Genuine Influence Line and Influence Surface Identification from Measured Bridge Response Considering Vehicular Wheel Loads
Publication: Journal of Bridge Engineering
Volume 28, Issue 2
Abstract
A bridge influence line (BIL) and a bridge influence surface (BIS) reflect the relationship between the bridge responses and the loads on the bridge and have been commonly used in techniques such as bridge damage detection, bridge safety evaluation, bridge model correction, bridge weigh-in-motion, and so on. Conventionally, a BIL can be extracted from the bridge response under a moving vehicle with known axle loads, while a BIS can then be obtained by lateral interpolation from BILs obtained for key transverse positions. However, in the traditional BIL-extracting methods, the transverse distance between the coaxial wheels was not considered; that is, the coaxial left and right wheel loads are simply treated as one concentrated load (usually referred to as the axle load). Therefore, the obtained BIL is the bridge response under two loads rather than one. Hence, errors may be introduced to the obtained BILs and propagated to the interpolated BIS. Moreover, the potentially significant effect of the unbalance of coaxial wheel loads on BIL identification is ignored. In this research, a new method for determining a genuine BIL and BIS is proposed. The wheel load rather than the axle load was taken into calculation in this method, where the effect of the transverse distance between the coaxial wheels and the unbalance of the coaxial wheel loads was naturally taken into consideration. Laboratory experiments and numerical simulations were performed to verify the effectiveness of the proposed method. Furthermore, a comprehensive parametric analysis was conducted to investigate the effects of some important parameters such as vehicle velocity, lateral deviation of the center of gravity of vehicles, axle count, and road surface condition on the identification performance. The results show that the BIL and BIS can be identified with satisfactory accuracy.
Practical Applications
Both the bridge influence line (BIL) and the bridge influence surface (BIS) have been commonly used in techniques such as bridge damage detection, bridge safety evaluation, bridge model correction, and bridge weigh-in-motion because they reflect the relationship between the bridge responses and the loads on the bridge. However, in the traditional vehicle-based BIL/BIS calibration methods, the track width of the calibration vehicle was ignored, leading to the accuracies of the calibrated influence lines/surfaces being affected by the track width and the transverse unbalance of the calibration vehicle. To address this problem, a new method, which can eliminate the negative effects of the track width and transverse unbalance, is proposed in this study. It provides a more accurate solution for bridge influence lines/surfaces than the traditional methods. The obtained bridge influence line/surface offers a more accurate input for related technologies and applications such as the bridge weigh-in-motion techniques and therefore improves the accuracy of these related technologies.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant Numbers 52108139 and 51778222), the Hunan Province Funding for Leading Scientific and Technological Innovation Talents (Grant Number 2021RC4025), the Key Research and Development Program of Hunan Province of China (Grant Number 2017SK2224), and the Talent Innovation and Entrepreneurship Project of Lanzhou (Grant Number 2021-RC-39).
References
Chen, Z., W. Yang, Q. Cheng, and J. Gao. 2019. “Bridge influence line identification method based on regularization and B-spline curves.” China J. Highway Transp. 32 (3): 101–108.
Chen, Z.-W., S. Zhu, Y.-L. Xu, Q. Li, and Q.-L. Cai. 2015. “Damage detection in long suspension bridges using stress influence lines.” J. Bridge Eng. 20 (3): 05014013. https://doi.org/10.1061/(asce)be.1943-5592.0000681.
Gonçalves, M. S., F. Carraro, and R. H. Lopez. 2021. “A B-WIM algorithm considering the modeling of the bridge dynamic response.” Eng. Struct. 228: 111533. https://doi.org/10.1016/j.engstruct.2020.111533.
Hansen, P. C., and D. P. O’Leary. 1993. “The use of the L-curve in the regularization of discrete ill-posed problems.” SIAM J. Sci. Comput. 14 (6): 1487–1503. https://doi.org/10.1137/0914086.
Hou, R., S. Jeong, J. P. Lynch, M. M. Ettouney, and K. H. Law. 2022. “Data-driven analytical load rating method of bridges using integrated bridge structural response and weigh-in-motion truck data.” Mech. Syst. Sig. Process. 163: 108128. https://doi.org/10.1016/j.ymssp.2021.108128.
Ieng, S.-S. 2015. “Bridge influence line estimation for bridge weigh-in-motion system.” J. Comput. Civ. Eng. 29 (1): 06014006. https://doi.org/10.1061/(asce)cp.1943-5487.0000384.
ISO. 1995. Mechanical vibration-road surface profiles-reporting of measured data. ISO-8608. Geneva: ISO.
Kinney, J., and C. Munsee. 1997. “Heavy truck wheel load distributions on the highway.” In Proc., Int. Congress and Exposition. SAE Publication Sp-1237. SAE Technical Paper 970966. Warrendale, PA: Society of Automotive Engineers (SAE).
Liao, J., G. Tang, L. Meng, H. Liu, and Y. Zhang. 2012. “Finite element model updating based on field quasi-static generalized influence line and its bridge engineering application.” Procedia Eng. 31: 348–353. https://doi.org/10.1016/j.proeng.2012.01.1035.
McCrum, D., E. O’Brien, and M. Khan. 2013. “Bridge health monitoring using an acceleration-based bridge weigh-in-motion system.” Key Eng. Mater. 569: 183–190. https://doi.org/10.2749/guimaraes.2019.0311.
McNulty, P., and E. J. O’Brien. 2003. “Testing of bridge weigh-in-motion system in a sub-arctic climate.” J. Test. Eval. 31 (6): 497–506. https://doi.org/10.1520/jte12377j.
Moses, F. 1979. “Weigh-in-motion system using instrumented bridges.” Transp. Eng. J. 105 (3): 233–249. https://doi.org/10.1061/tpejan.0000783.
O’Brien, E. J., M. J. Quilligan, and R. Karoumi. 2006. “Calculating an influence line from direct measurements.” Proc. Inst. Civ. Eng. Bridge Eng. 159 (1): 31–34. https://doi.org/10.1680/bren.2006.159.1.31.
O’Brien, E. J., L. Zhang, H. Zhao, and D. Hajializadeh. 2018. “Probabilistic bridge weigh-in-motion.” Can. J. Civ. Eng. 45 (8): 667–675. https://doi.org/10.1139/cjce-2017-0508.
Ojio, T., C. H. Carey, E. J. OBrien, C. Doherty, and S. E. Taylor. 2016. “Contactless bridge weigh-in-motion.” J. Bridge Eng. 21 (7): 04016032. https://doi.org/10.1061/(asce)be.1943-5592.0000776.
Ono, R., T. M. Ha, and S. Fukada. 2019. “Analytical study on damage detection method using displacement influence lines of road bridge slab.” J. Civ. Struct. Health Monit. 9 (4): 565–577. https://doi.org/10.1007/s13349-019-00352-9.
Park, M. Y., and T. Hastie. 2007. “L1-regularization path algorithm for generalized linear models.” J. R. Stat. Soc. B 69 (4): 659–677. https://doi.org/10.1111/j.1467-9868.2007.00607.x.
Quilligan, M. 2003. “Bridge weigh-in-motion: Development of a 2-D multi-vehicle algorithm.” Ph.D. thesis, Dept. of Civil and Architectural Engineering, Royal Institute of Technology.
Reichel, L., and G. Rodriguez. 2013. “Old and new parameter choice rules for discrete ill-posed problems.” Numerical Algorithms 63 (1): 65–87. https://doi.org/10.1007/s11075-012-9612-8.
Strauss, A., R. Wendner, K. Bergmeister, and D. M. Frangopol. 2011. “Monitoring and influence lines based performance indicators.” Appl. Stat. Probab. Civ. Eng. 106 (4): 1059–1068. https://doi.org/10.1201/b11332-160.
Strauss, A., R. Wendner, D. M. Frangopol, and K. Bergmeister. 2012. “Influence line-model correction approach for the assessment of engineering structures using novel monitoring techniques.” Smart Struct. Syst. 9 (1): 1–20. https://doi.org/10.12989/sss.2012.9.1.001.
Sysoev, O., and O. Burdakov. 2019. “A smoothed monotonic regression via L2 regularization.” Knowl. Inf. Syst. 59 (1): 197–218. https://doi.org/10.1007/s10115-018-1201-2.
Wang, N. B., L. X. He, W. X. Ren, and T. L. Huang. 2017. “Extraction of influence line through a fitting method from bridge dynamic response induced by a passing vehicle.” Eng. Struct. 151: 648–664. https://doi.org/10.1016/j.engstruct.2017.06.067.
Wu, B., G. Wu, and C. Yang. 2019. “Parametric study of a rapid bridge assessment method using distributed macro-strain influence envelope line.” Mech. Syst. Sig. Process. 120: 642–663. https://doi.org/10.1016/j.ymssp.2018.10.039.
Wu, B., G. Wu, C. Yang, and Y. He. 2018. “Damage identification method for continuous girder bridges based on spatially-distributed long-gauge strain sensing under moving loads.” Mech. Syst. Sig. Process. 104: 415–435. https://doi.org/10.1016/j.ymssp.2017.10.040.
Zhao, H., N. Uddin, X. Shao, P. Zhu, and C. Tan. 2015. “Field-calibrated influence lines for improved axle weight identification with a bridge weigh-in-motion system.” Struct. Infrastruct. Eng. 11 (6): 721–743. https://doi.org/10.1080/15732479.2014.904383.
Zheng, X., D. H. Yang, T. H. Yi, and H. N. Li. 2019. “Development of bridge influence line identification methods based on direct measurement data: A comprehensive review and comparison.” Eng. Struct. 198: 109539. https://doi.org/10.1016/j.engstruct.2019.109539.
Zheng, X., D. Yang, T. Yi, and H. Li. 2020. “Bridge influence line identification from structural dynamic responses induced by a high-speed vehicle.” Struct. Control Health Monit. 27 (7): 1–10. https://doi.org/10.1002/stc.2544.
Zheng, X., D. Yang, T. Yi, and H. Li. 2021. “Bridge influence surface identification method considering the spatial effect of vehicle load.” Struct. Control Health Monit. 28: 1–14. https://doi.org/10.1002/stc.2769.
Zou, H., and T. Hastie. 2005. “Regularization and variable selection via the elastic net.” J. R. Stat. Soc. B 67 (2): 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x.
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© 2022 American Society of Civil Engineers.
History
Received: Dec 16, 2021
Accepted: Oct 18, 2022
Published online: Dec 6, 2022
Published in print: Feb 1, 2023
Discussion open until: May 6, 2023
ASCE Technical Topics:
- Bridge engineering
- Bridge tests
- Bridges
- Concentrated loads
- Continuum mechanics
- Design (by type)
- Dynamic loads
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Field tests
- Highway transportation
- Infrastructure
- Load factors
- Models (by type)
- Numerical models
- Solid mechanics
- Static loads
- Statics (mechanics)
- Structural design
- Structural dynamics
- Structural engineering
- Tests (by type)
- Transportation engineering
- Transverse loads
- Vehicle loads
- Vehicles
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