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1st Edition - November 1, 2023
Editor: M. Z. Naser
Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure highlights the growing trend of fostering the use of CI… Read more
Immediately download your ebook while waiting for your print delivery. No promo code is needed.
Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure highlights the growing trend of fostering the use of CI to realize contemporary, smart, and safe infrastructure. This is an emerging area that has not fully matured yet, hence the book draws considerable interest and attention. In a sense, the book presents results of innovative efforts supplemented with case studies from leading researchers that can be used as benchmarks to carryout future experiments and/or facilitate the development of future experiments and advanced numerical models.The book is written with the intention to serve as a guide for a wide audience, including senior postgraduate students, academic and industrial researchers, materials scientists and practicing engineers working in civil, structural and mechanical engineering. While traditional methods fall short of adequately accounting for such complexity, fortunately, computational intelligence presents novel solutions that can effectively tackle growing demands of intense extreme events and modern designs of infrastructure – especially in this era where infrastructure is reaching new heights and serving larger populations with high social awareness and expectations.
Academic and industrial researchers, materials scientists and practicing engineers working in civil, structural and mechanical engineering
1. Integrated schematic design method for shear wall structures: A practical application of generative adversarial networks
2. Leveraging machine learning techniques to support a holistic performance-based seismic design of civil structures
3. Deep learning-based damage inspection for concrete structures
4. Explainable computational intelligence method to evaluate the damage on concrete surfaces compared to traditional visual inspection techniques
5. Smart building fire safety design driven by artificial intelligence
6. The potential of deep learning in dynamic maintenance scheduling for thermal energy storage chiller plants
7. The use of IDA on GPR data to monitor road transport infrastructures
8. Ai for large-scale evacuation modelling: promises and challenges
9. On the application of machine learning classifiers in evaluating liquefaction potential of civil infrastructure
10. Explainable machine learning model for prediction of axial capacity of strengthened CFST columns
11. Harnessing data from benchmark testing for the development of spalling detection techniques using deep learning
MN