Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

Qingqing Zhang; Wenju Zhao; Jian Zhang

doi:10.7250/bjrbe.2021-16.535

Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

Authors

Qingqing Zhang School of Civil Engineering, Sichuan Agricultural University, Dujiangyan 611830, China https://orcid.org/0000-0001-5695-8558
Wenju Zhao Jiangsu Key Laboratory of Engineering Mechanics, Southeast University, Nanjing 210096, China https://orcid.org/0000-0003-2892-390X
Jian Zhang Jiangsu Key Laboratory of Engineering Mechanics, Southeast University, Nanjing 210096, China https://orcid.org/0000-0002-9129-255X

DOI:

https://doi.org/10.7250/bjrbe.2021-16.535

Keywords:

axle parameters, influence line theory, long gauge strain, maximum strain, moving load identification

Abstract

Moving load identification has been researched with regard to the analysis of structural responses, taking into consideration that the structural responses would be affected by the axle parameters, which in its turn would complicate obtaining the values of moving vehicle loads. In this research, a method that identifies the loads of moving vehicles using the modified maximum strain value considering the long-gauge fiber optic strain responses is proposed. The method is based on the assumption that the modified maximum strain value caused only by the axle loads may be easily used to identify the load of moving vehicles by eliminating the influence of these axle parameters from the peak value, which is not limited to a specific type of bridges and can be applied in conditions, where there are multiple moving vehicles on the bridge. Numerical simulations demonstrate that the gross vehicle weights (GVWs) and axle weights are estimated with high accuracy under complex vehicle loads. The effectiveness of the proposed method was verified through field testing of a continuous girder bridge. The identified axle weights and gross vehicle weights are comparable with the static measurements obtained by the static weighing.

References

Alamandala, S., Prasad, R. L. N. S., Pancharathi, R. K., Pavan, V. D. R., & Kishore, P. (2021). Study on bridge weigh in motion (BWIM) system for measuring the vehicle parameters based on strain measurement using FBG sensors. Optical Fiber Technology, 61, 102440. https://doi.org/10.1016/j.yofte.2020.102440

Bao, T., Babanajad, S. K., Taylor, T., & Ansari, F. (2016). Generalized Method and Monitoring Technique for Shear-Strain-Based Bridge Weigh-in-Motion. Journal of Bridge Engineering, 21(1), 04015029. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000782

Bocciolone, M., Bucca, G., Collina, A., & Comolli, L. (2013). Pantograph–catenary monitoring by means of fibre Bragg grating sensors: Results from tests in an underground line. Mechanical Systems and Signal Processing, 41(1–2), 226–238. https://doi.org/10.1016/j.ymssp.2013.06.030

Caprani, C., OBrien, E. J., & Blacoe, S. (2013). Vision Systems for Analysis of Congested Traffic. IABSE Conference: Assessment, Upgrading and Refurbishment of Infrastructures, Rotterdam, The Netherlands, 6–8 May 2013, 432–433. https://doi.org/10.2749/222137813806501966

Chan, T. H. T., Yu, L., Law, S. S., & Yung, T. H. (2001). Moving Force Identification Studies, II: Comparative Studies. Journal of Sound and Vibration, 247(1), 77–95. https://doi.org/10.1006/jsvi.2001.3629

Chen, L., Jiang, Z., Gao, W., & Zhou, Y. (2020). Identification and adjustment of the pretension deviation in cable-strut tensile structures. KSCE Journal of Civil Engineering, 24(1), 143–152. https://doi.org/10.1007/s12205-020-1473-4

Chen, S-Z., Wu, G., & Feng, D-C. (2019). Development of a bridge weigh-in-motion method considering the presence of multiple vehicles. Engineering Structures, 191, 724–739. https://doi.org/10.1016/j.engstruct.2019.04.095

Dan, D., Ge, L., & Yan, X. (2019). Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision. Measurement, 144, 155–166. https://doi.org/10.1016/j.measurement.2019.05.042

Dunne, D., O’Brien, E. J., Basu, B., & Gonzalez, A. (2005). Bridge WIM systems with Nothing on the Road (NOR). International Conference on Weigh-in-motion, 4th, Taipei, Taiwan.

Feng, D., Sun, H., & Feng, M. Q. (2015). Simultaneous identification of bridge structural parameters and vehicle loads. Computers & Structures, 157, 76–88. https://doi.org/10.1016/j.compstruc.2015.05.017

Ge, L., Dan, D., Yan, X., & Zhang, K. (2020). Real time monitoring and evaluation of overturning risk of single-column-pier box-girder bridges based on identification of spatial distribution of moving loads. Engineering Structures, 210, 110383. https://doi.org/10.1016/j.engstruct.2020.110383

Gonzalez, A., Dowling, J., OBrien, E. J., & Žnidarič, A. (2012). Testing of a Bridge Weigh-in-Motion Algorithm Utilizing Multiple Longitudinal Sensor Locations. Journal of Testing and Evaluation, 40(6), 104576. https://doi.org/10.1520/JTE104576

González, A., Rowley, C., & OBrien, E. J. (2008). A general solution to the identification of moving vehicle forces on a bridge. International Journal for Numerical Methods in Engineering, 75(3), 335–354. https://doi.org/10.1002/nme.2262

He, W., Deng, L., Shi, H., Cai, C. S., & Yu, Y. (2017). Novel Virtual Simply Supported Beam Method for Detecting the Speed and Axles of Moving Vehicles on Bridges. Journal of Bridge Engineering, 22(4), 04016141. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001019

He, W., Ling T., Obrien, E. J., & Deng, L. (2019). Virtual axle method for bridge weigh-in-motion systems requiring no axle detector. Journal of Bridge Engineering, 24(9), 04019086. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001474

Jacob, B., & Feypell-de La Beaumelle, V. (2010). Improving truck safety: Potential of weigh-in-motion technology. Iatss Research, 34(1), 9–15. https://doi.org/10.1016/j.iatssr.2010.06.003

Kalin, J., Žnidarič, A., & Lavrič, I. (2006). Practical implementation of nothing-on-the-road bridge weigh-in-motion system. 9th International Symposium on Heavy Vehicle Weights and Dimensions, The Pennsylvania State University, Pennsylvania, Pittsburgh, PA, USA.

Law, S. S., & Chan, T. H. T., Zhu Q. Z., & Zeng Q. H. (2001). Regularization in moving force identification. Journal of Engineering Mechanics, 127(2), 136–148. https://doi.org/10.1061/(ASCE)0733-9399(2001)127:2(136)

Lee, I., Kwon, S. H, Park, J., & Oh, T. (2018). The effective near-surface defect identification by dynamic behavior associated with both impact-echo and flexural modes for concrete structures. KSCE Journal of Civil Engineering, 22, 747–754. https://doi.org/10.1007/s12205-017-1433-9

Li, H. L., Lu, Z. R., Liu, J. K., & Huang, M. (2016). Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm. Inverse Problems in Science and Engineering, 25(5), 694–709. https://doi.org/10.1080/17415977.2016.1191074

Li, S., & Wu, Z. (2007). Development of Distributed Long-gage Fiber Optic Sensing System for Structural Health Monitoring. Structural Health Monitoring, 6(2), 133–143. https://doi.org/10.1177/1475921706072078

Lu, Z. R., & Liu, J. K. (2011). Identification of both structural damages in bridge deck and vehicular parameters using measured dynamic responses. Computers & Structures, 89(13–14), 1397–1405. https://doi.org/10.1016/j.compstruc.2011.03.008

Meng, F., Yu, J., Alaluf, D., Mokrani, B., & Preumont, A. (2019). Modal flexibility based damage detection for suspension bridge hangers: A number and experimental investigation. Smart Structures and Systems, 23(1), 15–29. https://doi.org/10.12989/sss.2019.23.1.015

Pan, C. D., Yu, L., & Liu, H. L. (2017). Identification of moving vehicle forces on bridge structures via moving average Tikhonov regularization. Smart Materials and Structures, 26(8), 085041. https://doi.org/10.1088/1361-665X/aa7a48

Pinkaew, T., & Asnachinda, P. (2007). Experimental study on the identification of dynamic axle loads of moving vehicles from the bending moments of bridges. Engineering Structures, 29(9), 2282–2293. https://doi.org/10.1016/j.engstruct.2006.11.017

Rahbari, R., Niu, J., Brownjohn, J. M. W., & Koo, K. Y. (2015). Structural identification of humber bridge for performance prognosis. Smart Structures and Systems, 15(3), 665–682. https://doi.org/10.12989/sss.2015.15.3.665

Shahbaznia, M., Mirzaee, A., & Dehkordi, M. R. (2020). A new model updating procedure for reliability-based damage and load identification of railway bridges. KSCE Journal of Civil Engineering, 24(3), 890–901. https://doi.org/10.1007/s12205-020-0641-x

Tang, Q., Zhou, J., Xin, J., Zhao, S., & Zhou, Y. (2020). Autoregressive model-based structural damage identification and localization using convolutional neural networks. KSCE Journal of Civil Engineering, 24(7), 2173–2185. https://doi.org/10.1007/s12205-020-2256-7

Wang, H., Zhu, Q., & Li, J. (2019). Identification of moving train loads on railway bridge based on strain monitoring. Smart Structures and Systems, 23(3), 263–278. https://doi.org/10.12989/sss.2019.23.3.263

Wu, S. Q., & Law, S. S. (2001). Vehicle axle load identification on bridge deck with irregular road surface profile. Engineering Structures, 33(2), 591–601. https://doi.org/10.1016/j.engstruct.2010.11.017

Yang, C. Q., Yang, D., He, Y., Wu, Z. S., Xia, Y. F., & Zhang, Y. F. (2016). Moving Load Identification of Small and Medium-Sized Bridges Based on Distributed Optical Fiber Sensing. International Journal of Structural Stability and Dynamics, 16(4), 1640021. https://doi.org/10.1142/S0219455416400216

Zhang, H., Zhou, Y., & Quan, L. (2021). Identification of a moving mass on a beam bridge using piezoelectric sensor arrays. Journal of Sound and Vibration, 491, 115754. https://doi.org/10.1016/j.jsv.2020.115754

Zhang, Q., Zhang, J., Duan, W., & Wu, Z. (2017). Deflection distribution estimation of tied-arch bridges using long-gauge strain measurements. Structural Control and Health Monitoring, 25(3), e2119. https://doi.org/10.1002/stc.2119

Zhang, Q., Xia, Q., Zhang, J., Wu., Z. (2016). Optimal layout of long-gauge sensors for deformation distribution identification. Smart Structures and Systems, 18(3), 389–403. https://doi.org/10.12989/sss.2016.18.3.389

Zhang, Q., & Zhang, J. (2020). Internal force monitoring and estimation of a long-span ring beam using long-gauge strain sensing. Computer-Aided Civil and Infrastructure Engineering, 36(1), 109–122. https://doi.org/10.1111/mice.12569

Zhao, Z., Uddin, N., & O’Brien, E. J. (2016). Bridge Weigh-in-Motion Algorithms Based on the Field Calibrated Simulation Model. Journal of Infrastructure Systems, 23(1), 04016021. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000308

Zhu, X. Q., & Law, S. S. (2006). Moving load identification on multi-span continuous bridges with elastic bearings. Mechanical Systems and Signal Processing, 20(7), 1759–1782. https://doi.org/10.1016/j.ymssp.2005.06.004

Žnidarič, A., Kalin, J., & Lavrič, I. (2002). Bridge weigh-in-motion measurements on short slab bridges without axle detectors. International Conference on Weigh-In-Motion, Lowa State University, Orlando, Florida.

Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

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