Using Kalman filter to estimate the pavement profile of a bridge from a passing vehicle considering their interaction

He, Yan; Yang, Judy P.

doi:10.1007/s00707-021-03055-9

Cited by 15 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study of bridge span lengths and vehicle speeds was conducted to verify the method’s robustness. He and Yang [ 50 ] proposed a method for estimating road unevenness on a bridge from a single vehicle using a Kalman filter. The proposed method showed robust results against VBI, vehicle speed, noise contained in vehicle vibration, and bridge damping.…”

Section: Related Workmentioning

confidence: 99%

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

Shin

Okada

Yamamoto

2023

Sensors

View full text Add to dashboard Cite

For infrastructures to be sustainable, it is essential to improve maintenance and management efficiency. Vibration-based monitoring methods are being investigated to improve the efficiency of infrastructure maintenance and management. In this paper, signals from acceleration sensors attached to vehicles traveling on bridges are processed. Methods have been proposed to individually estimate the modal parameters of bridges and road unevenness from vehicle vibrations. This study proposes a method to simultaneously estimate the mechanical parameters of the vehicle, bridge, and road unevenness with only a few constraints. Numerical validation examined the effect of introducing the Kalman filter on the accuracy of estimating the mechanical parameters of vehicles and bridges. In field tests, vehicle vibration, bridge vibration, and road unevenness were measured and verified, respectively. The road surface irregularities estimated by the proposed method were compared with the measured values, which were somewhat smaller than the measured values. Future studies are needed to improve the efficiency of vehicle vibration preprocessing and optimization methods and to establish a methodology for evaluating accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

Shin

Okada

Yamamoto

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The measurement of vehicle parameters is usually complicated. Many existing studies assume that the vehicle parameters are known [ 2 , 7 , 11 ] or calibrated from vehicle vibrations [ 8 , 9 ]. However, Xue et al [ 10 ] and Keenahan et al [ 16 ] assume the parameters randomly first and apply a genetic algorithm (GA) to vehicle vibration data to simultaneously estimate the road profile and vehicle parameters.…”

Section: Road Unevenness Estimationmentioning

confidence: 99%

“…It has been confirmed that Xue’s method is robust to changes in vehicle speed. He et al [ 11 ] estimate bridge vibration and road unevenness from vehicle vibration using a Kalman filter. Yang et al [ 1 ] estimate bridge vibration components and extract road unevenness from vehicle vibration.…”

Section: Introductionmentioning

confidence: 99%

Practical Application of Drive-By Monitoring Technology to Road Roughness Estimation Using Buses in Service

Yamamoto

Shin

Sakuma

et al. 2023

Sensors

View full text Add to dashboard Cite

The efficiency of vehicles and travel comfort are maintained by the effective management of road pavement conditions. Pavement conditions can be inspected at a low cost by drive-by monitoring technology. Drive-by monitoring technology is a method of collecting data from sensors installed on a running vehicle. This technique enables quick and low-cost inspections. However, most existing technologies assume that the vehicle runs at a constant speed. Therefore, this study devises a theoretical framework that estimates road unevenness without prior information about the vehicle’s mechanical parameters even when the running speed changes. This paper also shows the required function of sensors for this scheme. The required ability is to collect the three-axis acceleration vibration and position data simultaneously. A field experiment was performed to examine the applicability of sensors with both functions to the proposed methods. Each sensor was installed on a bus in service in this field experiment. The vehicle’s natural frequency estimated from the measured data ranges from 1 to 2 Hz, but the natural frequency estimated by the proposed method is 0.71 Hz. However, the estimated road unevenness does not change significantly with changes in the vehicle’s estimated parameters. The results found that the accuracy of road unevenness estimation seems to be acceptable with the conventional method and the new method. Future work will include improving the algorithm and accuracy verification of the schemes.

show abstract

“…Vehicle scanning techniques have also been used to extract road [33][34][35][36] and rail [37,38] roughness profiles with the aid of Kalman filtering techniques [33][34][35][36][37][38] and neural network algorithms [39,40]. For example, Zeng et al [33] suggested a combination of an Extended Kalman Filter (EKF) and a Discrete Kalman Filter to estimate the road roughness from tire pressure.…”

Section: Introductionmentioning

confidence: 99%

“…Xiao et al [38] attempted to calculate track irregularities and bridge natural frequencies from vehicle response measurements based on a recursive Bayesian Kalman filtering algorithm. Dertimanis et al [37], Zhao et al [34,35], He and Yang [36], and Yang et al [41] also proposed different variants of Kalman Filters (including the Dual Kalman Filter [37], Augmented Kalman Filter (AKF) [34,35], Discrete Kalman filter [36], and EKF [41]) to estimate road roughness from vehicle response measurements.…”

Section: Introductionmentioning

confidence: 99%

Using Sensing On-Board Passing Vehicles for the Purpose of Virtual Sensing of Bridges

Siu,

Filippitzis,

Stoura

et al. 2023

Preprint

View full text Add to dashboard Cite

Using Kalman filter to estimate the pavement profile of a bridge from a passing vehicle considering their interaction

Cited by 15 publications

References 25 publications

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

Practical Application of Drive-By Monitoring Technology to Road Roughness Estimation Using Buses in Service

Using Sensing On-Board Passing Vehicles for the Purpose of Virtual Sensing of Bridges

Contact Info

Product

Resources

About