A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.

中文翻译：

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用于钢框架结构结构健康监测的振动数据驱动机器学习架构

基于振动数据的机器学习架构专为钢平面框架结构的结构健康监测（SHM）而设计。该架构使用特征袋算法，从注册振动数据的时频尺度图图像中提取加速鲁棒特征 (SURF)。然后使用 K 均值聚类将判别性图像特征量化为视觉词汇。最后，训练支持向量机（SVM）以基于判别特征来区分框架结构的未损坏和多重损坏情况。机器学习架构的潜力针对未在训练中使用的未见过的数据集以及来自接近现有（即经过训练的）损坏类别的全新损坏的一些数据集进行了测试。然后将结果与使用其他三种特征和学习算法组合获得的结果进行比较：(i) 具有 SVM 的定向梯度直方图 (HOG) 特征，(ii) 具有 k 最近邻 (KNN) 的 SURF 特征和 (iii) HOG具有 KNN 的特征。为了检验该方法的稳健性，通过考虑环境变化以及损害的定位和量化，进一步扩展了该研究。实验结果表明，机器学习架构可以有效地对未损坏和不同的接头损坏类别进行分类，并且测试精度很高，这表明其在此类框架结构中的 SHM 潜力。(ii) 具有 k 最近邻 (KNN) 的 SURF 特征和 (iii) 具有 KNN 的 HOG 特征。为了检验该方法的稳健性，通过考虑环境变化以及损害的定位和量化，进一步扩展了该研究。实验结果表明，机器学习架构可以有效地对未损坏和不同的接头损坏类别进行分类，并且测试精度很高，这表明其在此类框架结构中的 SHM 潜力。(ii) 具有 k 最近邻 (KNN) 的 SURF 特征和 (iii) 具有 KNN 的 HOG 特征。为了检验该方法的稳健性，通过考虑环境变化以及损害的定位和量化，进一步扩展了该研究。实验结果表明，机器学习架构可以有效地对未损坏和不同的接头损坏类别进行分类，并且测试精度很高，这表明其在此类框架结构中的 SHM 潜力。