A Bayesian algorithm with second order autoregressive errors for B-WIM weight estimation. (1st January 2022) - Gonçalves, Matheus Silva; Lopez, Rafael Holdorf; Oroski, Elder; Valente, Amir Mattar
Abstract: Bridge weigh-in-motion (B-WIM) systems are employed for estimating axle weights of vehicles traveling over the bridge structure, providing useful information for many applications regarding structural health monitoring. In this regard, the improvement of single axle weight estimates is a major concern, since B-WIM systems in general have more difficulties with such quantities when compared to the prediction of the total weight, mainly for closely spaced axles. The main goal of the present work is to develop a weigh strategy for B-WIM systems that prevents the occurrence of spurious values, improving the overall accuracy of estimates for single axle weights. For reaching this goal, prior beliefs regarding axle weights, such as their order of magnitude and similarity for closely spaced axles, are employed. Bayesian modeling is well suited for the present problem, since it allows the suitable combination of prior beliefs and experimental data for providing proper weight estimates. In addition, a covariance matrix based on a second order autoregressive process is employed for modeling the error between theoretical and measured responses aiming to overcome the negative effects due to the presence of serial correlation in such errors. Both simulated signals and an example of B-WIM system calibration data are employed for assessing the suitability of the proposed approach. Moreover, sensitivity analyses are conducted aiming to check the robustness of the strategy to its own model parameters. For all analyses, the overall accuracy of the proposed approach, when considering both single axle as well as gross vehicle weight (GVW) estimates, outperforms the baseline algorithms. Furthermore, the sensitivity analyses indicate that the conclusions are the same for distinct prior distributions based on the same prior information. Highlights: A Bayesian method allowing correlated errors for B-WIM weight estimation is proposed. General guidelines on how to define suitable prior distributions are presented. The errors are modeled as a second order autoregressive process. Accounting for prior beliefs for axle weights highly improves single axle estimates. Sensitivity analyses show that the method is robust to distinct prior parameters.