Faulty data detection and classification for bridge structural health monitoring via statistical and deep‐learning approach

Over the last several decades, a lot of bridges have been equipped with the bridge structural health monitoring system, leading to an accumulation of voluminous monitoring data. Since the sensors and associated transmission hardware are subjected to harsh environments, the monitoring data frequently contains various faults, and it is laborious to cleanse the data manually. For the purpose of automatically detecting and classifying faulty monitoring data in large quantities, this paper proposes a novel method that uses the relative frequency distribution histograms (RFDH) of monitoring data as well as the one-dimensional convolutional neural network (1-D CNN). The overall procedure of this method can be described as follows: First, RFDHs are constructed from different classes of hour-long data segments. Second, inverted envelopes of the RFDHs are labeled as the training data to train the 1-D CNN. Third, a well-trained 1-D CNN is used to detect and classify long-term monitoring data according to their RFDHs of hour-long data segments. Comprehensive validation of the proposed method is conducted with selective acceleration data collected from two long-span bridges. The validation yields satisfactory results, demonstrating the accuracy, efficiency, and generality of the method.