Comparative Study on Damage Detection for Plate Structures Using Chaotic-TLBO with Multiple Objective Functions

Structural Health Monitoring (SHM) has a crucial role in maintaining the safety and longevity of critical structures, such as buildings, bridges, and aerospace components. Early detection of structural damage, allows for timely intervention, reducing costs and preventing catastrophic failures. This study introduces an innovative approach for damage detection in plate structures by enhancing the Teaching-Learning-Based Optimization (TLBO) algorithm through the integration of chaotic maps. The proposed method, termed Chaotic Teaching-Learning-Based Optimization (CTLBO), leverages the dynamic properties of chaotic maps to improve the algorithm's performance and robustness. The proposed method utilizes modal data to solve an inverse optimization problem, aiming to enhance the accuracy of damage detection. Two benchmark plate structures namely an L-plate with dual clamps and a Rectangular plate with a quarter-circle cutout, are analyzed under noisy and noise-free conditions. Four objective functions, formulated based on modal frequencies and mode shapes, are utilized to quantify the error in damage localization and severity estimation. The effectiveness of the CTLBO algorithm is evaluated against the standard TLBO algorithm. Results demonstrate that CTLBO outperforms TLBO, particularly in noisy environments, achieving near-zero error and offering superior robustness in identifying damage locations and intensities. The findings suggest that the integration of chaotic maps improves the convergence speed and reliability of the TLBO algorithm, making it a promising tool for real-world SHM applications.

Keywords:

Damage Detection , Plate structure , Chaotic Maps , Modal data , Chaotic-TLBO