Autonomous structural health monitoring for vibration-based damage detection in civil structures

Large-scale civil structures are exposed to significant dynamic loads and harsh environmental conditions, leading to cyclic stresses that accelerate deterioration and increase failure risk. Structural Health Monitoring (SHM) has become essential for continuously assessing these structures, with modal parameters serving as key indicators of structural integrity. However, existing SHM systems often generate vast amounts of complex data that remain unanalyzed, underscoring the need for advanced automated algorithms to enable effective data interpretation. This Ph.D. project addresses the demand for autonomous SHM systems capable of
real-time assessments with minimal human intervention. Its primary aim is to develop an autonomous SHM framework that transforms raw vibration data into actionable insights regarding the structural condition of civil infrastructure. This framework integrates four key components: sensor fault diagnosis, modal parameter estimation, mitigation of environmental and operational effects, and damage detection. By linking shifts in modal parameters to structural issues, this automated SHM system is designed to operate reliably in the presence of significant external disturbances. The framework’s effectiveness is validated through experimental SHM applications, demonstrating its capability to detect structural changes under substantial environmental variability. The findings reveal notable advancements in modal identification accuracy, computational efficiency, and damage detection robustness, paving the way for improved performance in fully automated SHM systems.