An Efficient Computational System For Defect Prediction through Neural Network And Bio-inspired Algorithms

• DOI: 10.46223/hcmcoujs.acs.en.14.2.61.2024
• Publication Date: 2024-10-23T02:59:41Z
• Authors: Abdelwahhab KHATIR; Abdelmoumin Oulad...; Erica MAGAGNINI

Detecting and locating damage is essential in maintaining structural integrity. While Artificial Neural Networks (ANNs) are effective for this purpose, their performance can be significantly improved through advanced optimization techniques. This study introduces a novel approach using the Grasshopper Optimization Algorithm (GOA) to enhance ANN capabilities predicting defect aluminum plates. The methodology begins by deriving input parameters from natural frequencies, with locations as output. A Finite Element Model (FEM) used simulate data varying locations, creating comprehensive dataset. To validate approach, experimental vibration analyses of plates different collected. We then compare our GOA-optimized against other metaheuristic algorithms, such Cuckoo Search (CSA), Bat (BA), Firefly (FA). Notably, CSA's slightly close GOA. results show that GOA-based method outperforms these traditional demonstrating superior accuracy prediction. advancement holds significant potential applications integrity monitoring maintenance.