Deep Neural Network and Evolved Optimization Algorithm for Damage Assessment in a Truss Bridge

• Published in: Mathematics (Basel) 2024-08, Vol.12 (15), p.2300
• Main Authors: Nguyen-Ngoc, Lan, Nguyen-Huu, Quyet, De Roeck, Guido, Bui-Tien, Thanh, Abdel-Wahab, Magd
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Bridge failure; Bridge maintenance; Bridges; Case studies; Civil engineering; Damage assessment; Damage detection; Damage localization; Effectiveness; machine learning; Neural networks; Optimization; optimization algorithm; Optimization algorithms; Scheduling; Sensors; Structural failure; Structural health monitoring; truss bridge; Truss bridges
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12152300

In Structural Health Monitoring (SHM) of bridges, accurately assessing damage is critical to maintaining the safety and integrity of a structure. One of the primary challenges in damage assessment is the precise localization and quantification of defects, which is essential for making timely maintenance decisions and reducing the risk of structural failures. This paper introduces a novel damage detection method for SHM of a truss bridge by coupling a Deep Neural Network (DNN) model with an evolved Artificial Rabbit Optimization (EVARO) algorithm. The integration of DNN with the stochastic search capability of the EVARO algorithm helps to avoid local minima, thereby ensuring more accurate and reliable results. Additionally, the optimization algorithm’s effectiveness is further enhanced by incorporating evolving predator features and the Cauchy motion search mechanism. The proposed method is first validated using various data benchmark problems, demonstrating its effectiveness compared to other well-known algorithms. Secondly, a case study involving the Chuong Duong truss bridge under different simulated damage scenarios further confirms the superiority of the proposed method in both localizing and quantifying damages.