Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (1): 188-197.doi: 10.13229/j.cnki.jdxbgxb.20230135

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Bridge weigh-in-motion combined with machine version

Guan-xu LONG1,2(),Xiu-shi ZHANG3,Gong-feng XIN1,2,Tao WANG3(),Gan YANG3   

  1. 1.Innovation Research Institute，Shandong Hi-Speed Group Co. ，Ltd. ，Jinan 250101，China
    2.School of Highway，Chang'an University Xi'an 710064，China
    3.Shandong Key Laboratory of Highway Technology and Safety Assessment，Jinan 250101，China
  • Received:2023-02-16 Online:2024-01-30 Published:2024-03-28
  • Contact: Tao WANG E-mail:longgx5@163.com;wtbridge@chd.edu.cn

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Abstract:

To further improve the existing bridge weigh-in-motion technique， this paper proposes a bridge weigh-in-motion system integrated with machine vision. Firstly， the machine vision algorithm is used to identify and track the vehicle； then， the bridge response monitoring information under the action of the vehicle is processed； furthermore， the axle load and axle base are identified by using the virtual simply-supported beam theory； finally， the method is tested by simulation and test. The results show that the method proposed in this paper has a good identification effect on the axle weight and wheelbase of vehicles under various working conditions. The average relative errors of the identification of axle weight， wheelbase and total weight are 3.40%， 4.31% and 2.71% respectively. It has a certain anti-noise ability， which shows that the method has good robustness and applicability.

Key words: bridge engineering, bridge weigh-in-motion, machine vision, virtual simply-supported beam

CLC Number: 

  • U446.2

Fig.1

Framework of novel bridge weigh-in-motion approach"

Fig.2

Vehicle recognition effect"

Fig.3

Coordinate system transformation relation"

Fig.4

Schematics of virtual simply-supported beam method"

Fig.5

Diagram of wheelbase indentification"

Fig.6

Typical cross section（cm）"

Fig.7

Arrangement of measuring points"

Fig.8

Finite element model diagram"

Table 1

Vehicle model parameter information table"

参数两轴车三轴车
车辆总重G/t16.219.6
车体质量Mv /t12.415.7
车体点头惯性矩Jyv /（kg·m2）21 670172 000
车体侧翻惯性矩Jxv /（kg·m2）5 30061 500
第1轴轴重MA1/t6.25.5
第2轴轴重MA2/t1010
第3轴轴重MA3/t-4.1
第1轴到车体重心水平距离L1/m3.74.84
第2轴到车体重心水平距离L2/m2.31.51
第3轴到车体重心水平距离L3/m-2.81

Fig.9

Identification errors under different speeds"

Fig.10

Identification errors under different RSCs"

Fig.11

Identification errors under different noise levels"

Table 2

Multi-vehicle load conditions"

工况编号加载车辆 模型加载 车道加载速度/（m·s-1）车头时 距/s
S111100.4
2210
S211100.8
2210
S311101.2
2210

Table 3

Multi-vehicle driving conditions recognition error statistics table"

工况识别参数二轴车三轴车
实际识别误差/%实际识别误差/%
S1AW1/t6.206.170.445.505.561.04
AW2/t10.009.851.5110.009.514.85
AW3/t---4.104.325.39
G/t16.2016.020.445.505.561.04
D1/m6.006.274.506.356.878.19
D2/m---1.301.0717.69
S2AW1/t6.206.271.085.505.293.77
AW2/t10.009.920.7810.0010.222.19
AW3/t---4.103.943.99
G/t16.2016.190.0719.619.450.77
D1/m6.006.183.006.356.685.20
S3D2/m---1.301.1513.04
AW1/t6.206.260.985.505.500.09
AW2/t10.009.980.1910.009.841.57
AW3/t---4.104.222.90
G/t16.2016.240.2619.6019.570.17
D1/m6.006.101.676.356.492.20
D2/m---1.301.254.00

Fig.12

Test platform"

Table 4

Vehicle model wheelbase and axle load parameters table"

参数1号车辆 模型2号车辆 模型3号车辆 模型
轴距D/m0.410.7550.755
第1轴轴重AW1/kg1517.822
第2轴轴重AW2/kg1517.822
总重G/kg3035.644

Fig.13

Arrangement of strain sensors（cm）"

Fig.14

Measured dynamic response of strain sensor"

Fig.15

Result of test car"

Table 5

Cycling test conditions table"

工况编号加载车辆模型加载车道加载速度/（m·s-1）

S1

S2

1号车10.3
1号车10.5

S3

S4

1号车10.7
2号车10.3

S5

S6

2号车10.5
2号车10.7

Fig.16

Cycling conditions recognition error statistical figure"

Table 6

Multi-vehicle test conditions table"

工况编号加载车辆模型加载车道加载速度/（m·s-1）车头时距/s
S7110.34
320.3
S8110.38
320.3
S9110.312
320.3

Fig.17

Multi-vehicle conditions recognition error statistical figure"

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