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Z.L. Wang¹, Z.X. Tan², B.Q. Wang¹, K. Shi¹, H. Xu¹ and Y.B. Yang¹

¹School of Civil Engineering, Chongqing University Chongqing, P.R. China
²State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China

doi:10.4203/ccc.6.2.6

Z.L. Wang, Z.X. Tan, B.Q. Wang, K. Shi, H. Xu, Y.B. Yang, "Retrieving Bridge Surface Roughness from a Two-Axle Vehicle Response by Kalman Filter", in P. Ivanyi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Seventeenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 6, Paper 2.6, 2023, doi:10.4203/ccc.6.2.6

Keywords: bridge, Kalman filter, road profile, vehicle scanning method.

Abstract

The bridge surface roughness estimation plays a crucial role in the monitoring and maintenance of the bridge, vehicle suspension design and optimization, etc. However, the conventional methods via profilers cannot meet the actual needs due to its low efficiency and economy, the vehicle response-based techniques generally neglect the vehicle-bridge interaction (VBI) effect and therefore cause an inaccurate estimate. In this study, a new procedure for identifying the bridge surface roughness and vehicle states (responses) simultaneously based on the Kalman filter with unknown inputs (KF-UI) is proposed. Central to this study is the consideration of the vehicle-bridge interaction effect via deducting the bridge displacement from the estimated unknown input vector. The efficacy of the procedure is numerically validated and the robustness is also tested against different parameters, including vehicle speed, vehicle-bridge mass ratio, environmental noise, bridge damping.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 6 PROCEEDINGS OF THE SEVENTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: P. Ivanyi, J. Kruis and B.H.V. Topping Paper 2.6 Retrieving Bridge Surface Roughness from a Two-Axle Vehicle Response by Kalman Filter Z.L. Wang¹, Z.X. Tan², B.Q. Wang¹, K. Shi¹, H. Xu¹ and Y.B. Yang¹ ¹School of Civil Engineering, Chongqing University Chongqing, P.R. China ²State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China doi:10.4203/ccc.6.2.6 Full Bibliographic Reference for this paper Z.L. Wang, Z.X. Tan, B.Q. Wang, K. Shi, H. Xu, Y.B. Yang, "Retrieving Bridge Surface Roughness from a Two-Axle Vehicle Response by Kalman Filter", in P. Ivanyi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Seventeenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 6, Paper 2.6, 2023, doi:10.4203/ccc.6.2.6 Keywords: bridge, Kalman filter, road profile, vehicle scanning method. Abstract The bridge surface roughness estimation plays a crucial role in the monitoring and maintenance of the bridge, vehicle suspension design and optimization, etc. However, the conventional methods via profilers cannot meet the actual needs due to its low efficiency and economy, the vehicle response-based techniques generally neglect the vehicle-bridge interaction (VBI) effect and therefore cause an inaccurate estimate. In this study, a new procedure for identifying the bridge surface roughness and vehicle states (responses) simultaneously based on the Kalman filter with unknown inputs (KF-UI) is proposed. Central to this study is the consideration of the vehicle-bridge interaction effect via deducting the bridge displacement from the estimated unknown input vector. The efficacy of the procedure is numerically validated and the robustness is also tested against different parameters, including vehicle speed, vehicle-bridge mass ratio, environmental noise, bridge damping. download the full-text of this paper (PDF, 10 pages, 480 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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