Combining building information modeling (bim) and choosing by advantages (cba) method to select design construction solutions toward sustainable construction in viet nam

VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY DO DUY LINH COMBINING BUILDING INFORMATION MODELING (BIM) AND CHOOSING BY ADVANTAGES (CBA) METHOD TO SELECT DESIGN-CONSTRUCTION SOLUTIONS TOWARD SUSTAINABLE CONSTRUCTION IN VIETNAM Major: CONSTRUCTION MANAGEMENT Major code: 8580302 MASTER’S THESIS HO CHI MINH CITY, July 2023 THIS THESIS IS COMPLETED AT HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY – VNU-HCM Supervisor: Assoc Prof Luong Duc Long Examiner 1: Assoc Prof Tran Duc Hoc Examiner 2: Dr Chu Viet Cuong This master’s thesis is defended at HCM City University of Technology,VNUHCM City on 13th July 2023 Master’s Thesis Committee: Dr Nguyen Anh Thu - Chairman Dr Huynh Nhat Minh - Secretary Assoc Prof Tran Duc Hoc - Reviewer Dr Chu Viet Cuong - Reviewer Dr Dang Ngoc Chau - Member Approval of the Chairman of Master’s Thesis Committee and Dean of Faculty of Civil Engineering after the thesis being corrected (If any) CHAIRMAN OF THESIS COMMITTEE HEAD OF FACULTY OF CIVIL ENGINEERING Dr Nguyen Anh Thu i VIETNAM NATIONAL UNIVERSITY - HO CHI MINH CITY HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom - Happiness THE TASK SHEET OF MASTER’S THESIS Full name: Do Duy Linh 2170311 Date of birth: 21/10/1993 Major: Construction Management Student ID: Place of birth: Khanh Hoa Major ID: 8580302 I THESIS TITLE (In Vietnamese): Kết hợp mơ hình thông tin xây dựng (BIM) phương pháp lựa chọn theo ưu điểm (CBA) để lựa chọn giải pháp thiết kế thi công hướng đến xây dựng bền vững Việt Nam II THESIS TITLE (In English): Combining Building Information Modeling (BIM) and Choosing By Advantages (CBA) method to select Design-Construction solutions toward sustainable construction in Vietnam III TASKS AND CONTENTS: Building a predictive model of energy consumption for buildings using Building Information Modeling (BIM), Building Energy Modeling (BEM) and Machine Learning; Determining the factors affecting the decision to choose the design and construction option towards sustainable construction and the importance level of those factors; Developing a method for choosing optimal design and construction options by Choosing By Advantages (CBA) method IV THESIS START DAY: March 2023 V THESIS COMPLETION DAY: 10th June 2023 VI SUPERVISOR: Assoc Prof Luong Duc Long Ho Chi Minh City, date ……… SUPERVISOR (Full name and signature) HEAD OF DEPARTMENT (Full name and signature) Assoc Prof Luong Duc Long DEAN OF FACULTY OF CIVIL ENGINEERING (Full name and signature) Note: Student must pin this task sheet as the first page of the Master’s Thesis booklet ii ACKNOWLEDGEMENTS In order to complete this thesis, first of all, I would like to express my sincerest thanks to Associate Professor, Dr Luong Duc Long, who has enthusiastically guided, oriented and imparted valuable experiences to me during the process of making this thesis Next, I would like to thank the teachers of the Department of Construction Management, Faculty of Civil Engineering for their dedication in teaching and imparting specialized knowledge during their study at the school I also would like to thank the group of experts, colleagues and friends who have given their comments, participated in surveys as well as shared valuable knowledge and experiences and supported me in the process perform this research Finally, I would like to thank my family and relatives for always supporting and encouraging me during my study and thesis completion In the process of conducting research, errors cannot be avoided Therefore, I look forward to receiving your understanding and comments to improve Sincerely, Ho Chi Minh City, 31st July 2023 DO DUY LINH iii ABSTRACT Energy efficiency buildings are becoming more and more important in order to address the current energy problem and advance in line with the sustainable trend of the construction industry The potential for energy savings in buildings is relatively large when design-construction options are used to increase energy efficiency This has many tangible advantages for the socio-economy, including increased energy efficiency, improved quality of life, and a favorable effect on the environment Finding a designconstruction alternative for the building that is truly beneficial in terms of both technical and economic criteria, however, is proving to be very challenging The goal of this thesis is to help solve this issue To accomplish this task, Building Information Modeling (BIM) and software in this ecosystem are not only used for modeling (3D), time simulation (4D), quantity measurement (5D) but also for energy consumption simulation (6D) This thesis focuses on using BIM-based energy simulation software to simulate energy consumption for various types of building design and combining BIM 6D with the Choosing by Advantages (CBA) method, a multi-criteria decision-making method that has been widely applied in selecting design options, materials, and contractors related to sustainable construction In addition, besides using energy software to simulate and generate a data set of energy consumption for the building, this thesis introduces a method to predict energy consumption by using Learning Machine based on output datasets from the generating process Keywords: Building Information Modeling (BIM), BIM 6D, Choosing by Advantages (CBA), sustainable construction, Machine Learning iv TĨM TẮT LUẬN VĂN THẠC SĨ Các tịa nhà sử dụng lượng hiệu ngày trở nên quan trọng nhằm giải vấn đề lượng tiến tới phù hợp với xu hướng phát triển bền vững ngành xây dựng Tiềm tiết kiệm lượng tòa nhà tương đối lớn phương án thiết kế-xây dựng sử dụng để tăng hiệu sử dụng lượng Điều mang lại nhiều lợi ích hữu hình cho kinh tế xã hội, bao gồm tăng hiệu sử dụng lượng, cải thiện chất lượng sống tác động tích cực đến mơi trường Tuy nhiên, việc tìm kiếm phương án thiết kế-xây dựng cho tịa nhà thực có lợi tiêu chí kỹ thuật kinh tế tỏ khó khăn Mục tiêu luận án giúp giải vấn đề Để thực nhiệm vụ này, Mơ hình thơng tin xây dựng (BIM) phần mềm hệ sinh thái không sử dụng để mơ hình hóa (3D), mơ thời gian (4D), đo lường khối lượng (5D) mà cịn dùng để mơ tiêu thụ lượng (6D) Luận văn tập trung vào việc sử dụng phần mềm mô lượng BIM để mô mức tiêu thụ lượng cho loại thiết kế cơng trình kết hợp BIM 6D với phương pháp Lựa chọn theo Ưu điểm (CBA), phương pháp định đa tiêu chí áp dụng rộng rãi việc lựa chọn thiết kế, vật liệu nhà thầu liên quan đến xây dựng bền vững Ngoài ra, bên cạnh việc sử dụng phần mềm lượng để mô tạo liệu tiêu thụ lượng cho tòa nhà, luận văn giới thiệu phương pháp dự báo mức tiêu thụ lượng cách sử dụng Learning Machine dựa liệu đầu từ q trình mơ lượng Từ khóa: Building Information Modeling (BIM), BIM 6D, Choosing by Advantages (CBA), sustainable construction, Machine Learning v THE COMMITMENT OF THE THESIS’ AUTHOR The undersigned below: Student full name: DO DUY LINH Student ID: 2170311 Place and date of born: Khanh Hoa, Vietnam, 21st October 1993 Address: Di An City, Binh Duong With this declaration, the author finishes his master’s thesis entitled “COMBINING BUILDING INFORMATION MODELING (BIM) AND CHOOSING BY ADVANTAGES (CBA) METHOD TO SELECT DESIGNCONSTRUCTION SOLUTIONS TOWARD SUSTAINABLE CONSTRUCTION IN VIETNAM” under the advisor's supervision All works, ideas, and materials that was gain from other references have been cited correctly Ho Chi Minh City, 31st July 2023 DO DUY LINH vi TABLE OF CONTENTS CHAPTER 1: GENERAL INTRODUCTION 1.1 Research problem: 1.2 Objectives of the topic 1.3 Scope of study: 1.4 Scientific and practical significances 1.4.1 Practical significances 1.4.2 Scientific significances CHAPTER 2: THEORETICAL BASIC AND RELATED RESEARCH 2.1 Definitions and concepts 2.1.1 Sustainable Development 2.1.2 Energy-Efficient building 2.1.3 Building Information Modeling (BIM) 2.1.4 Multiple-criteria decision-making (MCDM) methods: 2.1.5 Choosing by Advantages (CBA) methods: 2.1.6 Artificial Intelligence and Machine Learning 11 2.2 Relative research 14 CHAPTER 3: METHODOLOGY 18 3.1 Research procedure 18 3.2 Collecting data 20 3.3 Data Analysis Tools 22 3.4 Energy simulation software: DesignBuilder 25 3.5 Random Forest Algorithm 26 3.5.1 General 26 3.5.2 The process of building a Random Forest model 27 vii 3.5.3 The advantages of Random Forest Algorithm 27 3.5.4 Evaluate the accuracy of the RF model 29 3.6 Building a model to predict energy consumption 30 3.7 Software used in the study 30 CHAPTER 4: DETERMINING FACTORS - DATA COLLECTION AND ANALYSIS 32 4.1 Determining the factors 32 4.2 Questionnaire Design 33 4.3 Survey results 33 4.4 Analyzing the characteristics of the study sample 33 4.5 Testing the reliability of the scale 36 4.6 Ranking factors 37 4.7 One sample T-Test 38 4.8 Multi-sample testing 39 CHAPTER 5: BUILDING A MODEL OF ENERGY CONSUMPTION 46 5.1 Simulation of building energy consumption using DesignBuilder 46 5.1.1 Procedure of building an energy simulation model 46 5.1.2 Model information 47 5.1.3 Defining design variables and input data to DesignBuilder 47 5.1.4 Simulation results: 53 5.2 Simulation of building energy consumption using Random Forest Algorithm by Python Programming Language 54 5.2.1 Procedure of creating an energy prediction model 54 5.2.2 Data and parameter for RF model 56 5.2.3 Checking results of prediction model 57 5.3 Optimal design variables 60 viii 5.3.1 Pareto frontier 60 5.3.2 Pareto frontier result from DesignBuilder 60 5.3.3 Pareto frontier result from Prediction model and comparison 61 CHAPTER 6: DECISION ON DESIGN OPTIONS BY CBA METHOD 63 6.1 CBA procedure and result 63 6.1.1 Identifying alternatives 63 6.1.2 Defining factors 63 6.1.3 Defining criteria for each factor 64 6.1.4 Describing the attributes of each alternative 65 6.1.5 Deciding advantages of alternatives & Decide the importance of advantages 73 6.1.6 Evaluating cost data 75 6.2 Finding the most optimal alternative by pareto frontier 80 6.3 Method of using aggregate values without units of measurement to rank alternatives 81 CHAPTER 7: CONCLUSION AND SUGGESTION 84 7.1 Conclusion 84 7.2 Suggestion 85 REFERENCE 87 81 6.3 Method of using aggregate values without units of measurement to rank alternatives In order to find the best options between alternatives, the author proposes to apply the Method of using aggregate values without units of measurement This method has some advantages as below: - Calculating all indicators with different units of measurement into a single composite indicator to rank the plan; - Multiple criteria can be included in the comparison; - Taking into account the importance of each indicator Method of using aggregate values without units of measurement has several primary steps as below: - Select criteria to include in comparison - Determine the direction and make the targets in the same direction - Determine whether the direction of the objective function is a maximum or a minimum - Co-direction of the criteria: any criteria that is in the opposite direction to the objective function must take their reciprocal numbers for comparison - Determine the weight of each criteria - Eliminate the units of measure of the criteria Currently, there are many methods to eliminate the measurement unit of the criteria The most popular is the Pattem method and the method of comparing each pair of criteria Pattem method is calculated by formulas as below: Pij  Cij *100 n C j 1 ij In this formula: Pij: values without units of criteria Cij (i: the criteria, j: the alternative, n: the number of alternatives); 82 Cij - values with units of criteria i, alternative j Determining the unitless sum of each indicator by formula: m m i 1 i 1 V j   S ij   PijWi In this formula: Vij - aggregate values without units of alternative j; Sij - aggregate values without units of criteria i of alternative j; Wi – weight of criteria i; Depending on the case that the value of the objective function is maximum or minimum, the value of Vj of the selected alternative will be maximum or minimum In this case, there are criteria: CBA score and Cost Because these criteria have opposite directions, we have to take reciprocal numbers of criteria for comparison Author chooses an objective function: maximum, so cost will be the criteria inverted because it has the opposite direction with CBA score and objective function About the weight of criteria, the author chooses options as below: CBA score = 0.6 and Cost = 0.4 CBA score = 0.4 and Cost = 0.6 CBA score = Cost = 0.5 These weight options will depend on the attitudes of the Decision maker in the actual situation but it should not be larger than 0.6 If any weight of criteria is larger than 0.6, the evaluation is markedly biased towards the higher weighted criteria and as a result, the value of the higher weighted criteria will completely dominate the assessment Table below perform the detail of Method of using aggregate values without units of measurement: 83 Table 6.11: Detail evaluation of Method of using aggregate values without units of measurement Alternatives Description Alt Alt 560 576 0.002703 0.002212 P1j value 49.30 50.70 P2j value 54.99 45.01 W1 = 0.6 W2 = 0.4 51.57 48.43 W1 = 0.4 W2 = 0.6 52.71 47.29 W1 = 0.5 W2 = 0.5 52.14 47.86 Criteria 1: CBA Score Criteria 2: Cost (inverted) Vij value As results from Table 6.11, for all attribute weight allocation options, Alternative is always the better choice 84 CHAPTER 7: CONCLUSION AND SUGGESTION 7.1 Conclusion The situation of energy shortage is becoming more and more serious, in addition to rapid urbanization and climate change Energy consumption becomes a major concern in construction becoming a potential important function in the strategy of saving and conserving energy Green building projects, energy efficient buildings with energy saving solutions are developed as a solution to this problem The thesis has accomplished the three initial objectives set out as: - Building a predictive model of energy consumption for buildings using Building Information Modeling (BIM), Building Energy Modeling (BEM) and Machine Learning; - Determining the factors affecting the decision to choose the design and construction option towards sustainable construction and the importance of those factors; - Developing a method for choosing design and construction options by Choosing By Advantages (CBA) method Based on the author's experience, experts' opinions and actual survey results, the author has identified 11 factors affecting the decision to choose the design and construction option towards sustainable construction and the importance of those factors These factors in the order of importance include: Total annual electricity usage Total annual discomfort hours Total annual CO2 emission Firefighting capacity Aesthetic aspect Impact to Structure Durability Impact to method statement and construction schedule Availability on market 10 Impact to operation and maintenance 11 Recycling capacity 85 An energy consumption prediction model is built by combining a physical model and an AI model Specifically, the simulation model is built using DesignBuilder software with the characteristics of a typical office, the values of the parameters used in the simulation process are referenced from technical documents, researches, etc previously for energy efficient buildings Parameters include: - Ratio of Window on Wall, - Building Orientation, - Heat transfer rate (U-value) of external Wall, - Heat transfer rate (U-value) of Glazing, - Heat transfer rate (U-value) of Roof, - Cooling setting point, - Cooling coefficient of performance Then, the simulation results are used to train and test the energy consumption prediction model using the RF algorithm Along with the energy consumption prediction model, the author also developed an algorithm to choose the optimal design options for energy consumption Finally, the study has built a process to select the most optimal design option among the results from the energy consumption simulation model by Choosing by Advantages (CBA) method Limitations of thesis: - The number of surveys is quite small, mainly acquaintances of the author - The parameters in the simulation model are limited to factors that can be quantified, while other factors also significantly affect the level of electricity energy efficiency of the building - The forecasting model is difficult to access to the majority of users because it needs to run on a professional programming language 7.2 Suggestion Based on the obtained results as well as the limitations of this study, the author recommends that the research topic on works towards sustainable construction needs more attention and investment Some future research directions: 86 - Expanding the scope of research to many other construction sites outside Ho Chi Minh City, more types of works such as apartment buildings, single houses, industrial houses - Building a predictive model that combines many other algorithms to create a model 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VIET NAM Dear Sir/Madam, My name is Do Duy Linh, a master student in Construction Management from University of Technology - Vietnam National University, Ho Chi Minh City Currently, I am doing my graduation thesis with the topic: “COMBINING BUILDING INFORMATION MODELING (BIM) AND CHOOSING BY ADVANTAGES (CBA) METHOD TO SELECT DESIGN-CONSTRUCTION SOLUTIONS TOWARD SUSTAINABLE CONSTRUCTION IN VIETNAM” The purpose of this survey questionnaire is to assess the influence of factors determining the selection of materials, design and construction options for green buildings, towards sustainable construction in the future In order to have an accurate database for research, I would like to ask you to spend a little time to contribute your comments, share your valuable experiences and assist me in answering the survey questions close The information you provide will be kept strictly confidential and used for research purposes only If you need more information, please contact the phone number or email address below: Name: Do Duy Linh Phone number: 0818 892 209 Email: Linh.do.imp21@hcmut.edu.vn Thank you very much 91 PART 1: INFORMATION OF THE PARTICIPANTS Name: ……………………… Email: ……………………… Organization: ☐Investor ☐Design Consultant ☐Project Manager ☐QS Consultant ☐Contractor Expertise: ☐ Project Manager ☐ Designer ☐ Supervisor ☐ Quantity Surveyor Experience in Construction field: ☐ Less than years ☐ 5-10 years ☐ More than 10 years Experience in Green Building projects: ☐Yes ☐No PART 2: SURVEYING FACTORS AFFECTING THE SELECTION OF GREEN BUILDING DESIGN – CONSTRUCTION TOWARD SUSTAINABLE CONSTRUCTION The influence of the factors is assessed on a scale of 0-100, divided into main levels for reference as follows: ≤20: Almost no effect 21-40: Little influence 41-60: Medium influence 61-80: Pretty much influenced 81-100: Very influential 92 Based on your experience, please enter a score from to 100 (eg 75) corresponding to the influence of the following factors: No A Factors Factors relating to energy consumption A1 Total annual electricity usage A2 Total annual CO2 emission A3 Total annual discomfort hours B Factors relating to designing features B1 Sound insulation capacity B2 Firefighting capacity B3 Aesthetic aspect B4 Impact to Structure B5 Durability B6 Impact to method statement and construction schedule B7 Impact to operation and maintenance B8 Availability in market B9 Recycling capacity Thank you very much for your contribution ! -***** - Score 93 APPENDIX 2: SURVEY RESULTS Sample Number Company Position Experience Exp In GB A1 A2 A3 B1 B2 B3 B4 B5 B6 B7 B8 B9 Project Manager Project Manager 5-10 years Yes 95 85 95 85 80 60 65 55 50 75 45 30 Contractor Supervisor 1-5 years Yes 100 95 90 85 75 55 65 70 60 80 30 35 Contractor Supervisor > 10 years Yes 80 100 80 70 80 80 80 80 60 80 90 90 Contractor Supervisor 1-5 years Yes 90 80 80 80 90 90 90 80 90 70 90 50 Contractor Supervisor > 10 years Yes 85 90 75 80 80 75 85 90 75 80 75 75 Design Consultant Designer > 10 years No 60 60 85 85 60 75 65 75 85 95 100 100 Design Consultant Designer > 10 years No 90 60 50 90 95 90 100 90 90 100 90 100 Project Manager Designer 5-10 years Yes 80 75 60 81 85 45 50 60 60 70 85 35 Client Supervisor 5-10 years Yes 75 70 80 90 95 80 90 90 95 95 60 60 10 Contractor Quantity Surveyor 1-5 years No 91 81 50 81 90 90 91 95 80 79 75 81 11 Contractor Supervisor 5-10 years Yes 95 77 92 81 65 64 52 55 47 44 35 21 12 QS Consultant Quantity Surveyor 5-10 years Yes 90 80 90 84 69 29 51 76 55 43 30 30 13 Project Manager Project Manager 5-10 years Yes 95 79 83 81 81 68 57 53 50 50 32 20 14 Project Manager Designer 5-10 years Yes 95 85 93 85 68 45 62 50 50 42 46 29 15 QS Consultant Quantity Surveyor 5-10 years Yes 97 80 80 76 73 52 56 78 74 55 50 28 16 Project Manager Designer 1-5 years Yes 80 83 84 82 60 48 65 70 57 46 45 32 17 Project Manager Designer 1-5 years Yes 80 85 90 80 77 75 53 78 50 45 35 20 18 Design Consultant Designer 5-10 years Yes 95 80 75 85 70 40 55 75 65 50 45 40 19 Design Consultant Designer 5-10 years Yes 95 80 75 85 70 35 55 80 65 50 45 40 20 Contractor Project Manager 5-10 years Yes 90 90 90 70 65 60 58 50 68 55 35 20 21 QS Consultant Quantity Surveyor 5-10 years Yes 100 80 90 80 60 25 60 60 40 40 25 40 22 Client Quantity Surveyor > 10 years Yes 95 75 80 75 65 25 60 72 68 55 25 30 23 Client Quantity Surveyor > 10 years Yes 90 75 80 73 65 20 70 70 75 50 35 25 24 QS Consultant Quantity Surveyor > 10 years Yes 95 90 88 72 70 60 60 75 73 45 50 25 25 Contractor Supervisor 5-10 years No 80 70 90 80 90 80 100 70 80 90 90 60 94 Sample Number 26 Company Position Experience Exp In GB A1 A2 A3 B1 B2 B3 B4 B5 B6 B7 B8 B9 Design Consultant Designer > 10 years Yes 80 85 90 78 75 52 60 78 55 50 40 30 27 Client Quantity Surveyor 5-10 years No 96 90 78 82 84 36 69 77 74 58 46 30 28 Contractor Quantity Surveyor > 10 years Yes 90 85 78 74 60 57 62 68 60 57 47 20 29 Contractor Quantity Surveyor > 10 years Yes 85 80 90 80 80 55 67 70 55 60 45 35 30 Client Designer > 10 years Yes 90 80 90 73 75 48 60 80 70 50 35 28 31 QS Consultant Quantity Surveyor 5-10 years No 85 85 75 70 79 30 70 50 55 45 45 20 32 Client Designer 5-10 years Yes 95 75 80 75 60 55 68 77 62 40 35 20 33 Client Quantity Surveyor > 10 years Yes 90 85 90 75 65 30 60 75 48 60 40 35 34 Client Designer 5-10 years No 90 80 95 75 75 40 65 65 70 50 50 20 35 Client Designer > 10 years Yes 95 82 90 80 75 30 70 65 75 40 38 25 36 Client Quantity Surveyor 5-10 years No 95 65 75 82 75 33 50 60 68 45 50 35 37 QS Consultant Quantity Surveyor > 10 years Yes 98 85 85 75 80 50 68 60 70 60 30 30 38 Contractor Designer > 10 years Yes 100 75 80 85 85 68 62 65 55 45 30 25 39 Project Manager Designer > 10 years Yes 85 90 80 75 78 25 65 70 72 45 38 40 40 Client Quantity Surveyor 5-10 years Yes 85 85 90 82 72 30 60 70 50 55 48 35 41 Client Quantity Surveyor > 10 years Yes 95 80 90 72 85 38 65 68 60 40 45 30 42 Client Quantity Surveyor > 10 years Yes 90 80 85 75 80 70 52 77 50 55 30 20 43 Client Project Manager > 10 years Yes 90 82 85 72 64 70 60 75 63 60 35 38 44 QS Consultant Quantity Surveyor > 10 years Yes 85 80 90 72 75 45 68 55 42 52 48 35 45 Design Consultant Designer 5-10 years Yes 100 80 90 75 70 75 70 68 45 60 45 25 46 Contractor Supervisor > 10 years Yes 95 87 90 75 85 52 68 78 63 50 35 30 47 Client Designer 5-10 years Yes 92 85 80 75 80 54 70 75 65 50 35 30 48 Design Consultant Designer > 10 years Yes 100 80 90 75 70 60 70 70 45 60 45 25 49 Design Consultant Designer 5-10 years Yes 90 80 90 85 68 30 65 75 50 55 50 30 50 Client Designer > 10 years Yes 95 75 87 80 75 28 55 65 50 55 42 25 51 QS Consultant Quantity Surveyor 5-10 years No 90 85 85 82 65 58 60 75 45 58 25 32 52 Client Designer > 10 years Yes 100 85 90 78 80 48 55 68 50 55 35 28 95 PROFILE Full name: ĐỖ DUY LINH Date of birth: 21st October 1993 Place of birth: Khánh Hòa Address: Lotus Apartment, An Binh Ward, Di An City, Binh Duong Email: duylinh.do@outlook.com / linh.do.imp21@hcmut.edu.vn Phone number: (+84) 818 892 209 TRAINING PROCESS:  From 08/2011 to 04/2017: HCMC University of Technology Bachelor of Engineering Major: Civil and Industrial Structures GPA: 8.58  From 09/2020 to present: HCMC University of Technology International Master Program Major: Construction Management GPA: 8.63 WORKING PROCESS:  From 01/2017 to 04/2019: Unicons Investment Construction Co Ltd Position: Quantity Surveyor – Tender Department  From 08/2019 to 01/2022: Structon Vietnam Pty Ltd Position: Senior Quantity Surveyor  From 02/2022 to present: First Quality Management Corporation Position: Project Manager