Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical Study
The scanning of bridge surface roughness by the test vehicle is a coupled and non-stationary problem since the bridge deflection caused by vehicles will inevitably enter into the vehicle response. To this end, a two-step procedure is proposed to retrieve the bridge surface profile from the noise-con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/9/3410 |
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| author | Y. B. Yang Baoquan Wang Zhilu Wang Kang Shi Hao Xu |
| author_facet | Y. B. Yang Baoquan Wang Zhilu Wang Kang Shi Hao Xu |
| author_sort | Y. B. Yang |
| collection | DOAJ |
| description | The scanning of bridge surface roughness by the test vehicle is a coupled and non-stationary problem since the bridge deflection caused by vehicles will inevitably enter into the vehicle response. To this end, a two-step procedure is proposed to retrieve the bridge surface profile from the noise-contaminated responses of a two-axle vehicle moving over bridges. Central to this is the elimination of the bridge deflection from the estimated unknown input to the test vehicle system. First, the extended Kalman filter with unknown inputs (EKF-UI) algorithm is extended to formulating the state-space equations for the moving vehicle over the bridge. Analytical recursive solutions are derived for the improved vehicle states and the unknown input vector consisting of the vehicle–bridge contact displacement and surface profile. Second, the correlation between the cumulated contact residuals and contact displacements for the two axles is approximately defined by using the vehicle’s parameters and location on the bridge. Then, the surface profile is retrieved from the unknown input by removing the roughness-free contact (bridge) displacement, calculated with no prior knowledge of bridge properties. The efficacy of the proposed procedure was validated by the finite element method and demonstrated in the parametric study for various properties of the system. It is confirmed that the retrieved bridge surface profile is in excellent agreement with the original (assumed). For practical use, the vehicle is suggested to run at a not-too-high speed or in a too noisy environment. The proposed technique is robust with regard to vehicle mass and bridge damping. |
| first_indexed | 2024-03-10T03:40:50Z |
| format | Article |
| id | doaj.art-3bb4c146f30d469b92fc92ed7b676e2c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:40:50Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3bb4c146f30d469b92fc92ed7b676e2c2023-11-23T09:18:04ZengMDPI AGSensors1424-82202022-04-01229341010.3390/s22093410Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical StudyY. B. Yang0Baoquan Wang1Zhilu Wang2Kang Shi3Hao Xu4National Engineering and Research Center for Mountainous Highways, Chongqing 400067, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400044, ChinaThe scanning of bridge surface roughness by the test vehicle is a coupled and non-stationary problem since the bridge deflection caused by vehicles will inevitably enter into the vehicle response. To this end, a two-step procedure is proposed to retrieve the bridge surface profile from the noise-contaminated responses of a two-axle vehicle moving over bridges. Central to this is the elimination of the bridge deflection from the estimated unknown input to the test vehicle system. First, the extended Kalman filter with unknown inputs (EKF-UI) algorithm is extended to formulating the state-space equations for the moving vehicle over the bridge. Analytical recursive solutions are derived for the improved vehicle states and the unknown input vector consisting of the vehicle–bridge contact displacement and surface profile. Second, the correlation between the cumulated contact residuals and contact displacements for the two axles is approximately defined by using the vehicle’s parameters and location on the bridge. Then, the surface profile is retrieved from the unknown input by removing the roughness-free contact (bridge) displacement, calculated with no prior knowledge of bridge properties. The efficacy of the proposed procedure was validated by the finite element method and demonstrated in the parametric study for various properties of the system. It is confirmed that the retrieved bridge surface profile is in excellent agreement with the original (assumed). For practical use, the vehicle is suggested to run at a not-too-high speed or in a too noisy environment. The proposed technique is robust with regard to vehicle mass and bridge damping.https://www.mdpi.com/1424-8220/22/9/3410bridgeKalman filterresidual responseroad profilevehiclevehicle scanning method |
| spellingShingle | Y. B. Yang Baoquan Wang Zhilu Wang Kang Shi Hao Xu Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical Study Sensors bridge Kalman filter residual response road profile vehicle vehicle scanning method |
| title | Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical Study |
| title_full | Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical Study |
| title_fullStr | Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical Study |
| title_full_unstemmed | Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical Study |
| title_short | Scanning of Bridge Surface Roughness from Two-Axle Vehicle Response by EKF-UI and Contact Residual: Theoretical Study |
| title_sort | scanning of bridge surface roughness from two axle vehicle response by ekf ui and contact residual theoretical study |
| topic | bridge Kalman filter residual response road profile vehicle vehicle scanning method |
| url | https://www.mdpi.com/1424-8220/22/9/3410 |
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