hướng dẫn khoa học 2: TS NGUYỄN ANH THƯ Chữ ký: Cán chấm nhận xét 1: TS LÊ HOÀI LONG Chữ ký: Cán chấm nhận xét 2: TS NGUYỄN VĂN TIẾP Chữ ký: Luận văn thạc sĩ bảo vệ Trường Đại học Bách Khoa, ĐHQG Tp.HCM ngày 13 tháng 07 năm 2023 Thành phần hội đồng đánh giá Luận văn thạc sĩ bao gồm: Chủ tịch hội đồng: PGS.TS Lương Đức Long Thư ký hội đồng: PGS.TS Đỗ Tiến Sỹ Ủy viên phản biện 1: TS Lê Hoài Long Ủy viên phản biện 2: TS Nguyễn Văn Tiếp Ủy viên hội đồng: TS Đặng Ngọc Châu Xác nhận Chủ tịch Hội đồng đánh giá Luận văn Trưởng Khoa quản lý chuyên ngành sau luận văn sửa chữa (nếu có) CHỦ TỊCH HỘI ĐỒNG TRƯỞNG KHOA KỸ THUẬT XÂY DỰNG ĐẠI HỌC QUỐC GIA TP.HCM TRƯỜNG ĐẠI HỌC BÁCH KHOA CỘNG HÒA XÃ HỘI CHỦ NGHĨA VIỆT NAM Độc lập – Tự – Hạnh phúc NHIỆM VỤ LUẬN VĂN THẠC SĨ Họ tên học viên: HUỲNH PHÚ HẢI Ngày, tháng, năm sinh: 24/03/1997 Chuyên ngành: Quản lý xây dựng I TÊN ĐỀ TÀI: MSHV: 2170248 Nơi sinh: Tp Hồ Chí Minh KẾT HỢP BIM VÀ THUẬT TỐN TIẾN HÓA ĐA MỤC TIÊU (EVOLUTION ALGORITH – EA) NHẰM TỔ CHỨC MẶT BẰNG CƠNG TRƯỜNG VÀ QUẢN LÝ AN TỒN LAO ĐỘNG TRONG DỰ ÁN XÂY DỰNG INTEGRATING BUILDING INFORMATION MODELING (BIM) WITH A MULTI-OBJECTIVE EVOLUTION ALGORITHM (EA) TO OPTIMIZE CONSTRUCTION SITE LAYOUT AND MANAGE LABOR SAFETY IN CONSTRUCTION PROJECTS II NHIỆM VỤ VÀ NỘI DUNG: 1.Tìm hiểu thuật toán EAs bao gồm: tối ưu bầy đàn (PSO) thuật toán di truyền (NSGA_II) 2.Xác định giải hàm mục tiêu tốn bố trí sở vật chất an toàn lao động trường hợp rơi vật tư trình vận chuyển 3.Kết hợp BIM thuật tốn EAs, tạo cơng cụ giúp tối ưu hố mặt cơng trường quản lý an toàn lao động III NGÀY GIAO NHIỄM VỤ: 06/02/2023 IV NGÀY HOÀN THÀNH NHIỆM VỤ: 12/06/2023 V CÁN BỘ HƯỚNG DẪN: PGS.TS TRẦN ĐỨC HỌC TS NGUYỄN ANH THƯ Tp Hồ Chí Minh, ngày 12 tháng 06 năm 2023 CÁN BỘ HƯỚNG DẪN CÁN BỘ HƯỚNG DẪN CHỦ NHIỆM BỘ MÔN PGS.TS TRẦN ĐỨC HỌC TS NGUYỄN ANH THƯ TS LÊ HOÀI LONG TRƯỞNG KHOA KỸ THUẬT XÂY DỰNG i LỜI CẢM ƠN Tôi xin gửi lời cảm ơn sâu sắc đến thầy cô hướng dẫn, thầy PGS.TS Trần Đức Học TS Nguyễn Anh Thư, hướng dẫn hỗ trợ quý báu họ suốt trình thực luận văn thạc sĩ tơi "KẾT HỢP BIM VÀ THUẬT TỐN TIẾN HÓA ĐA MỤC TIÊU (EVOLUTION ALGORITHM - EA) NHẰM TỔ CHỨC MẶT BẰNG CÔNG TRƯỜNG VÀ QUẢN LÝ AN TOÀN LAO ĐỘNG TRONG DỰ ÁN XÂY DỰNG." Kiến thức nhận biết họ đóng vai trị quan trọng việc hình thành nghiên cứu này, khích lệ họ thúc đẩy tơi nỗ lực Tơi thật biết ơn kiên nhẫn, hiểu biết hướng dẫn thầy cô Cùng với tơi muốn gửi lời cảm ơn đến Trường Đại học Bách Khoa, Đại học Quốc gia, cung cấp cho môi trường học tập tuyệt vời hội để thực nghiên cứu Tôi muốn gửi lời cảm ơn đến khoa Quản lý Xây dựng tạo môi trường học thuật sáng tạo, phịng thí nghiệm mơ thơng tin cơng trình BIM (BIMLab) đến bạn đồng niên tơi đồng lịng thảo luận trí tuệ Cuộc hành trình khơng giống khơng có người Cuối cùng, tơi muốn dành cơng trình cho gia đình tơi tình u hỗ trợ khơng ngừng nghỉ họ suốt q trình học tập Niềm tin họ vào khả nguồn sức mạnh cảm hứng không ngừng Và lần cảm ơn tất người phần hành trình Tp Hồ Chí Minh, ngày 08 tháng 02 năm 2023 Huỳnh Phú Hải Master Student: Huynh Phu Hai Student Code: 2170248 ii TÓM TẮT Bước khởi đầu quan trọng q trình thi cơng xây dựng cơng trình lập kế hoạch bố trí mặt cơng trường Thành cơng bước góp phần đẩy nhanh tiến độ dự án Từ vấn đề bố trí mặt công trường xây dung ý, có nhiều phương pháp giải từ trực quan dựa kinh nghiệm sử dụng thuật toán máy tính đại Tuy nhiên, việc tìm giải pháp tối ưu cho toán thách thức lớn với nhà quản lý dự án dự án ngày phức tạp có nhiều mục tiêu khác cần cân Với khả kết hợp mơ hình thơng tin (BIM) thuật tốn tiến hoá tối ưu (EAs) coi xu hướng tất yếu tương lai ngành xây dựng nhằm giải tốn tối ưu cơng trình xây dựng BIM cung cấp khả mơ hình hóa thơng tin chi tiết, xác cơng trình Trong đó, EAs với thuật tốn tối ưu hóa tiên tiến xử lý tốn kinh doanh phức tạp, đưa giải pháp tối ưu thời gian thực Trong nghiên cứu này, phát triển mơ hình tối ưu hóa bố trí mặt cơng trường nhằm mục đích giảm rủi ro luồng hoạt động tối ưu hố bình đồ cơng trường phát triển Đề tài nghiên cứu thuật tốn tối ưu hóa tiến hóa PSO NSGA-II, sau kết hợp với BIM để xây dựng mơ hình tốn học cơng cụ hỗ trợ định cho tốn tổ chức mặt cơng trường Master Student: Huynh Phu Hai Student Code: 2170248 ii ABSTRACT The most important initial step in the construction process of a project is planning the layout of the construction site The success of this step contributes to accelerating the project's progress Thus, the layout arrangement in the construction site has been given attention, with various solutions proposed, ranging from intuitive approaches based on experience to the use of modern computer algorithms However, finding an optimal solution for this problem remains a significant challenge for project managers due to the increasing complexity of projects and the need to balance various objectives Combining Building Information Modeling (BIM) and Evolutionary Algorithms (EAs) is considered an inevitable trend in the future of the construction industry to address optimization problems in construction projects BIM provides the ability to model detailed, accurate information about the project, while EAs with advanced optimization algorithms can handle complex business problems, offering optimal solutions in real-time In this study, a layout optimization model for construction sites is developed with the aim of reducing the risks between activity flows and optimizing the site's layout The research topic investigates evolutionary optimization algorithms such as PSO and NSGA-II, and then combines them with BIM to construct a mathematical model and decision support tool for the construction site layout organization problem Master Student: Huynh Phu Hai Student Code: 2170248 iii LỜI CAM ĐOAN Tôi xin cam đoan luận văn tơi thực hướng dẫn PGS.TS Trần Đức Học TS Nguyễn Anh Thư Các kết luận văn thật chưa công bố nghiên cứu khác Tôi xin chịu trách nhiệm cơng việc thực Tp Hồ Chí Minh, ngày 08 tháng 02 năm 2023 Huỳnh Phú Hải Master Student: Huynh Phu Hai Student Code: 2170248 iv TABLE OF CONTENTS TABLE OF CONTENTS iv LIST OF ABBREVIATIONS vi LIST OF TABLES vii LIST OF FIGURES viii LIST OF EQUATION .x CHAPTER 1: INTRODUCTION .1 1.1 PROBLEM STATEMENT 1.1.1 Construction site layout 1.1.2 Construction site safety 1.1.3 Evolution Algorithm - EA 1.1.4 EA Assessment: 10 1.1.5 Building Information Modelling 12 1.1.6 Combining BIM and EA 18 1.2 PROBLEM STATEMENT 18 1.2.1 Thesis related question 18 1.3 RESEARCH OBJECTIVES 19 1.3.1 Research Objective 19 1.3.2 Research Scope 19 1.3.3 Research contributions 19 CHAPTER 2: LITTERATURE REVIEW 21 2.1 CONSTRUCTION SITE LAYOUT AND CONSTRUCTION SAFETY 21 2.1.1 Construction site layout planning .21 2.1.2 Construction safety prevention[41]: 27 2.2 MULTI – OBJECTIVE EVOLUTION ALGORITHM .28 Master Student: Huynh Phu Hai Student Code: 2170248 v 2.2.1 Multi – Objective optimization - MOO: .28 2.2.2 Step to analyze by EA 31 2.2.3 Particle Swarm Optimization (PSO) inspiration 33 2.2.4 Particle Swarm Optimization (PSO) .34 2.3 RELATED STUDIES 37 2.3.1 Metaheuristics Algorithm 37 2.3.2 Evolutional Algorithm 39 2.3.3 BIM in construction site layout 40 CHAPTER 3: METHODOLOGY 42 3.1 THESIS WORKFLOW .42 CHAPTER 4: CASE STUDY 57 4.1 OBJECTIVE FUNCTION AND PARAMETER .57 4.1.1 Project Parameter 57 4.1.2 PSO in MOO: .64 4.1.3 Optimize result: 64 4.1.4 MOPSO comparison with NSGA II 74 4.2 RESEARCH FINDDING 82 4.2.1 EA – BIM Intergration .82 CHAPTER 5: CONCLUSIONS .85 5.1 Conclusion: 85 5.2 Limitation: 86 5.3 Future research: 86 REFFENCES 88 Master Student: Huynh Phu Hai Student Code: 2170248 vi LIST OF ABBREVIATIONS BIM Mơ hình thơng tin cơng trình MOPSO Thuật tốn đa mục tiêu bầy đàn Multi Objective Particle Swarm Optimization NSGA II Thuật tốn di truyền khơng None Dominated Sorting Genetic Algorithm vượt trội OSH An Toàn Lao động Occupational Safety and Health IT Công nghệ thông tin Information Technology EA Thuật Tốn tiến hóa Evolution Algorithm CSLP Sắp xếp bình đồ công trường Construction Site Layout Planning API Giao diện lập trình ứng dụng Application Programming Interface CSC Hệ số an toàn cẩu trục Crane Safety Criterion MCDC Ra định đa tiêu chí Multiple Criteria Decision Making Master Student: Huynh Phu Hai Building Information Modelling Student Code: 2170248 Table 4-16: Descriptive Statistics MOPSO NSGA-II Min Distance 594.63 563.81 Max Distance 1258.2 1366.7 Min CSC 8.06 8.72 Max CSC 11.53 11.34 Mean Distance 930.94 922.06 9.76 10.21 170.81 204.52 0.82 0.74 Mean CSC Std Dev Distance Std Dev CSC The table provided compares two optimization algorithms: MOPSO (MultiObjective Particle Swarm Optimization) and NSGA-II (Non-dominated Sorting Genetic Algorithm II) The comparison is based on several metrics, including minimum and maximum distance, minimum and maximum CSC, mean distance, mean CSC, and the standard deviation of both distance and CSC NSGA-II outperforms MOPSO in terms of minimum distance (563.81 vs 594.63), indicating that NSGA-II found a solution with a shorter minimum distance NSGA-II also has a superior minimum CSC value (8.72 compared to MOPSO's 8.06), indicating that the worst CSC value obtained by NSGA-II was better than that of MOPSO Furthermore, NSGA-II has a higher mean CSC (10.21 compared to MOPSO's 9.76), implying that, on average, the solutions found by NSGA-II had better CSC values However, MOPSO found a solution with a shorter maximum distance (1258.2 compared to NSGA-II's 1366.7), and it also achieved a higher maximum CSC (11.53 compared to NSGA-II's 11.34), suggesting that the best CSC value obtained by MOPSO was better than that of NSGA-II In terms of variability, NSGA-II had a higher standard deviation for distance (204.52 vs 170.81), indicating greater variability in the solutions it found However, MOPSO had a higher standard deviation for CSC (0.82 compared to NSGA-II's 0.74), suggesting greater variability in its CSC solutions Master Student: Huynh Phu Hai - 2170248 Pages 80 | 97 While NSGA-II found solutions with a shorter minimum distance, better minimum and average CSC values, these solutions had a larger maximum distance and greater variability in terms of distance On the other hand, MOPSO found solutions with a shorter maximum distance and better maximum CSC, but these solutions exhibited a greater variability in terms of CSC The choice between these two algorithms would depend on the specific requirements and constraints of the optimization problem being solved When comparing the performance of the Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA II) based on the provided data, several observations can be made In terms of solution quality, both algorithms have found solutions with a wide range of objective function values However, NSGA II seems to have found solutions with higher values for the maximum CSC objective, indicating that it might be more effective at optimizing this objective On the other hand, MOPSO has found solutions with lower values for the minimum ∑ 𝑑𝑑𝑖𝑖𝑖𝑖 objective, suggesting that it might be more effective at optimizing this objective Therefore, the choice between these two algorithms might depend on which objective is more important for the specific problem at hand Regarding the diversity of solutions, both algorithms have found a wide range of solutions, as indicated by the spread of the objective function values This suggests that both algorithms are effective at exploring the solution space and providing a diverse set of solutions However, a more detailed analysis would require calculating metrics like the spread or the hypervolume of the solution set As for the stability or reliability of the algorithms, there seems to be some variation in the solutions found by each algorithm across multiple runs This is indicated by the range of the objective function values for the solutions found by each algorithm However, without more information about the variation in the problem parameters or the initial conditions, it's difficult to make a definitive judgment about the stability or reliability of the algorithms Master Student: Huynh Phu Hai - 2170248 Pages 81 | 97 In conclusion, both MOPSO and NSGA II have their strengths and weaknesses, and the choice between them would depend on the specific requirements of the problem For problems where the maximum CSC objective is more important, NSGA II might be the better choice, while for problems where the minimum ∑ 𝑑𝑑𝑖𝑖𝑖𝑖 objective is more important, MOPSO might be more effective Both algorithms provide a diverse set of solutions, but further analysis would be required to assess their stability or reliability 4.2 RESEARCH FINDDING 4.2.1 EA – BIM Intergration The EA-BIM integration approach leverages the 3D visualization and information management capabilities of BIM, alongside the multi-objective optimization capacity of EA This integration resulted in a holistic and dynamic optimization of the construction site layout Comparing to Ning, et al [55], Ning, et al [49], utilizing with conventional method which relies heavily on the expertise and judgment of the project manager or site engineer Although expert judgment benefits from practical experience and professional intuition, it lacks the comprehensive data analysis and optimization capabilities found in computational models Conventional methods can be timeconsuming and potentially less cost-effective, as they not optimize the sequence of construction activities and the allocation of resources Furthermore, while safety is always a priority in construction, the expert judgment method may overlook some hidden safety risks that can be identified through more systematic and data-driven approaches Also, the spatial layout under the conventional method may not be optimal, as it might not consider all the complex interrelations between different elements and activities on the site Master Student: Huynh Phu Hai - 2170248 Pages 82 | 97 Figure 4-26: Schematic layout drawing by Ning, et al [55] On the other hand, the EA-BIM approach, which combines the 3D visualization and information management capabilities of BIM with the powerful optimization capabilities of EA, results in a robust and dynamic method for construction site layout planning and safety management This method can significantly reduce construction time and overall project cost compared to the conventional method It also allows for advanced risk management by integrating safety parameters into the EA, thus identifying, and mitigating potential hazards more effectively Moreover, the EA-BIM approach can optimize the placement and movement of resources on the construction site, reducing congestion and improving workflow, which is a level of efficient spatial planning difficult to achieve using the conventional method as shonw in Figure 4-1 Master Student: Huynh Phu Hai - 2170248 Pages 83 | 97 Figure 4-27: Automation in Construction Layout Master Student: Huynh Phu Hai - 2170248 Pages 84 | 97 CHAPTER 5: CONCLUSIONS 5.1 Conclusion: A new model has been developed to optimize both construction safety and resource movement on site during the site layout planning process The model leverages building information modeling to search for and generate optimal site layout plans that balance these two goals while satisfying all practical constraints The model was developed in three phases: Defining the decision variables and objectives to optimize This includes identifying what can be changed in the site layout and what needs to be optimized Identifying and defining all the practical constraints that must be satisfied These include things like budget, available space, etc Implementing the model using a multiobjective evolutionary algorithm This algorithm can optimize multiple objectives at once (safety and resource movement) while satisfying the defined constraints The model includes three new ways to quantify and optimize construction safety using BIM: Choosing safe locations for temporary facilities around cranes by analyzing the site in BIM Using BIM to ensure hazardous materials are adequately separated based on which combinations could create unsafe conditions Reducing intersections between high-traffic resource routes in BIM to minimize accidents and collisions An example was used to demonstrate how the model works and its capabilities The results showed that the model can generate optimal trade-offs between construction safety and resource movement, which should benefit construction planners and lead to improved safety and schedule performance when using BIM In summary, the new model leverages BIM to simultaneously optimize construction safety and resource movement during site layout planning while satisfying all practical Master Student: Huynh Phu Hai - 2170248 Pages 85 | 97 constraints An example showed that the model can generate good trade-offs between these two goals to benefit planning and performance 5.2 Limitation: Some additional limitations of using BIM and EAs for construction site layout and safety optimization include: Interoperability issues: Integrating BIM with EAs requires exchange of model data between different software platforms which can be challenging due to interoperability issues Standard data formats and APIs need to be used to enable this integration Increased complexity: Combining BIM and EAs leads to a more complex computational problem with larger solution spaces and many constraints to satisfy This can reduce the performance and scalability of optimization algorithms Reliance on BIM model: The optimization results depend heavily on the availability and accuracy of information in the BIM model Any missing or incorrect information in the model can compromise the optimization outcomes Compatibility with existing tools: Integrated BIM and EA systems for construction site layout may require developing customized tools These tools need to be compatible with and complementary to existing design and planning tools used in practice This requires close collaboration with industry partners Lack of standards: There is a lack of standards for how to apply BIM and EAs for construction site layout optimization This can lead to inconsistencies, inefficiencies and limited reuse of efforts across projects Developing best practices and guidelines can help address this limitation 5.3 Future research: The integration of Building Information Modeling (BIM) and multi-objective optimization algorithms is an emerging field of research that can significantly enhance construction management BIM provides a digital representation of construction projects that can be used to systematically extract objectives and constraints for Master Student: Huynh Phu Hai - 2170248 Pages 86 | 97 optimization of site layout, safety, productivity, and resources Optimization algorithms such as Particle Swarm Optimization and Genetic Algorithms can then generate optimal trade-off solutions that satisfy multiple objectives while meeting constraints By linking BIM and optimization techniques, construction projects can be optimized in an automated and holistic fashion Site layout can be optimized to maximize space utilization and minimize 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Công ty The BIM Factory Master student: Huynh Phu Hai Nghiên cứu viên Portcoast Kỹ sư Thực tập Student code: 2170248