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

被引：0

作者：

Nanjing Highway Development Center, Changzhou ^{[1
]}

211106, China

不详 ^{[2
]}

210000, China

不详 ^{[3
]}

93405, United States

不详 ^{[4
]}

21544, Egypt

不详 ^{[5
]}

不详 ^{[6
]}

LS2 9JT, United Kingdom

机构：

[1] Nanjing Highway Development Center, Changzhou

[2] Nanjing Zhixing Information Technology Co., Ltd., Nanjing

[3] Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, 93405, CA

[4] Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria

[5] Department of Civil Engineering, Nyala University, P.O. Box 155, Nyala

[6] School of Civil Engineering, University of Leeds, Leeds

来源：

SDHM Struct. Durability Health Monit. | / 6卷 / 763-783期

关键词：

big model; bridges; Convolutional Neural Network (CNN); Finite Element Method (FEM); Structural Health Monitoring (SHM);

D O I：

10.32604/sdhm.2024.053763

中图分类号：

学科分类号：

摘要：

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. © 2024 The Authors.

引用

页码：763 / 783

页数：20

共 32 条

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