Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure

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.

• Focuses on civil engineering applications for extreme events
• Explains the fundamentals of AI/ML and how they can be applied in civil engineering
• Features case study examples, design codes, and problems and solutions that would work for extreme events

Academic and industrial researchers, materials scientists and practicing engineers working in civil, structural and mechanical engineering