Damage detection in railway bridges using traffic-induced dynamic responses

This paper aims at detecting damage in railway bridges based on traffic-induced dynamic responses. To achieve this goal, an unsupervised automatic data-driven methodology is proposed, consisting of a combination of time series analysis methods and multivariate statistical techniques. Damage-sensitive features of train-induced responses are extracted and allow taking advantage, not only of the repeatability of the loading, but also, and more importantly, of its great magnitude, thus enhancing the sensitivity to small-magnitude structural changes. The efficiency of the proposed methodology is validated in a long-span steel-concrete composite bowstringarch railway bridge with a permanent structural monitoring system installed. An experimentally validated finite element model was used, along with experimental values of temperature, noise, and train loadings and speeds, to realistically simulate baseline and damage scenarios. The proposed methodology proved to be highly sensitive in detecting early damage, even when it consists of small stiffness reductions that do not impair the safety or use of the structure, and highly robust to false detections. The analysis and validation allowed concluding that the ability to identify early damage, imperceptible in the original signals, while avoiding observable changes induced by variations in train speed or temperature, was achieved by carefully defining the modelling and fusion sequence of the information. A single-value damage indicator, proposed as a tool for real-time structural assessment of bridges without interfering with the normal service condition, proved capable of characterizing multi-sensor data while being sensitive to identify local changes.

Automated summary

This paper proposes an automatic data-driven method for detecting damage in railway bridges based on traffic-induced dynamic responses, which extracts damage-sensitive features and utilizes the magnitude of loading to enhance sensitivity to small structural changes. Experimental validation shows that the method is highly sensitive to early damage detection and robust to false detections.