Structural Damage Identification in GFRP Composite Plates Using TLBO Algorithm

Abstract

The damage detection techniques based on the response from a structure under vibration have gained significant attention for the health monitoring of various engineering structures. These techniques focus on the detection of damage present in the structures and also its severity based on the structural modal data, i.e. eigenvalues. In the present study, both undamaged and damaged glass fibre-reinforced plastic (GFRP) plates are modelled numerically to obtain the vibrational responses such as eigenvalues, eigenvectors using the finite element method. The vibration-based damage detection technique is implemented in which an error function is minimized which is the difference between the eigenvalues of the damaged plate and the undamaged plate obtained numerically. The minimization of the error function is carried out using a metaheuristic-based algorithm, namely, Teacher�Learning-based Optimization (TLBO). The baseline plate model is being iteratively updated for matching its response with the damaged model. In this paper, the error function is structured based on the eigenvalues of the plate models. It is observed that the vibration-based damage technique coupled with the TLBO algorithm is efficient for the detection of damage and its extent in the plates. � 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.