A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals

Abstract

Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure’s health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge’s health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others’ N−1 measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness.

An Improved Lightweight Deep Learning Model and Implementation for Track Fastener Defect Detection with Unmanned Aerial Vehicles

“…This method is inefficient, costly in terms of labor, and has unreliable accuracy, making it incapable of meeting modern requirements. This has also been confirmed in railway bridge inspection [3]. In order to improve detection efficiency, researchers have developed non-destructive testing methods, such as detection based on vibration signals [4,5], ultrasonic detection [6], laser detection [7], and machine vision detection [8].…”

The Concept of Creating Digital Twins of Bridges Using Load Tests

“…Due to the scope, amount, and complexity of the data, this comparison is usually carried out using automatic or semi-automatic systems based on machine learning and artificial intelligence algorithms [57][58][59]. It may include damage recognition based on the computer vision [60,61], structural condition assessment by pattern recognition and detection of anomalies [62], surrogate model-based reliability analysis [63], and dynamic measurement of displacement [64]. The results and decisions derived from the analysis are visualized in the as-built BIM model.…”

A Comparison of Deep Learning Algorithms for Anomaly Detection in Discrete Mechanical Systems

“…Indeed, the MAE loss represents the error of reconstruction performed by the autoencoder. Thus, it is likely to assume that the greater the error of reconstruction, the greater the damage [38].…”