基于自编码器无监督学习结构损伤量化检测研究
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1.天津城建大学 计算机与信息工程学院 天津 300384;2.天津城建大学计算机与信息工程学院 天津 300384

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TP212;TN911.72

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天津市科技计划项目 23YDTPJC00350


Research on Unsupervised Structural Damage Quantification Detection Based on Autoencoder
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    摘要:

    结构健康检测指通过实时或周期性监测评估工程结构的健康状态,深度学习方法因能从原始数据中提取高层特征而备受关注。针对实际应用中损伤类别的多样性,缺乏对损伤状态进行定量分析,本文提出了部分跳跃卷积自编码器损伤判断量化方法。本方法使用卷积自编码器处理结构响应,将高维数据降维至低维特征空间,通过重构误差设定损伤指标,以判断健康状态;基于低维特征构建损伤系数,实现结构损伤量化。利用IASC-ASC I和IASC-ASCE II数据集验证了算法在损伤判断和量化方面的有效性。实验结果表明,损伤指标对大部分损伤状态的判定准确率达到100%,个别损伤状态下的准确率为96%,对不同损伤状态的量化均符合预期。

    Abstract:

    Structural health monitoring refers to the evaluation of the health condition of engineering structures through real-time or periodic monitoring. Deep learning methods have gained attention due to their ability to extract high-level features from raw data. However, the diversity of damage types in practical applications and the lack of quantitative analysis for damage states remain challenging. In this paper, a partial skip-connected convolutional autoencoder-based approach for damage assessment and quantification is proposed. This method utilizes a convolutional autoencoder to process structural responses, reducing high-dimensional data to a low-dimensional feature space. A damage index is defined based on reconstruction error to assess health status, while a damage coefficient constructed from the low-dimensional features enables quantitative damage assessment. The effectiveness of the algorithm in damage detection and quantification is validated using the IASC-ASCE Benchmark Structures I and II datasets. Experimental results demonstrate that the damage index achieves 100% accuracy in identifying most damage states, with 96% accuracy in certain specific cases, and that the quantification aligns well with expected values across different damage states.

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  • 收稿日期:2024-09-18
  • 最后修改日期:2024-11-20
  • 录用日期:2024-11-21
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