Static and dynamic vehicle load identification with lane detection from measured bridge acceleration and inclination responses

Abstract

Summary The effects from passing vehicle's load on bridge are divided into two categories: static effect and dynamic effect. The static effect usually indicates the pseudo‐static responses of the bridge caused by the vehicle gross weight while the dynamic effect means dynamic responses due to vehicle and bridge dynamic properties and bridge pavement roughness. Both effects need to be properly evaluated because the vehicle static weight is a governing factor in determining bridge's fatigue life while the dynamic part of the vehicle load tends to amplify the bridge responses. In this paper, a method based on an extended Kalman filter is proposed to identify vehicle static and dynamic load only from responses recorded by portable accelerometers, together with their transverse position, which affects the identification of the loads. Even though only accelerometers are employed as sensors, the bridge vertical acceleration can capture the dynamic load components and the inclination obtained from the projection of the gravitational acceleration in the longitudinal direction can capture the low‐frequency component. The feasibility of the proposed method is proved through numerical simulation and an experimental test on a bridge. This paper makes full use of three‐axis accelerometers to estimate vehicle's static and dynamic load considering the vehicle's passing route, thus solving the nonlinear problem caused by vehicle's transverse position. Moreover, the proposed method eliminates the need of using other devices like displacement sensor or strain gauges to obtain low‐frequency components.

Vehicle Load Estimation Using the Reaction Force of a Vertical Displacement Sensor Based on Fiber Bragg Grating

“…Equation ( 12) is obtained by substituting Equation (10) into Equation (11). Equation ( 12) can be converted into Equation (13), which can be reconstructed using W n * ↔ W kl and r ni * (t) ↔ r kli (t), as shown in Equation ( 14).…”

“…To identify changes in the condition of a bridge, it is important to accurately calculate the total loads acting on it [13]. Important loads acting on a bridge include dead loads and live loads (e.g., vehicle loads, wind loads, and seismic loads).…”

Investigation on the moving load identification for bridges based on long-gauge strain sensing and skew-Laplace fitting

“…The detection of vehicles and tires at two different scales can be obtained by the method. Wang et al [22] proposed a vehicle static and dynamic load identification method based on an extended Kalman filter. The identification process can be achieved from responses recorded by portable accelerometers and their transverse position.…”

Bayesian Vehicle Load Estimation, Vehicle Position Tracking, and Structural Identification for Bridges with Strain Measurement

“…Additional information of road roughness and vehicle parameters are generally required in these methods but they are difcult to be accurately estimated. To avoid the direct analysis of the coupled system, Wang et al [23] introduced the random walk model [24] for vehicle loads and conducted the simultaneous estimation of the vehicle loads and bridge states with augmented Kalman flter (AKF). Lai et al [25] further achieved the estimation of structural parameters with model updating method.…”