Artificial Intelligence in Vision-Based Structural Health Monitoring by Khalid M. Mosalam, Yuqing Gao
This book provides a comprehensive coverage of the state-of-the-art Artificial Intelligence (AI) technologies in vision-based structural health monitoring (SHM). In this data explosion epoch, AI-aided SHM and rapid damage assessment after natural hazards have become of great interest in civil and structural engineering, where using Machine Learning and Deep Learning in vision-based SHM brings new research direction. As researchers begin to apply these concepts to the structural engineering domain, especially in SHM, several critical scientific questions need to be addressed: (1) What can AI solve for the SHM problems? (2) What are the relevant AI technologies? (3) What is the effectiveness of the AI approaches in vision-based SHM? (4) How to improve the adaptability of the AI approaches for practical projects? (5) How to build a resilient AI-aided disaster prevention system making use of the vision-based SHM? This book introduces and implements the state-of-the-art Machine Learning and Deep Learning technologies for vision-based SHM applications. During this time the SHM field has adopted many data-driven algorithms from disparate fields such as radar and sonar detection, Artificial Intelligence/Machine Learning, speech-pattern recognition, statistical decision theory, and econometrics.
