Resistance degradation model of concrete beam bridge based on inverse Gaussian stochastic process

To accurately reflect the time-dependence and randomness of the resistance degradation of concrete girder bridges,the inverse Gaussian (IG)stochastic process was used to establish the structural resistance degradation model. Based on the field test data,the IG stochastic process was updated in real-time by Bayesian updating theory. In addition,a mixed Gibbs sampling method was proposed to address the difficulty in estimating high-dimensional parameters in the model due to the non-conjugate prior and posterior distributions. The feasibility of the method was demonstrated by numerical cases,and the resistance prediction of a concrete girder bridge was conducted. The research results show that the IG stochastic process can employ the field detection information to update the bridge resistance degradation process in real time. Moreover,the mixed Gibbs sampling method can solve the problem of high-dimensional parameter estimation,and overcome the defect of empirical given value of exponential q in the shape function. With the increase of service life,the accumulated deterioration of bridge structure gradually increases. When the service life of the bridge is 60 a,the accumulated deterioration is 3. 48 times that of 30 a. Compared with the Gamma stochastic process,the IG stochastic process avoids the assumption of several initial parameters,and thus an accurate bridge resistance degradation model is obtained. © 2024 Southeast University. All rights reserved.