This paper investigates a model-free damage detection method using a laboratory model of a steel arch bridge with a five-metre span. The efficiency of the algorithm was studied for various damage cases. The structure was excited with a rolling mass and seven accelerometers were used to record its response. An artificial neural network (ANN) was trained to predict the bridge accelerations based on data collected from the undamaged structure. Damage-sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state was established with which new data could be compared to. Outliers from the reference state were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov–Smirnov test. The method showed promising results and damage was successfully detected for four out of the five single damage cases. The gradual damage case was also detected, however, for some instances, greater damage did not result in an increase in the damage index. The Kolmogorov–Smirnov test performed best at detecting small single damage cases, while Mahalanobis distance was better at tracking gradual damage.

中文翻译：

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使用人工神经网络的实验室桥梁无模型损伤检测

本文研究了使用五米跨度钢拱桥的实验室模型进行的无模型损伤检测方法。研究了各种损坏情况下算法的效率。该结构被滚动质量激励，并使用七个加速度计记录其响应。训练了一个人工神经网络（ANN），可根据从未损坏结构中收集的数据预测桥梁加速度。损伤敏感特征定义为实测数据与ANN预测之间的均方根误差。建立了可以与新数据进行比较的基线健康状态。来自参考状态的异常值被视为损坏的指示。使用了两种离群值检测方法：马氏距离和Kolmogorov-Smirnov检验。该方法显示出令人鼓舞的结果，并且在五个单例损坏案例中有四个成功检测到损坏。还检测到了逐渐损坏的情况，但是，在某些情况下，更大的损坏并未导致损坏指数的增加。Kolmogorov–Smirnov检验在检测较小的单个损坏案例中表现最佳，而Mahalanobis距离在跟踪渐进损坏方面表现更好。