Smart Structures and Systems

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A vibration-based approach for detecting arch dam damage using RBF neural networks and Jaya algorithms

  • Ali Zar (Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology) ;
  • Zahoor Hussain (Department of Civil and Environmental Engineering, North Dakota State University) ;
  • Muhammad Akbar (Department of Engineering Institute of Mountain Hazards and Environment, Chinese Academy of Sciences) ;
  • Bassam A. Tayeh (Civil Engineering Department, Faculty of Engineering, Islamic University of Gaza) ;
  • Zhibin Lin (Department of Civil and Environmental Engineering, North Dakota State University)
  • Received : 2023.01.30
  • Accepted : 2023.10.27
  • Published : 2023.11.25
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Abstract

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

Keywords

Acknowledgement

This research is jointly supported by the China Postdoctoral Science Foundation (No. 2018M631807), National Natural Science Foundation of China (Grant No. 51578202), Fundamental Research Funds for the Central Universities (Nos. N160103002, N170108029), National Natural Science Foundation of Liaoning (No. 201702281). The authors further acknowledge the support partially provided by USDOTs (693JK318500010CAAP and 693JK32110003POTA). The results, discussion, and opinions reflected in this paper are those of the authors only and do not necessarily represent those of the sponsors. The research funds above are greatly appreciated by the authors. Author can provide training and test data sets for both Cases in case of intrusted reader or researchers request us.

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