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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2020, Vol. 14 Issue (5) : 1232-1246     https://doi.org/10.1007/s11709-020-0653-0
RESEARCH ARTICLE
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.

Keywords bridge weigh-in-motion      continuous bridge      fast Fourier transform      influence line      axle weight calculation     
Corresponding Author(s): Hua ZHAO   
Just Accepted Date: 10 July 2020   Online First Date: 08 September 2020    Issue Date: 16 November 2020
 Cite this article:   
Hanli WU,Hua ZHAO,Jenny LIU, et al. A filtering-based bridge weigh-in-motion system on a continuous multi-girder bridge considering the influence lines of different lanes[J]. Front. Struct. Civ. Eng., 2020, 14(5): 1232-1246.
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http://journal.hep.com.cn/fsce/EN/10.1007/s11709-020-0653-0
http://journal.hep.com.cn/fsce/EN/Y2020/V14/I5/1232
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Hanli WU
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Fig.1  The field installation of BWIM system.
Fig.2  The photo of the test span of Lunzhou Bridge.
Fig.3  The elevation of the instrumented bridge and sensor positions (unit: cm).
Fig.4  Sensors positions on the test span of Lunzhou Bridge; (a) Sensors positions on the plane layout; (b) Sensors positions on the cross section (unit: cm).
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  The vehicle information for the initial measurement
Fig.5  The vehicle for the initial calibration of the test span.
Fig.6  The original FAD signals and the FFT treated FAD signals.
Fig.7  The axle identification with FAD sensors.
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  The axle spacing identification and calculated vehicle speeds
Fig.8  The influence line ordinates at time step k.
Fig.9  The calculated ILs of lane 1 (North−South).
Fig.10  The calculated ILs of lane 2 (South−North).
Fig.11  The calculated ILs of lane 3 (South−North).
Fig.12  Comparison of the averaged ILs of lanes 1, 2, and 3.
Fig.13  Comparison of the field measured strains and the predicted strains.
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  The errors of identified axle weights for calibration vehicle on lane 1 (%)
Fig.14  The error box chart of axle weights calculation of lane 1.
Fig.15  The error box chart of axle weights calculation of lane 2.
Fig.16  The error box chart of axle weights calculation of lane 3.
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  The errors of identified axle weights for calibration vehicle on lane 2 (%)
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  The errors of identified axle weights for calibration vehicle on lane 3 (%)
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  The errors of identified axle weights for vehicle 2 with using the vehicle 1 calibrated influence line from each lane (%)
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