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在桥梁中早期检测损坏,使用基于自编码器的混合无监督学习框架

Seyed Soroush Pakzad¹, Amir R Masoodi²

¹Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

Scientific reports

|December 3, 2025

概括

这项研究增强了桥梁结构健康监测 (SHM) 使用混合无监督机器学习 (HUML) 与变异自动编码器 (VAE). VAE-OCSVM集成提供卓越的早期损坏检测,即使缺少数据和环境变化.

科学领域:

土木工程土木工程是指土木工程.
机器学习机器学习
结构健康监测结构健康监测

背景情况:

结构健康监测 (SHM) 对桥梁安全至关重要,但面临着缺少数据和环境变化等挑战.
在这些条件下,现有的方法难以在早期检测损害.

研究的目的:

评估混合无监督机器学习 (HUML) 框架,特别是变化自动编码器 (VAE),用于早期检测桥梁损坏.
在严重的环境和操作变化 (EOV) 和缺失数据下,比较六个基于VAE的模型的性能.

主要方法:

采用了四个步骤的框架:初始数据分析 (IDA),基于VAE的潜在表示,损害指标 (DI) 的HUML和值的极端值理论 (EVT).
在Z24桥数据集上评估了VAE,VAE-OCSVM,VAE-IF,VAE-LOF,VAE-DBSCAN和VAE-MSD.
根据决策错误,对EOV的稳定性和无值检测来评估性能.

主要成果:

将VAE与异常探测器集成,显著改善了损坏检测.
VAE-OCSVM在对抗EOV时表现出最高的精度,回忆,特异性和稳定性.
VAE-IF 和 VAE-DBSCAN 的表现相对较差.

结论:

混合无监督机器学习框架,特别是VAE-OCSVM,对于在桥梁中早期检测损坏是有效的.
拟议的框架成功地缓解了结构性健康监测中缺少数据和EOV的挑战.

关键词:

早期损害检测早期损害检测混合学习是一种混合学习.结构健康监测 (SHM) 是一种结构健康监测.无监督的机器学习 (UML)变化自编码器 (VAE) 是一个变化自编码器.

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