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

Lan Nguyen-Ngoc, Quyet Nguyen-Huu, Guido De Roeck, Thanh Bui-Tien and Magd Abdel-Wahab ()
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Lan Nguyen-Ngoc: Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Technologiepark Zwijnaarde 46, B-9052 Zwijnaarde, Belgium
Quyet Nguyen-Huu: DX Laboratory, The University of Transportation and Communications Limited Company (UCT), Hanoi 100000, Vietnam
Guido De Roeck: Department of Civil Engineering, KU Leuven, B-3001 Leuven, Belgium
Thanh Bui-Tien: Department of Bridge Engineering and Underground Infrastructure, Faculty of Civil Engineering, University of Transport and Communications, Hanoi 100000, Vietnam
Magd Abdel-Wahab: Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Technologiepark Zwijnaarde 46, B-9052 Zwijnaarde, Belgium

Mathematics, 2024, vol. 12, issue 15, 1-25

Abstract: 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.

Keywords: damage assessment; truss bridge; machine learning; optimization algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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