Application of Bayesian Update Method in the Construction Control of Continuous Rigid Frame Bridge Girders with High Piers and Large Spans

Abstract

In the construction process of large-scale bridges, there are uncertainties and time-varying factors in the environment and construction loads. It is difficult to make accurate estimates of the theoretical calculation models of construction control in advance. In view of this situation, Bayesian dynamic updating method is introduced to re-estimate the predicted results of the theoretical model. When applying this method, first, the finite element calculation model is determined based on the response surface method, and its calculation results are used as prior information. Then, combined with the actual detection data during the construction process, the Bayesian update formula is derived based on the conjugate prior distribution to correct the theoretical prediction results of bridge construction monitoring. Finally, the actual stress detection data of the control section of high-pier and large-span continuous rigid frame bridges during the construction process illustrate the application process of Bayesian updating in improving the theoretical prediction model. Results indicate that the internal force of the bridge control section obtained by re-evaluating by Bayesian theory not only incorporates the priori information models but also actual monitors sample information during the construction process. The predicted results reflect the true deformation and stress state of the bridge during the bridge construction process and improve the precision of construction monitoring.

Coupled Analysis of Safety Risks in Bridge Construction Based on N-K Model and SNA

“…In the construction process of large bridges, various uncertain factors such as environmental conditions and construction loads exist. To reduce the theoretical calculation errors in monitoring and controlling indicators during the construction of high bridge piers with large spans of continuous rigid-frame bridges, Zhou et al [5] introduced the Bayesian dynamic updating method to reevaluate the predicted results of the theoretical model. Li et al [6] constructed a bridge construction accident risk early warning model integrating the rough set (RS), sparrow search algorithm (SSA), and least squares support vector machine (LSSSVM) to predict the construction safety risk of bridge projects.…”