为充分发挥监测系统的应有作用，实现大型桥梁车辆荷载识别，将桥梁动态称重（B-WIM）技术与健康监测系统（HMS）相结合，研究基于HMS的B-WIM的相关理论与方法。依托斜拉桥及其HMS实际工程，选取被监测的运营索力为观测参数，通过索力时程特征分析和神经网络方法识别车速和车重;首先采用小波和EMD分解方法对运营索力进行处理和解析，将索力的车辆响应分量与恒载、温度响应以及随机干扰部分分离，进而通过单索力车辆响应峰值锐度对车速预估，再通过多索力车辆响应峰值的匹配解算车速；然后，面向多索力响应构建车重识别的BP网络模型，基于相似公路车速车重联合分布模型构建车队样本进行车桥耦合分析，提取索力响应建立了2 916组数据样本用于网络的训练和检验，实现了较高精度的车重识别网络训练；最后，采用实际斜拉桥连续24 h索力监测数据，将上述车速车重识别方法在实际工程进行了应用和检验，共识别出车重50 kN以上的车辆463辆。通过对识别结果的统计分析表明，识别的车速、车重分布以及二者的联合分布较好的符合实际。斜拉索具有全桥空间广泛分布的特点，索力也是监测系统的必测响应并对车辆具有良好的敏感性，以索力为观测参数实现车重车速的识别是可行的，并该方法的数据处理与车辆识别过程易于实现在线和自动化。
Abstract
In order to give full play to function of a monitoring system and realize vehicle load identification of large bridges, the relevant theories and methods of bridge weight in motion (B-WIM) technology based on health monitoring system (HMS) were studied by combining HMS and B-WIM. Based on an actual cable-stayed bridge and its HMS, the monitored operating cable force was taken as the observation parameter, and the cable force time-history characteristic analysis and neural network method were used to recognize vehicle speed and weight. Firstly, the operating cable force was processed and analyzed with wavelet and EMD, and the vehicle response component of the cable force was separated from constant load, temperature response and random interference parts. Then, the vehicle speed was pre-estimated with the vehicle response’s peak sharpness of the single-cable force, and the vehicle speed was calculated by matching vehicle response peak values of multi-cable forces. Secondly, a BP network model for vehicle weight recognition was established based on multi-cable force response. According to the joint distribution model for vehicle speed and weight of similar highways, a fleet sample was constructed for vehicle-bridge coupled analysis. Cable force responses were extracted to build
2 916 sets of data samples for network training and testing, and realize vehicle weight recognition network training with higher accuracy. Finally, using 24-hour continuous cable force monitoring data of an actual cable-stayed bridge, the vehicle speed and weight recognition method described above was applied and tested in practical engineering to recognize 463 vehicles with weight of more than 50 kN. The statistical analysis for the recognized results showed that the recognized vehicle speed and weight distributions and their joint distribution agree better with real ones; stayed cables has the feature of wide distribution in a full bridge space, cable force is also the response measured necessarily of the monitoring system and has good sensitivity to vehicle, taking cable force as the observation parameter to realize the recognition of vehicle weight and speed is feasible; this method’s data processing and vehicle identification process are easy to realize online operation and automation.
关键词
桥梁健康监测 /
车速车重识别 /
监测索力 /
神经网络 /
斜拉桥
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Key words
bridge health monitoring /
vehicle speed and weight recognition /
cable force monitoring /
neural network /
cable-stayed bridge
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参考文献
[1] Jacob B, Feypell-De, La Beaumelle V. Improving Truck Safety: Potential of Weigh-in-motion Technology[J]. IATSS Research, 2010,34(1): 9-15
[2] Snyder R, Moses F, Kriss M. Bridge Weigh-in-motion System: USA,5111897[P]. 1972-05-12
[3] Moses F. Weigh-in-motion System Using Instrumented Bridges [J]. Transportation Engineering Journal, 1979, 105 (3) :233-249
[4] S.Q. Wu, S.S. Law. Vehicle Axle Load Identification on Bridge Deck with Irregular Road Surface Profile[J]. Engineering Structures 33 (2011) 591–601
[5] Andrew Lansdell, Wei Song, Brandon Dixon. Development and Testing of a Bridge Weigh-in-motion Method Considering Nonconstant Vehicle Speed[J], Engineering Structures 152 (2017) 709–726
[6] T.H.T. Chan, S.S. Law, T.H. Yung. Moving Force Identification Using an Existing Prestressed Concrete Bridge[J]. Engineering Structures 22 (2000) 1261–1270
[7] S.S. Law, J.Q. Bu, X.Q. Zhu, et al, Vehicle Axle Loads Identification Using Finite Element Method[J]. Engineering Structures 26 (2004) 1143–1153
[8] Tospol Pinkaew, Pattarapong Asnachinda. Experimental Study on the Identification of Dynamic Axle Loads of Moving Vehicles from the Bending Moments of Bridges[J]. Engineering Structures 29 (2007) 2282–2293
[9] 钱长照，陈昌萍．利用弯矩影响线的移动荷载识别方法[J], 动力学与控制学报, 2016, 14(2):183-186
QIAN Changzhao, CHEN Changping. Mehtod for Moving Force Identification Using Bending Moment Influence Line[J]. Journal of Dynamics and Control, 2016,14(2):182-185.
[10] Haoqi Wang, Tomonori Nagayama, Boyu Zhao, et al. Identification of Moving Vehicle Parameters Using Bridge Responses and Estimated Bridge Pavement Roughness[J]. Engineering Structures 153 (2017) 57–70
[11] 余佳代，王赞芝，王丹，等．基于动应变识别桥梁移动荷载[J], 国防交通工程与技术，2017(5):9-14
YU Jiadai, WANG Zanzhi, WANG Dan, et al. Identification of Bridge Moving Load Based on Dynamic Strain [J], Traffic Engineering and Technology for National Defence, 2017(5):9-14
[12] 杨慧，李奉阁，闫维明，等．基于ANN和动应变的梁桥移动荷载识别及试验[J], 振动、测试与诊断, 2018, 38(2):305-314
YANG Hui, LI Fengge, YAN Weiming, et al. Identification and Test of Moving Load of Beam Bridge Based on ANN and Dynamic Strain[J]. Journal of Vibration, Measurement and Diagnosis, 2018, 38(2):305-314
[13] Dongming Feng, Hao Sun, Maria Q. Feng, Simultaneous Identification of Bridge Structural Parameters and Vehicle Loads[J]. Computers and Structures 157 (2015) 76–88
[14] 邓露, 施海, 何维, 等. 基于虚拟简支梁法的桥梁动态称重研究[J]. 振动与冲击, 2018, 37(15):209-215.
DENG Lu, SHI Hai, HE Wei, et al. Vehicles' BWIM Based on Virtual Simply-supported Beam Method[J]. Journal of Vibration and Shock, 2018,37(15):209-215.
[15] 李小年, 陈艾荣, 马如进．桥梁动态称重研究综述[J]. 土木工程学报, 2013, 46(3):79-85
LI Xiaonian, CHEN Airong, MA Rujin. Review of bridge weigh-in-motion[J]. China Civil Engineering Journal, 2013, 46(3):79-85
[16] 陈惟珍, 王志平, 徐俊．一种用于钢桁架桥的BWIM方法[J]. 桥梁建设, 2009, 22(4):22-75
CHEN Weizhen, WANG Zhiping, Xujun. A BWIM Method Used for Steel Truss Bridges[J]. Bridge Construction, 2009, 22(4):22-75
[17] 王蕾, 侯吉林, 欧进萍．基于荷载形函数的大跨桥梁结构移动荷载识别[J]. 计算力学学报, 2012, 29(2):153-158
WANG Lei, HOU Jilin, OU Jinping. Moving Force Identification Based on Load Shape Function for a Long-span Bridge Structure[J]. Chinese Journal of Computational Mechanics, 2012, 29(2):153-158
[18] 张福俭．大跨度斜拉桥拉索索力与车辆荷载识别及建模研究[D]. 哈尔滨工业大学博士论文, 2013
ZHANG Fujian. Identificaion and Modeling of Stay Cable Tension and Vehicle Loads of Long-span Cable-stayed Bridges[D]. Ph.D. Dissertation of Harbin University of Technology, 2013
[19] 王伟，唐怀平．基于动力响应的桥梁移动载荷识别[J]. 应用数学和力学，2014, 35(增刊) :270-274
WANG Wei, TANG Huaiping. Identification of Bridge Dynamic Loads Based on Dynamic Responses[J]. Applied Mathematics and Mechanics, 2014,35(S):270-274
[20] 刘泽佳，程楠，周立成，等．基于桥梁实测应变大数据的超重车信号识别方法[J]，科学技术与工程，2018, 18(17): 101-106
LIU Zejia, CHENG Nan, ZHOU Licheng, et al. Identification Method of Overload Vehicles Based on Monitored Big Data of Strain in Bridge[J]. Science Technology and Engineering, 2018, 18(17): 101-106
[21] Huang N E, Zheng S, Long S R, et al. The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis [C]. Proceedings of the Royal Society of London, Series A, 1998, 454: 903-995
[22] 孙宗光，陈一飞，邵元．基于概率统计的运营状态下的索力变异性分析与评价[J]. 桥梁建设, 2018, 48(3):79-84
SUN Zongguang, CHEN Yifei, SHAO Yuan. Analysis and Evaluation of Stay Cable Tension Variability of Operation Conditions Based on Probability and Statistics[J]. Bridge Construction, 2018, 48(3):79-84
[23] Pranesh Chatterjee, Eugene OBrien, Yingyan Li, et al. Wavelet domain analysis for identification of vehicle axles from bridge measurements[J], Computers and Structures 2006, 84(28):1792-1801
[24] 柏林，刘小峰，秦树人．小波-形态-EMD综合分析法及其应用[J]. 振动与冲击, 2008, 27(5):1-4
BO Lin, LIU Xiaofeng, QIN Shuren. Hybrid Wavelet-Morphology-EMD Analysis and Its Application[J]. Journal of Vibration and Shock, 2008, 27(5):1-4
[25] 刘晓艳，贾明涛．基于改进EMD-小波阈值联合去噪的城市地下管线识别与定位[J]. 中国安全生产科学技术, 2015, 11(6):56-62
LIU Xiaoyan, JIA Mingtao. Identification and Positioning of Urban Underground Pipeline Based on Joint Denoising of Improved EMD and Wavelet Threshold[J]. Journal of Safety Science and Technology, 2015, 11(6):56-62
[26] 龚志强，邹明玮，高新全，等．基于非线性时间序列分析经验模态分解和小波分解异同性的研究[J]. 物理学报, 2005, 54(8):3947-3957
GONG Zhiqiang, ZOU Mingwei, GAO Xingquan, et al. On the Difference Between Empirical Mode Decomposition and Wavelet Decomposition in the Nonlinear Time Series[J]. Acta Physica Sinica, 2005, 54(8):3947-3957
[27] 孙宗光，陈一飞．桥梁结构健康监测分析与评价[M]. 北京：中国建筑工业出版社，2017.
SUN Zongguang, CHEN Yifei. Health Monitoring Analysis and Evaluation for Bridge Structures[M]. Beijing: China Architecture and Building Press, 2017.
[28] 陈斌，基于WIM的疲劳荷载模型和钢桥疲劳性能评估方法研究[D]，浙江大学博士学位论文，2018
Chen Bin, Research on Fatigue Load Model and Fatigue Performance Evaluation Method of Steel Bridge based on WIM Data[D], Ph.D. Dissertation of Zhejiang University, 2018
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