A Structural Damage Early Warning Method Based on Convolutional Neural Networks and Autoencoders

Luo Yongpeng; He Aoli; Yu Kunming; Zhang Kai; Liao Feiyu

doi:10.13206/j.gjgS25092901

Volume 41 Issue 6

Jun. 2026

Turn off MathJax

Article Contents

Article Navigation > STEEL CONSTRUCTION(Chinese & English) > 2026 > 41(6): 1-9

Luo Yongpeng, He Aoli, Yu Kunming, Zhang Kai, Liao Feiyu. A Structural Damage Early Warning Method Based on Convolutional Neural Networks and Autoencoders[J]. STEEL CONSTRUCTION(Chinese & English), 2026, 41(6): 1-9. doi: 10.13206/j.gjgS25092901

Citation:

Luo Yongpeng, He Aoli, Yu Kunming, Zhang Kai, Liao Feiyu. A Structural Damage Early Warning Method Based on Convolutional Neural Networks and Autoencoders[J]. STEEL CONSTRUCTION(Chinese & English), 2026, 41(6): 1-9. doi: 10.13206/j.gjgS25092901

Citation:

PDF( 1971 KB)

A Structural Damage Early Warning Method Based on Convolutional Neural Networks and Autoencoders

doi: 10.13206/j.gjgS25092901

1. School of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China;
2. Nanping Highway Business Development Center, Nanping 353000, China;
3. Fujian Provincial Expressway Datong Inspection Co., Ltd., Fuzhou 350108, China

Received Date: 2025-09-29
Available Online: 2026-07-06

Abstract

Abstract

Sufficiently rich and diverse sample data are essential prerequisites for intelligent structural damage diagnosis. However， the scarcity of damage-state data in practical engineering applications limits the implementation of such methods. Unsupervised deep learning algorithms can partially address this challenge， and an autoencoder-based framework for structural damage early warning combining convolutional neural networks （CNNs） and autoencoders （AEs） was therefore proposed. The proposed framework comprises three modules： data preprocessing， feature extraction， and damage early warning. The feature extraction module uses a CNN to derive discriminative features from healthy-state samples， which serve as input to the damage early warning module. This module adopts an autoencoder model trained exclusively on healthy-state data. Structural damage is detected by monitoring the reconstruction error of input data. Damage is identified when this error exceeds a predefined threshold， which is determined through statistical analysis of the reconstruction error distribution from the healthy-state dataset. The feasibility and reliability of the method were validated using the Qatar Stadium benchmark dataset and controlled indoor damage tests. Experimental results demonstrated that the framework achieved a recognition accuracy exceeding 98% at a 20 dB signal-to-noise ratio， a damage recognition accuracy exceeding 93% even at a 5 dB signal-to-noise ratio， demonstrating its excellent noise immunity.
- structural health monitoring,
- damage identification,
- deep learning,
- convolutional neural network,
- autoencoder

FullText(HTML)

References(28)

References

[1]	朱宏平, 余璟, 张俊兵. 结构损伤动力检测与健康监测研究现状与展望[J]. 工程力学, 2011, 28（2）:1-11.
[2]	Domaneschi M, Martinelli L, Cucuzza R, et al. Structural control and health monitoring contributions to service-life extension of Bridges[J]. Proceedings in Civil Engineering, 2023, 6（5）:741-745.
[3]	李宏男, 李东升. 土木工程结构安全性评估、健康监测及诊断述评[J]. 地震工程与工程振动, 2002, 22（3）:82-90.
[4]	Alkayem N F, Cao M, Zhang Y, et al. Structural damage detection using finite element model updating with evolutionary algorithms:a survey[J]. Neural Computing and Applications, 2018, 30:389-411.
[5]	Chellini G, De Roeck G, Nardini L, et al. Damage analysis of a steel-concrete composite frame by finite element model updating[J]. Journal of Constructional Steel Research, 2010, 66（3）:398-411.
[6]	Lee J J, Lee J W, Yi J H, et al. Neural networks-based damage detection for bridges considering errors in baseline finite element models[J]. Journal of Sound and Vibration, 2005, 280（3/4/5）:555-578.
[7]	Salehi M, Ziaei-Rad S, Ghayour M, et al. A frequency response based structural damage localization method using independent component analysis[J]. Journal of Mechanical Science and Technology, 2013, 27（3）:609-619.
[8]	Fallahian M, Ahmadi E, Khoshnoudian F. A structural damage detection algorithm based on discrete wavelet transform and ensemble pattern recognition models[J]. Journal of Civil Structural Health Monitoring, 2022, 280:323-338.
[9]	Okafor A C, Dutta A. Structural damage detection in beams by wavelet transforms[J]. Smart Materials and Structures, 2000, 9（6）:906.
[10]	Han J, Ren W, Sun Z. Wavelet packet based damage identification of beam structures[J]. International Journal of Solids and Structures, 2005, 42（26）:6610-6627.
[11]	Esfandiari A, Nabiyan M S, Rofooei F R. Structural damage detection using principal component analysis of frequency response function data[J]. Structural Control and Health Monitoring, 2020, 27（7）:e2550.
[12]	Gui G, Pan H, Lin Z, et al. Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection[J]. KSCE Journal of Civil Engineering, 2017, 21（2）:525-534.
[13]	Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521:436-444.
[14]	Cheng F, Zhang H, Fan W, et al. Image Recognition Technology Based on Deep Learning[J]. Wireless Personal Communications, 2018, 102:1917-1933.
[15]	Otter D W, Medina J R, Kalita J K. A survey of the usages of deep learning for natural language processing[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32（2）:604-624.
[16]	Mei Q, Gül M. A fixed-order time series model for damage detection and localization[J]. Journal of Civil Structural Health Monitoring, 2016, 6（5）:763-777.
[17]	杨铄, 许清风, 王卓琳. 基于卷积神经网络的结构损伤识别研究进展[J]. 建筑科学与工程学报, 2022, 39（4）:38-57.
[18]	郭旭, 骆勇鹏, 王林堃, 等. 基于CNN与DCGAN的结构振动监测传感器故障诊断及监测数据恢复[J]. 铁道科学与工程学报, 2022, 19（11）:3383-3395.
[19]	Sharma S, Sen S. One-dimensional convolutional neural network-based damage detection in structural joints[J]. Journal of Civil Structural Health Monitoring, 2020, 10（5）:1057-1072.
[20]	李雪松, 马宏伟, 林逸洲. 基于卷积神经网络的结构损伤识别[J]. 振动与冲击, 2019, 38（1）:159-167.
[21]	骆勇鹏, 王林堃, 廖飞宇, 等. 基于一维卷积神经网络的结构损伤识别[J]. 地震工程与工程振动, 2021, 41（4）:145-156.
[22]	崔广新, 李殿奎. 基于自编码算法的深度学习综述[J]. 计算机系统应用, 2018, 27（9）:47-51.
[23]	Pathirage C S N, Li J, Li L, et al. Structural damage identification based on autoencoder neural networks and deep learning[J]. Engineering Structures, 2018, 172:13-28.
[24]	Rastin Z, Ghodrati A G, Darvishan E. Unsupervised structural damage detection technique based on a deep convolutional autoencoder[J]. Shock and Vibration, 2021, 2021（1）:6658575.
[25]	Shang Z, Sun L, Xia Y, et al. Vibration-based damage detection for bridges by deep convolutional denoising autoencoder[J]. Structural Health Monitoring, 2020, 20（2）:1880-1903.
[26]	Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323:533-536.
[27]	Avci O, Abdeljaber O, Kiranyaz S, et al. Wireless and real-time structural damage detection:a novel decentralized method for wireless sensor networks[J]. Journal of Sound and Vibration, 2018, 424:158-172.
[28]	李行, 骆勇鹏, 郭旭, 等. 强噪声小样本条件下基于图卷积神经网络的结构损伤识别[J]. 地震工程与工程振动, 2024, 44（3）:52-60.