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Damage assessment in truss structures with limited sensors using a two-stage method and model reduction


D. Dinh-Cong, T. Vo-Duy, T. Nguyen-Thoi*

Source title: 
Applied Soft Computing, 66: 264–277, 2018 (ISI)
Academic year of acceptance: 

The paper proposes a practical two-stage approach for damage assessment in truss structures using noisy modal data collected from a limited number of sensors. In the first stage, a newly developed damage indicator, named here as normalized modal strain energy based damage index (nMSEBI), is proposed to locate effectively potential damage elements. In the second stage, the teaching-learning-based optimization (TLBO) algorithm is utilized as a robust optimization solver to determine the damage severity of suspected damage sites and also to exclude false alarms (if any) obtained in the previous stage. In addition, a Neumann series expansion-based second-order model reduction method (NSEMR-II) is adopted to condense the structural physical properties due to a limited number of sensors placed on the structure. The robustness and effectiveness of the proposed two-stage damage identification method are verified through two specific structures including a 31-bar planar truss and a 52-bar space truss with various damage scenarios. The obtained results clearly indicate that the proposed method can work well in determining both damage location and damage severity in the truss structures.