Localized Damage Detection Algorithm and Implementation on a Large-Scale Steel Beam-to-Column Moment Connection

Civil structures experience loading scenarios ranging from typical ambient excitations to extreme loads induced by natural events that, depending on their intensity, cause damage. It is important to detect damage before it propagates to become detrimental to integrity and functionality of the structure. Significant research efforts are focused on developing damage detection algorithms to diagnose damage from performance and response of the structure. A major challenge in many existing algorithms is in their validation and absence of real-scale implementation. This paper presents implementation of influence-based damage detection algorithm by implementation on a large-scale structural model (steel beam-to-column moment connection) which experiences progressive damage towards collapse of the system through increasing cyclic loading. IDDA utilizes statistical analysis of correlation functions between the structural responses at different locations. It is shown through this implementation that IDDA, accompanied by a statistical framework, can accurately identify structural changes and indicate the intensity of the damage.