Bridge weigh-in-motion considering dynamic response in observation noise with application to multiple driving conditions

Abstract

In addition to causing a static response when traversing a bridge, moving vehicles also produce a dynamic response, which is thought to be one of the factors adversely affecting the accuracy of bridge weigh-in-motion approaches. This paper proposes a novel method for improving the accuracy of such approaches. Rather than modeling the dynamic response directly, the proposed model considers it as noise with autocorrelation. A Bayesian inference approach is considered as it allows a rational way of assimilating data into the model. Here, the dynamic response is considered in the form of a covariance matrix of observation noise, composed of the residuals of the measured and calculated responses, in a Bayesian framework. In a validation study using actual measured data, it was confirmed that the estimation of axle weights in the presence of multiple vehicles was significantly improved. From the result, it can be concluded that proper modeling of the dynamic response in observation data leads to more accurate weigh-in-motion estimates of axle weights and GVW, particularly when complicated vehicle travel is involved. A comparison of static and dynamic influence lines is also discussed using actual measured data. It is shown that the weights estimated by the dynamic influence line are generally more accurate than those by the static influence line when correlation of noise is not considered. The dynamic component in influence line can be interpreted as a regularization term. The proposed method, which uses static influence line and covariance matrix considering correlation of noise, is more accurate than the method with dynamic influence line and also, it does not cause any bias.