Structural Health Monitoring and Engineering Structures (eBook)

The book presents the select proceedings of International Conference on Structural Health Monitoring and Engineering Structures (SHM&ES) 2020. It brings together different applied and technological aspects of structural health monitoring. The main topics covered in this book include damage assessment, structural health monitoring, engineering fracture mechanics, Inverse problem using optimization techniques, machine learning, deep learning, Artificial intelligent and non-destructive evaluation. It will be a reference for professionals and students in the areas of civil engineering, applied natural sciences and engineering management.

Dr. Tinh Q. Bui is currently an Associate Professor at the Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Japan. He holds other academic positions including Visiting Professor (Dalian University of Technology, China) and Visiting Associate Professor (NTUST, Taiwan). Prof. Bui received his PhD in Mechanical Engineering, Vienna University of Technology, Austria, 2009. He serves Editor for Applied Mathematical Modelling, Thin-Walled Structures; Engineering Analysis with Boundary Elements; Scientific Reports, SN Applied Sciences. He was awarded 'The 2018 JACM Award for Young Investigators in Computational Mechanics' from Japan Association for Computational Mechanics. He is authored and co-authored of over 150 ISI peer-reviewed articles. Prof. Bui's research interests are computational mechanics, fracture mechanics, composites, computational inelasticity, and numerical methods'.

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The book presents the select proceedings of International Conference on Structural Health Monitoring and Engineering Structures (SHM&ES) 2020. It brings together different applied and technological aspects of structural health monitoring. The main topics covered in this book include damage assessment, structural health monitoring, engineering fracture mechanics, Inverse problem using optimization techniques, machine learning, deep learning, Artificial intelligent and non-destructive evaluation. It will be a reference for professionals and students in the areas of civil engineering, applied natural sciences and engineering management.