Application of electric eel foraging optimization algorithm in structural damage detection

In this thesis, an optimization-based approach was developed for detecting structural damage by utilizing the vibration-based dynamic behavior of structures. The primary objective of the study is to investigate the effectiveness of the Electric Eel Foraging Optimization (EEFO) algorithm, a nature-inspired metaheuristic method, in the context of structural damage detection, and to compare its performance with widely used algorithms such as Harmony Search (HS) and Artificial Bee Colony (ABC). The study delves into the formulation of the damage detection problem as an optimization task, aiming to minimize the discrepancy between measured and modeled structural data. To this end, an objective function based on natural frequencies and mode shapes was defined and solved using the EEFO algorithm. The method was implemented in MATLAB, and analyzes were conducted on both a 10-element planar truss and a 72-element spatial truss system. Furthermore, the algorithms were tested under varying population sizes (20, 50, 75, and 100) to evaluate their sensitivity to population parameters. To validate the accuracy of the findings, comparative analyzes were performed using the SAP2000 software. The results demonstrate that the EEFO algorithm delivers higher accuracy and robustness compared to the other algorithms not only in larger population sizes , but also under smaller populations, where it still outperformed HS and ABC in terms of damage localization and estimation accuracy. EEFO shows a more balanced exploration of the solution space, yielding lower error rates and more consistent predictions even in limited population conditions. In this context, EEFO emerges as a promising and effective alternative for structural damage detection problems.