Intelligent Structural Health Monitoring for Seismic Performance and Resilience of Civil Structures

Earthquake excitation can induce changes in the stiffness, damping, dynamic characteristics, and load-carrying behavior of civil structures, ranging from barely detectable damage to significant structural deterioration. Reliable identification of these changes is essential for evaluating structural safety and resilience, particularly because earthquake-induced damage may be localized, internal, or otherwise invisible during conventional visual inspections. Structural Health Monitoring (SHM) provides a powerful framework for continuously measuring structural response and identifying changes in condition through damage-sensitive features obtained from vibration, strain, wave propagation, and other sensing techniques. Recent advances in smart sensors, wireless and distributed sensing, guided-wave and vibration-based monitoring, and real-time data acquisition have significantly enhanced the capability of SHM systems to capture both immediate and progressive structural changes associated with seismic events. Furthermore, artificial intelligence (AI), machine learning, and physics-informed approaches offer new opportunities for interpreting complex monitoring data and improving damage detection, localization, and assessment. Integrating these developments can provide more reliable and intelligent SHM approaches for evaluating seismic performance and structural resilience.The goal of this Research Topic is to advance Structural Health Monitoring (SHM) methodologies for assessing the seismic performance and condition of civil structures before, during, and following earthquake events. A major challenge is the reliable identification of earthquake-induced changes and damage, particularly when damage is localized, subtle, internal, or masked by environmental and operational variability. Conventional visual inspection and conventional response-based assessment may therefore fail to identify early-stage damage or accurately quantify changes in structural condition. Recent advances in vibration-based monitoring, guided-wave techniques, smart and distributed sensing, wireless sensor networks, real-time data acquisition, and multimodal sensing provide new opportunities for capturing damage-sensitive changes in structural behavior. At the same time, machine learning, deep learning, physics-informed methods, and digital-twin technologies offer powerful approaches for extracting and interpreting complex SHM data. This Research Topic aims to bring together innovative developments that improve the sensitivity, reliability, automation, and real-time capability of SHM for seismic damage detection, localization, quantification, structural condition assessment, and resilience.This Research Topic welcomes innovative research on Structural Health Monitoring (SHM) of civil engineering structures and infrastructure, with emphasis on monitoring structural condition, detecting and assessing damage, and improving structural safety, performance, and resilience. Contributions may address the development of advanced SHM methodologies, intelligent monitoring and data interpretation, damage identification and assessment, structural condition evaluation, real-time and continuous monitoring, and the integration of experimental, numerical, and data-driven approaches. Applications may cover a wide range of civil structures and infrastructure subjected to seismic, environmental, operational, and other forms of loading. Particular interest is given to research addressing challenging damage scenarios, early-stage or hidden damage, uncertainty, changing environmental and operational conditions, and the assessment of structural performance and resilience. Studies related to earthquake-induced damage and post-event assessment are also encouraged. We welcome original research articles, review articles, and methodological studies presenting significant advances in SHM theory, methodologies, modeling, validation, and practical applications. Topic editors: Reza Soleimanpour, Sayed Mohamad Soleimani, Mohamad Hussein Farhat Source: https://www.frontiersin.org/research-topics/86224/intelligent-structural-health-monitoring-for-seismic-performance-and-resilience-of-civil-structures