Structural Damage Identification Using Novel Objective Functions and Metaheuristic Algorithms

Structural Health Monitoring (SHM) technics have attracted vast amounts of attention among infrastructural managers for evaluating the structure''s health state and making an appropriate decision in case of emergency. The present study is aimed at proposing three novel objective functions using modal structural characteristics, including modal strain energy (MSE), generalized flexibility matrix (GFM), and mode shapes for structural damage detection. Modal assurance criterion (MAC) was also employed to form the objective functions. Moreover, three metaheuristic optimization algorithms (EO, Jaya, and TLBO) were implemented to execute the model updating process. The goal is to determine the most precise combination of the objective functions and optimization algorithms by examining them using three structural numerical models, comprising a 29-element steel planar truss, a 28-element steel planar frame, and a 25-element steel spatial truss under multiple damage scenarios. Moreover, the performance of the mentioned optimization algorithms was compared in terms of accuracy, stability and convergence speed by considering the impact of noisy and limited modal data. The obtained results indicated that the objective function formulated using MSE and mode shapes produced the most reliable outcomes for damage identification in all examples. Moreover, TLBO algorithm was introduced as the most precise optimization tool in most cases.