Global bridge damage detection using multi-sensor data based on optimized functional echo state networks

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.