This study proposes a damage detection method for bridges using Bayesian hypothesis testing, aiming at efficient inspection based on vibration monitoring. In the proposed damage detection method, firstly a posterior distribution of the parameters composing multivariate auto-regressive model is acquired from a bridge under healthy condition by means of Bayesian inference. Secondly, based on the distribution representing vibration of the healthy bridge, a Bayesian hypothesis test is conducted to detect change in the modal properties caused by damage. To investigate feasibility of the proposed method for damage detection, this study utilized data from a field experiment on an actual steel truss bridge whose truss member was artificially severed. The proposed method detected two different damage levels successfully.
Goi, Y., & Kim, C. W. (2017). Bayesian outlier detection for health monitoring of bridges. In Procedia Engineering (Vol. 199, pp. 2120–2125). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2017.09.073