Application of Particle Swarm Optimization method to On-going Monitoring for estimating vehicle-bridge interaction system

TL;DR

This paper introduces a particle swarm optimization-based method for estimating vehicle and bridge parameters, as well as road unevenness, using only vibration and position data, verified through numerical experiments.

Contribution

The paper presents a novel PSO-based approach for vehicle-bridge interaction parameter estimation using minimal data, enhancing monitoring capabilities.

Findings

Vehicle weight can be estimated with reasonable accuracy.

Other parameters' estimation accuracy needs improvement.

Numerical experiments confirm the method's applicability.

Abstract

This study proposes a method for estimating the mechanical parameters of vehicles and bridges and the road unevenness, using only vehicle vibration and position data. In the proposed method, vehicle input and bridge vibration are estimated using randomly assumed vehicle and bridge parameters. Then, the road profiles at the front and rear wheels can be determined from the vehicle input and bridge vibration. The difference between the two road profiles is used as the objective function because they are expected to coincide when synchronized. Using the particle swarm optimization (PSO) method, the vehicle and bridge parameters and the road unevenness can be estimated by updating the parameters to minimize the objective function. Numerical experiments also verify the applicability of this method. In the numerical experiments, it is confirmed that the proposed method can estimate the vehicle weight with reasonable accuracy, but the accuracy of other parameters is not sufficient. It is necessary to improve the accuracy of the proposed method in the future.