Details

Autor(en) / Beteiligte

• Meixedo, Andreia
• Santos, João
• Ribeiro, Diogo
• Calçada, Rui
• Todd, Michael

Titel

Damage detection in railway bridges using traffic-induced dynamic responses

Ist Teil von

• Engineering structures, 2021-07, Vol.238, p.112189, Article 112189

Ort / Verlag

Kidlington: Elsevier Ltd

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •A robust damage detection methodology using traffic-induced responses is proposed.•Advantage is taken by extracting autoregressive features from the large magnitude responses.•Influence of temperature, train type and speed are efficiently removed from the features.•Multi-sensor data fusion based on Mahalanobis distances enhances detection of early-damages.•Validation was conducted using experimental data and a digital twin of a long-span railway bridge. 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 bowstring-arch 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.