Over the last decades, the rising number of aging infrastructures has progressively fueled much interest toward the field of structural health monitoring. Following the increasing popularity of artificial intelligence algorithms, an autoencoder-based damage detection technique within the context of unsupervised learning is proposed in this paper to provide support for practical engineering applications. The developed methodology uses the autoencoder to reconstruct raw acceleration sequences of user-defined length collected from a healthy structure. To quantify the errors between the original input and the reconstructed output, which may be representative of damage occurrence, two indexes of reconstruction loss are selected as damage-sensitive features. To support damage detection, a selected number of short-time sequences are finally grouped into a unique macrosequence. The novel procedure can effectively both work at the single sensor level, as well as combine the predictive models using an ensemble learning strategy. Avoiding system identification, results obtained in the Z24 bridge demonstrate that the proposed method is quite effective for local damage detection with limited computational effort and using a limited number of sensors, thereby suitable to be easily applicable in the context of real-time bridge assessment.

中文翻译：

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用于无监督实时桥梁健康评估的自动编码器

在过去的几十年里，越来越多的老化基础设施逐渐激发了人们对结构健康监测领域的兴趣。随着人工智能算法的日益普及，本文提出了一种基于自编码器的无监督学习环境下的损伤检测技术，为实际工程应用提供支持。开发的方法使用自动编码器重建从健康结构收集的用户定义长度的原始加速度序列。为了量化原始输入和重建输出之间可能代表损伤发生的误差，选择重建损失的两个指标作为损伤敏感特征。为了支持损坏检测，选定数量的短时序列最终被组合成一个唯一的宏序列。新程序既可以有效地在单个传感器级别上工作，也可以使用集成学习策略组合预测模型。避免系统识别，在 Z24 桥梁中获得的结果表明，所提出的方法在计算量有限和使用有限数量的传感器的情况下对于局部损伤检测非常有效，因此适合在实时桥梁评估的背景下轻松应用。