While machine learning has been increasingly incorporated into structural damage detection, most existing methods still rely on hand-crafted damage features. For a given structure, the performance of detection is heavily impacted by the quality of features, and choosing the optimal features may be difficult and time-consuming. Various time series classification algorithms studied in machine learning are able to classify structural responses into damage conditions without feature engineering; however, most of them only deal with univariate time series classification and are either inapplicable or ineffective on multivariate (i.e. multi-dimensional) data, thus unable to fully utilize all sensors available on real bridges. To address these limitations, we propose a global bridge damage detection method based on multivariate time series classification with optimized functional echo state networks. In this method, data from multiple sensors are directly used as inputs without feature extraction. Training of the functional echo state network is simple and straightforward, and by leveraging the nonlinear mapping capacity and dynamic memory of functional echo state network, the separability of different classes, that is, classifying accuracy is enhanced compared to conventional classification algorithms. Furthermore, hyperparameters of the functional echo state network are automatically optimized with particle swarm optimization algorithm, which further improves the accuracy while saving the cost of manual tuning. Experimental results on two classical data sets show that functional echo state network achieves high and stable accuracy, which indicate that our method can detect global bridge structural damage efficiently by analyzing multiple sensor data, and is prospected to be applied in real bridge structural health monitoring systems.

尽管机器学习已越来越多地纳入结构损伤检测中，但是大多数现有方法仍依赖手工制作的损伤特征。对于给定的结构，检测性能会受到特征质量的严重影响，并且选择最佳特征可能很困难且耗时。机器学习中研究的各种时间序列分类算法无需特征工程就可以将结构响应分类为损伤条件。但是，它们中的大多数仅处理单变量时间序列分类，对多变量（即多维）数据不适用或无效，因此无法充分利用实际桥梁上的所有传感器。为了解决这些限制，我们提出了一种基于多元时间序列分类和优化功能回波状态网络的全局桥梁损伤检测方法。在这种方法中，来自多个传感器的数据直接用作输入而无需特征提取。功能回波状态网络的训练简单明了，并且通过利用功能回波状态网络的非线性映射能力和动态内存，与传统的分类算法相比，不同类别的可分离性（即分类准确度）得到了提高。此外，利用粒子群优化算法自动优化功能性回波状态网络的超参数，从而进一步提高了精度，同时节省了手动调整的成本。