Damage Detection of High-Speed Railway Box Girder Using Train-Induced Dynamic Responses

Abstract

This paper proposes a damage detection method based on the train-induced responses of high-speed railway box girders. Under the coupling effects of bending and torsion, the traditional damage detection method based on the Euler beam theory cannot be applied. In this research, the box girder section is divided into different components based on the plate element analysis method. The strain responses were preprocessed based on the principal component analysis (PCA) method to remove the influence of train operation variation. The residual error of the autoregressive (AR) model was used as a potential index of damage features. The optimal order of the model was determined based on the Bayesian information criterion (BIC) criterion. Finally, the confidence boundary (CB) of damage features (DF) constituting outliers can be estimated by the Gaussian inverse cumulative distribution function (ICDF). The numerical simulation results show that the proposed method in this paper can effectively identify, locate and quantify the damage, which verifies the accuracy of the proposed method. The proposed method effectively identifies the early damage of all components on the key section by using four strain sensors, and it is helpful for developing effective maintenance strategies for high-speed railway box girders.

Damage Identification of Railway Bridges through Temporal Autoregressive Modeling

“…The effectiveness of the proposed approach was demonstrated through an application example of a high-fidelity digital twin of the Sado railway bridge in Portugal, reaching minimal false detection incidences of 2%. A similar study was recently reported by Wang et al [43], who investigated the use of strain measurements and the residual errors of an AR model to identify damage in high-speed railway box girders. Although their work demonstrated the potential of strain measurements for damage identification, it did not include a comparison with the results obtained using acceleration data, nor did it investigate the influence of environmental factors.…”