A Review on Seismic Vulnerability Assessment with Emphasis on Structural Health Monitoring

Abstract

Earthquake-prone regions like the circum-Pacific belt necessitate proactive measures to mitigate seismic risks. This study explores the synergy between Seismic Vulnerability Assessment (SVA) and Structural Health Monitoring (SHM) for enhanced infrastructure resilience. The researchers integrate machine learning algorithms with diverse sensors, including seismometers and accelerometers, to analyze vibration data for non-destructive testing. This multifaceted approach, encompassing ground-penetrating radar for subsurface analysis and acoustic emission/ultrasonic testing for internal assessment, refines SHM precision for pre-emptive vulnerability identification in various materials and configurations. By analyzing existing SVA studies focused on SHM, the authors ad-dress current gaps in monitoring practices, aiming to create more robust, scalable, and adaptable solutions for diverse structures and environments. The research dissects various SHM methods, analyzes their parameters and prototypes, and examines challenges in extant structure assessments. The authors propose com-prehensive strategies and solutions to bridge research gaps, leveraging non-destructive methods like the rebound hammer test and ferro scanning alongside computer-aided structural design software and strategic retrofitting. The authors aim to advance SHM for effective and interdisciplinary seismic risk mitigation strategies through collaborative efforts across academia, industry, and government sectors.