Developing a new hybrid fuzzy-Harris Hawks optimization approach of rule-based classification for damage detection of a composite panel-type structure

This study presents a novel structural health monitoring (SHM) approach for a composite panel-type structure, with a focus on damage localization and quantification. The approach leverages advanced fuzzy logic systems optimized through meta-heuristic algorithms, with a particular emphasis on the Fuzzy-Harris Hawk Optimization (Fuzzy-HHO) system, which serves as the foundation of the proposed SHM framework. A comparative analysis with the Fuzzy-Genetic Algorithm (Fuzzy-GA) system was conducted to evaluate their performance under various conditions. To validate the methodology, a physical model of a laminated composite panel was fabricated, and its vibration responses were analyzed through experimental modal analysis under fixed and pinned support conditions. The experimental data were utilized to update a finite element model, providing a reliable baseline for detecting structural damage. Damage scenarios were introduced by applying local mass changes, and the resulting frequency variations were used as critical input features for the fuzzy systems. The results demonstrated that the proposed Fuzzy-HHO system achieved superior performance in accurately identifying the location and magnitude of damage under both fixed and pinned support conditions. Its success rate surpassed that of the Fuzzy-GA system, particularly under challenging scenarios with added noise, showcasing its robustness and precision. The study highlights the efficiency and adaptability of the Fuzzy-HHO-based SHM framework, marking a significant advancement in the monitoring and maintenance of composite structures, particularly in challenging operational environments.