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E-raamat: Artificial Intelligence in Construction Engineering and Management

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Artificial Intelligence in Construction Engineering and ManagementSuurem pilt
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This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally.
Introduction to Artificial Intelligence
1(16)
1 What Is AI
1(3)
1.1 AI Development
1(1)
1.2 AI Techniques
2(2)
2 What Is CEM
4(5)
2.1 Significance of CEM
5(1)
2.2 Activities in CEM
5(2)
2.3 Characteristics of CEM
7(2)
3 How AI Benefits CEM
9(2)
4 Organization of the Book
11(3)
References
14(3)
Knowledge Representation and Discovery
17(24)
1 Introduction
17(1)
2 Safety Leadership in Construction
18(1)
3 Knowledge Representation, Learning, and Discovery
19(1)
4 SEM-Enabled Knowledge Discovery
20(4)
4.1 Survey and Participants
20(1)
4.2 Hypothesis on Causal Relationships
20(3)
4.3 Knowledge Learning and Discovery
23(1)
5 Knowledge Learning from Data and Validation
24(8)
5.1 Data Collection
24(3)
5.2 Common Method Bias
27(1)
5.3 SEM Development
27(2)
5.4 Measurement Model Evaluation
29(1)
5.5 Structural Model Evaluation
29(3)
6 Knowledge Discovery in Safety Leadership
32(4)
6.1 Impacts of Path Coefficients
32(2)
6.2 Impacts of Stakeholder Participation
34(2)
7 Merits of SEM
36(1)
8 Summaries
37(1)
References
37(4)
Fuzzy Modeling and Reasoning
41(26)
1 Introduction
41(2)
2 Fuzzy Modeling and Reasoning Methods
43(1)
3 A Holistic FCM Approach
44(6)
3.1 FCM Development
45(3)
3.2 FCM Computation
48(1)
3.3 FCM Analytics
49(1)
4 TBM Performance in Tunnel Construction
50(3)
4.1 TBM Failure Mechanism
50(2)
4.2 TBM Failure Map Modeling
52(1)
5 FCM-Enabled TBM Performance Analysis
53(8)
5.1 Predictive RCA
54(3)
5.2 Diagnostic RCA
57(2)
5.3 Hybrid RCA
59(2)
6 Merits of FCM
61(3)
6.1 FCM Interpretability
61(1)
6.2 FCM Validation
62(1)
6.3 FCM Capacities
63(1)
7 Summaries
64(1)
References
65(2)
Time Series Prediction
67(28)
1 Introduction
67(1)
2 Estimation of Tunnel-Induced Ground Settlement
68(5)
2.1 Empirical Methods
68(1)
2.2 Analytical Methods
69(1)
2.3 Numerical Methods
70(1)
2.4 Intelligent Methods
71(1)
2.5 Comparison
71(2)
3 Time Series Prediction Methods
73(2)
4 A Hybrid Time Series Prediction Approach
75(7)
4.1 Decomposing Original Data Using WPT
76(2)
4.2 Predicting Separate Time Series Using LSSVM
78(2)
4.3 Reconstructing Different Time Series
80(1)
4.4 Prediction Performance Evaluation
81(1)
5 A Realistic Tunnel Case
82(9)
5.1 Data Collection
82(2)
5.2 Training Details
84(3)
5.3 Analysis of Results
87(4)
6 Summaries
91(1)
References
92(3)
Information Fusion
95(30)
1 Introduction
95(2)
2 Structural Health Assessment (SHA) in Tunnels
97(2)
3 Information Fusion
99(1)
4 A Hybrid Information Fusion Approach
100(7)
4.1 Probabilistic SVM
101(2)
4.2 Improved D-S Evidence Rule
103(3)
4.3 Safety Risk Analysis and Assessment
106(1)
5 Information Fusion for SHA in Tunnels
107(8)
5.1 Project Profile
107(2)
5.2 SVM Model Training
109(4)
5.3 Multi-classifier Information Fusion
113(2)
6 Merits Analysis
115(6)
6.1 Uncertainty Modeling
115(4)
6.2 Comparative Study
119(2)
7 Summaries
121(1)
References
122(3)
Dynamic Bayesian Networks
125(22)
1 Introduction
125(1)
2 BN and DBN
126(2)
2.1 B.N
126(1)
2.2 DBN
127(1)
3 Dynamics in Tunnel-Induced Damages
128(4)
3.1 Tunnel-Induced Road Damage
129(1)
3.2 Control Standard for Road Damage
130(2)
4 DBN-Enabled Dynamic Risk Analysis
132(5)
4.1 Risk/Hazard Identification
132(1)
4.2 DBN Learning
133(2)
4.3 DBN Analytics
135(2)
5 Dynamic Risk Analysis in Tunnel-Induced Damages
137(8)
5.1 Case Profile
137(1)
5.2 DBN Model Development
137(4)
5.3 Predictive Analysis and Control
141(2)
5.4 Sensitivity Analysis and Control
143(1)
5.5 Diagnostic Analysis and Control
144(1)
6 Summaries
145(1)
References
146(1)
Process Mining
147(26)
1 Introduction
147(2)
2 Process Mining
149(2)
3 Event Logs in BIM
151(2)
4 Process Mining Framework
153(7)
4.1 Process Discovery
154(2)
4.2 Process Diagnosis
156(1)
4.3 Process Prediction
157(1)
4.4 Social Network Analysis
158(2)
5 Typical Applications in BIM Process Discovery
160(10)
5.1 Data Collection
160(1)
5.2 BIM Process Discovery
161(1)
5.3 BIM Process Diagnosis
161(4)
5.4 BIM Process Prediction
165(2)
5.5 BIM Collaborative Network Analysis
167(3)
6 Summarizes
170(1)
References
171(2)
Agent-Based Simulation
173(28)
1 Introduction
173(1)
2 Agent-Based Simulation
174(1)
3 Pedestrian Evacuation Under Emergency
175(3)
4 Simulation-Based Route Planning and Optimization
178(6)
4.1 Evacuation Network Construction
178(1)
4.2 Influential Factors Identification
179(2)
4.3 Route Planning Strategy Design
181(1)
4.4 Evacuation Efficiency Assessment
182(2)
5 Pedestrian Evacuation Simulation
184(6)
5.1 Case Profile
184(1)
5.2 Simulation Model Construct
185(3)
5.3 Simulation Model Validation
188(2)
6 Route Planning Optimization
190(5)
6.1 Average Pedestrian Density
191(2)
6.2 Average Evacuation Length
193(1)
6.3 Average Evacuation Time
194(1)
6.4 Average Evacuation Capacity
195(1)
7 Merits of Simulation and Optimization
195(2)
8 Summaries
197(1)
References
197(4)
Expert Systems
201(30)
1 Introduction
201(2)
2 Expert Systems
203(2)
3 Relationships Between BIM and Construction Safety
205(3)
3.1 BIM for Safety
205(1)
3.2 BIM Data Reuse
206(1)
3.3 Construction Safety Risks in Tunnels
207(1)
4 Knowledge Base Development for Construction Safety
208(7)
4.1 Knowledge Resources
208(3)
4.2 Knowledge Representation
211(3)
4.3 Knowledge Database Structure
214(1)
5 BIM-Based Risk Identification System (B-RIES)
215(6)
5.1 System Architecture
215(3)
5.2 Engineering Parameters Extraction
218(1)
5.3 Knowledge-Based Reasoning
218(2)
5.4 Risk Analysis and Control
220(1)
6 System Application
221(6)
6.1 Project Profile
221(1)
6.2 Safety Risk Identification
222(5)
6.3 Implementation Effects
227(1)
7 Summaries
227(1)
References
228(3)
Computer Vision
231(26)
1 Introduction
231(2)
2 Deep Learning and Computer Vision
233(4)
3 Computer Vision Framework
237(6)
3.1 Feature Pyramid Attention Module
238(1)
3.2 Spatial Attention Module
239(1)
3.3 Channel Attention Module
240(1)
3.4 Learning Process and Evaluation Metrics
241(2)
4 Computer Vision for Automated Crack Detection
243(8)
4.1 Data Resource
243(1)
4.2 Model Training
243(3)
4.3 Analysis of Results
246(3)
4.4 Comparison with Existing Models
249(2)
5 Merits of the Proposed Computer Vision Approach
251(3)
6 Summaries
254(1)
References
255(2)
Conclusions and Future Directions
257
1 Main Conclusions
257(2)
2 Future Directions
259(4)
References
263
Dr. Zhang is currently an Assistant Professor at the School of Civil and Environmental Engineering (CEE), Nanyang Technological University (NTU), Singapore. He received his B.S., M.S., and Ph.D. degrees from Huazhong University of Science and Technology (HUST), China, in 2009, 2012, and 2014, respectively. Dr. Zhangs research interests focus on Construction Automation, Artificial Intelligence, Building Information Modeling, and Infrastructure Resilience. He serves as the editorial board member of peer-reviewed journals, such as Automation in Construction, and Smart and Sustainable Built Environment. He has led research projects with up to 2 million Singapore dollars and has more than 90 papers published in peer-reviewed journals.

Yue Pan is currently a Ph.D. candidate at the School of Civil and Environmental Engineering (CEE) in Nanyang Technological University, Singapore. Her research interests include construction informatics, building information modeling, and data mining to support smart construction engineering and management. She received the M.S. of Civil Engineering from Carnegie Mellon University, USA, in 2017, where she was involved in the group of advanced infrastructure systems (AIS). She earned the B.S. of Engineering Mechanics from Tongji University, China, in 2016.  Xianguo Wu is Professor at the School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology (HUST), China. Prof. Wu received the Ph.D. degree at HUST in 2006. Her research interests include tunnel construction safety, BIM, digital twin, and green buildings. She had led several projects funded by the National Natural Science Foundation of China (NSFC).

Dr. Skibniewski is A. James Clark Endowed Chair  Professor of Construction Engineering and Project Management in the Department of Civil and Environmental Engineering at the University of Maryland in College Park.USA. Prior to his current appointment, he served for 20 years as a faculty member at Purdue University in West Lafayette, Indiana, where he held a position of Professor of Civil Engineering, Construction Engineering and Management. He received his M.Eng. degree from Warsaw University of Technology, and M.S. and Ph.D. degrees from Carnegie Mellon University.  As a researcher and educator, Professor Skibniewski currently specializes in e-commerce technology applications to engineering project management for construction and in construction automation. Dr. Skibniewski served on the National Academy of Engineering USA-Germany and USA-Japan Frontiers In Engineering committees, American Society of Civil Engineers' Robotics and Field Sensing Committee, Information Technology Committee, and  Intelligent Computing Committee, various technical committees of the Construction Industry Institute.
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