Damage Detection in Railway Truss Bridges Employing Data Sensitivity under Bayesian Framework: A Numerical Investigation

Abstract

In general, for a structure it is quite difficult to get information about all of its modes through its dynamic response under ambient or external excitation. Therefore, it is vital to exhaustively use the available information in the acquired modal data to detect any damage in the structures. Further, in a Bayesian algorithm, it can be quite beneficial if a damage localization algorithm is first used to localize damage in the structure. In this way, the number of unknown parameters in the Bayesian algorithm can be reduced significantly and thus, the efficiency of Bayesian algorithm can be enhanced. This study exploits a mode shape and its derivative based approach to localize damage in truss type structures. For damage quantification purpose, a parameter sensitivity based prediction error variance approach in Bayesian model updating is employed, which allows extracting maximum information available in the modal data. This work employs the sensitivity based Bayesian algorithm to determine the posterior confidence in truss type railway bridges. Results of the study show that the proposed approach can efficiently detect and quantify damage in railway truss bridges.

An identification method of the weak link of stiffness for cantilever beam structure

“…But the reconstruction method has very high stability for the noise in testing data. Compared to Brasiliano et al, Frans et al, Sha et al, Alshalal et al, Nguyen et al, Wei et al, Yan and Golinval, and Kanta and Samit, 21 – 28 the identification method in this paper is more rapid, simple, and accurate to identify the weak link of the stiffness. Meantime, the superiority of the application of this method in the engineering field is proved.…”

“…Yan and Golinval 27 proposed a subspace identification technique applied for the identification of modal parameters, from which the measured flexibility matrix is constructed, but it is difficult to indicate exact damage location at a point where the change of diagonal entries of the flexibility matrix just appears. Kanta and Samit 28 utilized the change of mode shape and curvature to detect and quantify the damage in railway truss bridges, and the damage degree was evaluated by Bayesian damage identification algorithm with sensitivity. However, when the measured data is polluted by high-level noise, the accuracy of the modal parameters of the structure is affected.…”

Application of Bayesian statistical method in sensitivity-based seismic damage identification of structures: Numerical and experimental validation

“…41 Moreover, when an inverse problem of structural health monitoring with considerable unknowns is solved in Bayesian inference, the convergence becomes difficult and leads to false solution (especially for noise contaminated data) and increment of computational costs and accuracy of the results is affected. 42 Accordingly, utilizing multiple measurements can mitigate the effects of noise in the data and systematic changes can be separated from random fluctuations. 40 In this study, a statistical method in Bayesian paradigm is utilized to enhance damage detection results.…”