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Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

邮发代号 80-968

2019 Impact Factor: 1.68

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Frontiers of Structural and Civil Engineering  2020, Vol. 14 Issue (5): 1232-1246   https://doi.org/10.1007/s11709-020-0653-0
  本期目录
A filtering-based bridge weigh-in-motion system on a continuous multi-girder bridge considering the influence lines of different lanes
Hanli WU1,2, Hua ZHAO1(), Jenny LIU2, Zhentao HU3
1. Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan University, Changsha ‚410082, China
2. Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO ‚65409, USA
3. Qingyuan Traffic and Transportation Bureau, Qingyuan 511500, China
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Abstract：

A real-time vehicle monitoring is crucial for effective bridge maintenance and traffic management because overloaded vehicles can cause damage to bridges, and in some extreme cases, it will directly lead to a bridge failure. Bridge weigh-in-motion (BWIM) system as a high performance and cost-effective technology has been extensively used to monitor vehicle speed and weight on highways. However, the dynamic effect and data noise may have an adverse impact on the bridge responses during and immediately following the vehicles pass the bridge. The fast Fourier transform (FFT) method, which can significantly purify the collected structural responses (dynamic strains) received from sensors or transducers, was used in axle counting, detection, and axle weighing technology in this study. To further improve the accuracy of the BWIM system, the field-calibrated influence lines (ILs) of a continuous multi-girder bridge were regarded as a reference to identify the vehicle weight based on the modified Moses algorithm and the least squares method. In situ experimental results indicated that the signals treated with FFT filter were far better than the original ones, the efficiency and the accuracy of axle detection were significantly improved by introducing the FFT method to the BWIM system. Moreover, the lateral load distribution effect on bridges should be considered by using the calculated average ILs of the specific lane individually for vehicle weight calculation of this lane.
Key words： bridge weigh-in-motion    continuous bridge    fast Fourier transform    influence line    axle weight calculation
收稿日期: 2019-02-23      出版日期: 2020-11-16
Corresponding Author(s): Hua ZHAO   
 引用本文:   
. [J]. Frontiers of Structural and Civil Engineering, 2020, 14(5): 1232-1246.
Hanli WU, Hua ZHAO, Jenny LIU, Zhentao HU. A filtering-based bridge weigh-in-motion system on a continuous multi-girder bridge considering the influence lines of different lanes. Front. Struct. Civ. Eng., 2020, 14(5): 1232-1246.
 链接本文:  
https://academic.hep.com.cn/fsce/CN/10.1007/s11709-020-0653-0
https://academic.hep.com.cn/fsce/CN/Y2020/V14/I5/1232
Fig.1  
Fig.2  
Fig.3  
Fig.4  
calibration vehicle axle weights [kg (lbs)] axle spacing [m (in.)]
1st axle 2nd axle 3rd axle 4th axle GVWa) L1b) L2c) L3d)
vehicle 1 8430.0 9090.0 12250.0 12240.0 42010.0 1.850 2.850 1.350
(18585.0) (20040.0) (27006.6) (26984.6) (92616.2) (72.8) (112.2) (53.1)
vehicle 2 6800.0 7090.0 10660.0 11120.0 35670.0 1.950 3.100 1.350
(14991.4) (15630.8) (23501.3) (24515.4) (78638.9) (76.8) (122.1) (53.1)
Tab.1  
Fig.5  
Fig.6  
Fig.7  
run lane 1 lane 2 lane 3
speed (km/h) axle spacing (m) speed (km/h) axle spacing (m) speed (km/h) axle spacing (m)
L1 L2 L3 L1 L2 L3 L1 L2 L3
1 42.7 1.848 2.796 1.374 58.4 1.851 2.825 1.331 60.4 1.879 2.852 1.342
(−0.1) (−1.9) (1.8) (0.0) (−0.9) (−1.4) (1.6) (0.1) (−0.6)
2 54.5 1.848 2.818 1.333 59.2 1.842 2.862 1.316 58.8 1.830 2.843 1.307
(−0.1) (−1.1) (−1.2) (−0.4) (0.4) (−2.5) (−1.1) (−0.2) (−3.2)
3 53.6 1.845 2.798 1.369 58.8 1.830 2.810 1.340 58.4 1.818 2.825 1.331
(−0.3) (−1.8) (1.4) (−1.1) (−1.4) (−0.8) (−1.7) (−0.9) (−1.4)
4 46.6 1.839 2.824 1.399 58.8 1.863 2.843 1.340 – – – –
(−0.6) (−0.9) (3.6) (0.7) (−0.2) (−0.8) – – –
5 – – – – 58.8 1.830 2.810 1.340 61.2 1.837 2.823 1.327
– – – (−1.1) (−1.4) (−0.8) (−0.7) (−0.9) (−1.7)
6 – – – – 58.8 1.863 2.843 1.373 60.0 1.833 2.867 1.300
– – – (0.7) (−0.2) (1.7) (−0.9) (0.6) (−3.7)
7 53.3 1.834 2.781 1.361 60.0 1.867 2.833 1.367 58.4 1.818 2.825 1.331
(−0.8) (−2.4) (0.8) (0.9) (−0.6) (1.2) (−1.7) (−0.9) (−1.4)
8 51.1 1.847 2.813 1.335 59.6 1.854 2.815 1.358 60.0 1.800 2.833 1.300
(−0.2) (−1.3) (−1.1) (0.2) (−1.2) (0.6) (−2.7) (−0.6) (−3.7)
9 52.6 1.813 2.778 1.345 58.8 1.830 2.778 1.340 59.6 1.854 2.815 1.325
(−2.0) (−2.5) (−0.4) (−1.1) (−2.5) (−0.8) (0.2) (−1.2) (−1.9)
10 40.9 1.841 2.818 1.318 60.4 1.846 2.852 1.342 59.6 1.821 2.815 1.291
(−0.5) (−1.1) (−2.4) (−0.2) (0.1) (−0.6) (−1.6) (−1.2) (−4.3)
mean – 1.840 2.803 1.363 – 1.848 2.827 1.345 – 1.832 2.833 1.317
(−0.6) (−1.6) (0.3) (−0.1) (−0.8) (−0.4) (−1.0) (−0.6) (−2.4)
St. dev – 0.012 0.018 0.026 – 0.014 0.025 0.017 – 0.023 0.018 0.018
(0.6) (0.6) (2.0) (0.8) (0.9) (1.2) (1.2) (0.6) (1.3)
Tab.2  
Fig.8  
Fig.9  
Fig.10  
Fig.11  
Fig.12  
Fig.13  
run lane 1-case 1 lane 1-case 2 lane 1-case 3
GOA1 GOA2 GVW GOA1 GOA2 GVW GOA1 GOA2 GVW
A1+A2 A3+A4 A1+A2 A3+A4 A1+A2 A3+A4
1 −12.0 ??6.9 −1.0 −88.0 46.7 ?−9.5 −33.4 5.9 −10.5
2 ??1.0 ??0.6 ??0.8 −76.0 42.0 ?−7.0 −39.8 14.3? ?−8.3
3 ??2.7 −3.0 ??2.3 −51.0 33.9 ?−1.5 −14.0 5.0 ?−3.0
4 ??1.7 −0.4 ??0.5 −76.5 40.8 ?−8.1 −34.9 9.5 ?−9.0
5 – – – – – – – – –
6 – – – – – – – – –
7 ?−0.5 −2.1 −1.5 −68.2 32.2 ?−9.7 −35.0 7.0 −11.0
8 ??0.1 −3.2 −1.8 −71.9 34.1 −10.1 −31.0 3.0 −11.0
9 ?−0.8 −4.4 −2.9 −76.4 35.8 −11.0 −37.0 6.0 −12.0
10 −12.0 ??6.0 −2.0 −77.0 38.0 −10.0 −37.6 8.1 −10.9
mean −1.2 −0.7 −17.6 −8.4 −12.7 −9.5
St. dev ??5.2 ??1.7 ??57.9 ??3.0 ??21.6 2.9
Tab.3  
Fig.14  
Fig.15  
Fig.16  
run lane 2-case 1 lane 2-case 2 lane 2-case 3
GOA1 GOA2 GVW GOA1 GOA2 GVW GOA1 GOA2 GVW
A1+A2 A3+A4 A1+A2 A3+A4 A1+A2 A3+A4
1 43.2 −19.4 ?6.7 −1.0 −2.0 −1.0 14.1 −13.3 −1.9
2 – – – – – – – – –
3 37.4 ?−9.3 10.2 ??1.9 ??2.2 ??2.1 ??7.9 ?−2.5 ??1.8
4 47.4 −13.1 12.1 ??0.5 ??6.5 ??4.0 10.0 ?−1.0 ??4.0
5 43.6 ?−6.8 14.2 ??8.0 ??5.0 ??6.0 18.7 ?−2.8 ??6.2
6 40.0 −22.0 ?4.0 −8.0 −2.0 −5.0 ??5.2 −12.2 −4.9
7 40.6 −23.7 ?3.1 −5.0 −5.0 −5.0 ??9.0 −16.0 −6.0
8 41.0 −21.0 ?5.0 ??0.0 −6.0 −3.0 13.0 −16.0 −4.0
9 40.8 −10.0 11.1 ??2.4 ??4.2 ??3.5 ??9.4 ?−1.2 ??3.2
10 43.4 −20.5 ?6.1 −4.0 −1.0 −2.0 10.2 −11.3 −2.3
Mean 12.9 ?8.1 −0.2 −0.1 ?1.2 −0.4
St. dev 30.3 ?3.9 ??4.5 ??4.1 11.2 ??4.4
Tab.4  
run lane 3-case 1 lane 3-case 2 lane 3-case 3
GOA1 GOA2 GVW GOA1 GOA2 GVW GOA1 GOA2 GVW
A1+A2 A3+A4 A1+A2 A3+A4 A1+A2 A3+A4
1 28.0 −15.2 ?2.8 34.7 −33.7 −5.2 ??2.1 −11.1 −5.6
2 17.5 ???9.3 12.7 26.4 −10.2 ??5.1 ??5.2 ??5.0 ??5.1
3 – – – – – – – – –
4 – – – – – – – – –
5 – – – – – – – – –
6 30.1 −10.5 ?6.4 30.9 −24.8 −1.6 ?−5.0 ??0.0 −2.0
7 24.0 ?−4.0 ?7.0 20.5 −15.4 −0.5 −14.0 ??9.0 −1.0
8 24.0 ?−8.0 ?5.0 32.0 −27.0 −3.0 ??7.0 −10.0 −3.0
9 33.4 ?−2.9 12.2 40.0 −21.0 ??4.0 ??8.0 ??1.0 ??4.0
10 23.0 ?−8.0 ?5.0 34.0 −30.0 −3.0 ??1.0 ?−7.0 −4.0
mean 10.0 ?7.3 ?4.0 −0.6 −0.6 −0.9
St. dev 17.5 ?3.8 29.1 ??3.8 ??7.5 ??4.0
Tab.5  
run lane 1 lane 2 lane 3
GOA1 GOA2 GVW GOA1 GOA2 GVW GOA1 GOA2 GVW
A1+A2 A3+A4 A1+A2 A3+A4 A1+A2 A3+A4
1 – – – −4.3 −7.5 −6.3 −2.3 ??1.2 −0.2
2 – – – ??0.9 −7.3 −4.1 ??7.2 ??4.4 ??5.5
3 – – – ??0.9 ??7.4 ??4.9 ?22.2 −4.1 ??6.1
4 ?10.7 −19.9? −8.0 – – – ??7.8 −12.7? −4.7
5 −4.0 −8.1 −6.5 ??5.1 ??6.2 ??5.8 −1.6 −9.6 −6.5
6 ??0.5 −5.2 −3.0 −0.2 −10.6?? −6.6 ??5.0 −12.7? −5.8
7 ??3.4 ??6.7 ??5.4 – – – −5.7 −5.7 −5.7
8 −1.0 ?10.9 ??6.2 −4.0 −4.2 −4.1 −5.6 ??9.2 ??3.4
9 ??2.9 ??4.4 ??3.8 ??2.4 ??7.2 ??5.3 ?17.3 ??0.9 ??7.3
10 ??4.5 ??9.6 ??7.6 −4.9 −6.6 −5.9 −1.1 −3.2 −2.4
mean 1.1 ??0.8 −1.2 −1.4 0.5 −0.3
St. dev 8.4 ??6.5 ??5.8 ??5.6 9.1 ??5.5
Tab.6  
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