Improving the effectiveness of anomaly detection in bridges through a deep learning method based on coherence of signals

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Abstract:

In recent years, real-time monitoring of health conditions for massive structures, such as bridges and buildings, has grown in interest. Some of the key factors are the possibility to estimate continuously the health condition, as well as a reduction in the personnel involved in visual inspections and operative costs. However, while dealing with such structures, it is extremely rare to observe anomaly conditions, and when those are met is in general too late. Consequently, the structural health monitoring problem must be tackled as an unsupervised one. The idea exploited in this research is to transform the intrinsically unsupervised problem into a supervised one. Considering a structure equipped with N sensors, which measure static or quasi-static quantities (distance, inclinations, temperatures, etc.), it could be helpful to evaluate if the relations among sensors change over time. This involves the training of N models, each of them able to estimate the quantity measured by a sensor, by using the other N-1 measurements. In this way, an ensemble of models representing the system is built (iterative model). This approach allows us to compare the expected measurement of every sensor with the real one. The difference between the two can be addressed as a symptom of modifications in the structure with respect to the nominal condition. This approach is tested on a real case, i.e. the Candia bridge in Italy.