Open Access

ARTICLE

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

Yadong Xu¹, Weixing Hong², Mohammad Noori^3,6,*, Wael A. Altabey^4,*, Ahmed Silik⁵, Nabeel S.D. Farhan²

1 Nanjing Highway Development Center, Changzhou, 211106, China
2 Nanjing Zhixing Information Technology Co., Ltd., Nanjing, 210000, China
3 Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
4 Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt
5 Department of Civil Engineering, Nyala University, P.O. Box 155, Nyala, Sudan
6 School of Civil Engineering, University of Leeds, Leeds, LS2 9JT, UK

* Corresponding Authors: Mohammad Noori. Email: ; Wael A. Altabey. Email:

Structural Durability & Health Monitoring 2024, 18(6), 763-783. https://doi.org/10.32604/sdhm.2024.053763

Received 09 May 2024; Accepted 24 July 2024; Issue published 20 September 2024

Abstract

This study introduces an innovative “Big Model” strategy to enhance Bridge Structural Health Monitoring (SHM) using a Convolutional Neural Network (CNN), time-frequency analysis, and fine element analysis. Leveraging ensemble methods, collaborative learning, and distributed computing, the approach effectively manages the complexity and scale of large-scale bridge data. The CNN employs transfer learning, fine-tuning, and continuous monitoring to optimize models for adaptive and accurate structural health assessments, focusing on extracting meaningful features through time-frequency analysis. By integrating Finite Element Analysis, time-frequency analysis, and CNNs, the strategy provides a comprehensive understanding of bridge health. Utilizing diverse sensor data, sophisticated feature extraction, and advanced CNN architecture, the model is optimized through rigorous preprocessing and hyperparameter tuning. This approach significantly enhances the ability to make accurate predictions, monitor structural health, and support proactive maintenance practices, thereby ensuring the safety and longevity of critical infrastructure.

---

Keywords

---