Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure
- 1st Edition - October 18, 2023
- Imprint: Woodhead Publishing
- Editor: M. Z. Naser
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 4 0 7 3 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 4 0 7 4 - 8
Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure highlights the growing trend of fostering machine l… Read more
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Request a sales quoteInterpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure highlights the growing trend of fostering machine learning to realize contemporary, smart, and safe infrastructure.
This volume delves into the latest advancements in machine learning and artificial intelligence, providing readers with practical insights into their applications in the analysis, design, and assessment of civil infrastructure. From the innovative use of Generative Adversarial Networks in the design of shear wall structures to the application of deep learning for damage inspection of concrete structures, each chapter offers a unique perspective on the integration of cutting-edge technology in the field. Explore the potential of AI-driven fire safety design for smart buildings, the challenges and promises of large-scale evacuation modeling, and the use of machine learning classifiers for evaluating liquefaction potential. The book also features an in-depth discussion on explainable machine learning models for predicting the axial capacity of strengthened CFST columns and the development of spalling detection techniques using deep learning. Whether you are a civil engineer, researcher, or industry professional, this book is an invaluable resource that will equip you with the knowledge and tools to revolutionize civil infrastructure design and management.
This book presents innovative research results supplemented with case studies from leading researchers in this dynamic and emerging field to be used as benchmarks to carry out future experiments and/or facilitate the development of future experiments and advanced numerical models. The book is delivered as a guide for a wide audience, including senior postgraduate students, academic and industrial researchers, materials scientists, and practicing engineers working in civil, environmental, and mechanical engineering.
- Presents the fundamentals of AI/ML and how they can be applied in civil and environmental engineering
- Shares the latest advances in explainable and interpretable methods for AI/ML in the context of civil and environmental engineering
- Focuses on civil and environmental engineering applications (day-to-day and extreme events) and features case studies and examples covering various aspects of applications
Academic and industrial researchers, materials scientists and practicing engineers working in civil, structural and mechanical engineering
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- 1: Integrated schematic design method for shear wall structures: a practical application of generative adversarial networks
- Abstract
- Acknowledgements
- 1.1. Introduction
- 1.2. Literature review
- 1.3. Framework
- 1.4. Preprocessing of architectural CAD drawings
- 1.5. Intelligent structural design based on GANs
- 1.6. Establishment of the structural analysis model
- 1.7. Case study
- 1.8. Conclusions
- Appendix 1.A. Python code to extract shear wall elements from a pixel image
- References
- 2: Leveraging machine learning techniques to support a holistic performance-based seismic design of civil structures
- Abstract
- 2.1. Introduction
- 2.2. Background
- 2.3. ML-assisted holistic seismic design
- 2.4. Case study
- 2.5. Conclusion
- Appendix 2.A. Example codes for ML implementation
- References
- 3: Deep learning methods for concrete structure damage inspection
- Abstract
- 3.1. Introduction
- 3.2. Main focus of the chapter
- 3.3. Conclusions
- References
- 4: Explainable computational intelligence method to evaluate the damage on concrete surfaces compared to traditional visual inspection techniques
- Abstract
- Acknowledgement
- 4.1. Introduction
- 4.2. Traditional visual inspection
- 4.3. Neural networks
- 4.4. Image classification problem
- 4.5. Conclusions
- References
- 5: Smart building fire safety design driven by artificial intelligence
- Abstract
- Acknowledgement
- Abbreviations
- 5.1. Introduction
- 5.2. AI model and numerical fire database
- 5.3. Intelligent Fire Engineering Tool (IFETool)
- 5.4. Perspectives of AI-driven building fire safety design
- 5.5. Summary
- References
- 6: The potential of deep learning in dynamic maintenance scheduling for thermal energy storage chiller plants
- Abstract
- 6.1. Background
- 6.2. Overview of deep learning
- 6.3. Deep learning for thermal-energy-storage chiller plants
- 6.4. Proposed intelligent maintenance system for TES chiller
- 6.5. Potential of deep learning-based maintenance scheduling
- 6.6. Summary
- References
- 7: Role of intelligent data analysis to enhance GPR data interoperability: road transports
- Abstract
- Acknowledgements
- 7.1. Introduction
- 7.2. Fundamentals of GPR images and complexity in data interpretations
- 7.3. Intelligent data analysis approaches for GPR applications
- 7.4. Real case study using IDA with GPR data for road transports
- 7.5. NDT indicators for road assessment condition performance
- 7.6. Final remarks and future prospective
- References
- 8: AI for large-scale evacuation modeling: promises and challenges
- Abstract
- Acknowledgements
- 8.1. Introduction
- 8.2. Literature review
- 8.3. Conceptual framework
- 8.4. Discussion
- 8.5. Conclusions
- Appendix 8.A.
- References
- 9: On application of machine learning classifiers in evaluating liquefaction potential of civil infrastructure
- Abstract
- 9.1. Introduction
- 9.2. Overview of the used machine learning algorithms
- 9.3. Explanatory data analysis
- 9.4. Model development process
- 9.5. Performance of the developed models
- 9.6. Risk-based liquefication evaluation
- 9.7. Illustrative example
- 9.8. Conclusions
- Data availability statement
- References
- 10: Explainable machine learning model for prediction of axial capacity of strengthened CFST columns
- Abstract
- 10.1. Introduction
- 10.2. Experimental datasets
- 10.3. FE simulation
- 10.4. Machine learning
- 10.5. Strength reduction factors
- 10.6. Conclusions
- Appendix 10.A. Supplementary data
- References
- 11: Harnessing data from benchmark testing for the development of spalling detection techniques using deep learning
- Abstract
- 11.1. Introduction
- 11.2. Bridge specimen
- 11.3. Proposed convolutional neural networks
- 11.4. Convolutional neural network training and testing
- 11.5. Summary and conclusions
- References
- Index
- Edition: 1
- Published: October 18, 2023
- No. of pages (Paperback): 298
- No. of pages (eBook): 340
- Imprint: Woodhead Publishing
- Language: English
- Paperback ISBN: 9780128240731
- eBook ISBN: 9780128240748
MN
M. Z. Naser
M. Z. Naser is a tenure-track Assistant Professor at the Department of Civil and Environmental Engineering and Earth Sciences and a member of the Artificial Intelligence Research Institute for Science and Engineering (AIRISE) at Clemson University. At the moment, his research group is creating causal & eXplainable machine learning methodologies to discover new knowledge hidden within systems belonging to the domains of structural engineering and materials science to help realize functional, sustainable, and resilient infrastructure. He is currently serving as the chair of the ASCE Advances in Information Technology committee and on a number of international editorial boards, as well as codal building committees (in ASCE, ACI, PCI, and FiB). He is a registered professional engineer in the states of Michigan and South Carolina.
Affiliations and expertise
Assistant Professor, Department of Civil and Environmental Engineering and Earth Sciences, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, SC, United StatesRead Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure on ScienceDirect