Recent Advances in Structural Health Monitoring

Dear Colleagues,

Structural health monitoring (SHM) is critical for ensuring the safety, resilience, and sustainability of civil infrastructure. Climate change and the global drive towards zero-carbon solutions further highlight the need for efficient monitoring strategies that optimize maintenance, extend service life, and reduce environmental impact. As structures become more complex and exposed to evolving risks, advancements in sensing technologies and data analytics are transforming SHM into a more intelligent and autonomous discipline. Recent developments in fiber-optic sensors, wireless networks, UAV-based inspections, and computer vision techniques have significantly improved real-time structural assessment. Meanwhile, machine learning is enabling automated damage and anomaly detection, predictive maintenance, and data-driven decision making. The integration of digital twins, data fusion techniques, and hybrid physics-based and data-driven approaches further enhances monitoring accuracy and operational efficiency. 

This Special Issue invites cutting-edge research on SHM advancements, including but not limited to the following: 

• Population-based SHM;
• Advanced sensing and instrumentation in SHM;
• Computer vision techniques for structural assessment;
• Machine learning applications in SHM;
• Digital twins for structural performance monitoring;
• Data fusion and integration of monitoring data;
• Decision-making frameworks for predictive maintenance and risk assessment;
• Case studies in SHM applications and lessons learnt. 

Dr. Zuo Zhu
Dr. Weijie Li
Dr. Yanlong Xie
Dr. Yichen Zhu
Dr. Miaomin Wang
Guest Editors

Manuscript Submission Information

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