Open Access

ARTICLE

Guided Wave Based Composite Structural Fatigue Damage Monitoring Utilizing the WOA-BP Neural Network

Borui Wang, Dongyue Gao^*, Haiyang Gu, Mengke Ding, Zhanjun Wu

College of Fiber Engineering and Equipment Technology, Jiangnan University, Wuxi, 214122, China

* Corresponding Author: Dongyue Gao. Email:

(This article belongs to the Special Issue: Computing Technology in the Design and Manufacturing of Advanced Materials)

Computers, Materials & Continua 2025, 83(1), 455-473. https://doi.org/10.32604/cmc.2025.060617

Received 06 November 2024; Accepted 30 December 2024; Issue published 26 March 2025

Abstract

Fatigue damage is a primary contributor to the failure of composite structures, underscoring the critical importance of monitoring its progression to ensure structural safety. This paper introduces an innovative approach to fatigue damage monitoring in composite structures, leveraging a hybrid methodology that integrates the Whale Optimization Algorithm (WOA)-Backpropagation (BP) neural network with an ultrasonic guided wave feature selection algorithm. Initially, a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves, thereby establishing a signal space that correlates with the structural condition. Subsequently, the Relief-F algorithm is applied for signal feature extraction, culminating in the formation of a feature matrix. This matrix is then utilized to train the WOA-BP neural network, which optimizes the fatigue damage identification model globally. The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets, with its performance benchmarked against the traditional BP neural network algorithm. The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities. The effect of different sensor-receiver path signals on the model damage recognition results is also discussed. The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area. Consequently, the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.

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