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Frontiers of Structural and Civil Engineering

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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.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-020-0653-0
https://academic.hep.com.cn/fsce/EN/Y2020/V14/I5/1232
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 (%)
1 J C Pais, S I Amorim, M J Minhoto. Impact of traffic overload on road pavement performance. Journal of Transportation Engineering, 2013, 139(9): 873–879
https://doi.org/10.1061/(ASCE)TE.1943-5436.0000571
2 D Rys, J Judycki, P Jaskula. Analysis of effect of overloaded vehicles on fatigue life of flexible pavements based on weigh in motion (WIM) data. International Journal of Pavement Engineering, 2016, 17(8): 716–726
https://doi.org/10.1080/10298436.2015.1019493
3 Y Yu, C S Cai, L Deng. State-of-the-art review on bridge weigh-in-motion technology. Advances in Structural Engineering, 2016, 19(9): 1514–1530
https://doi.org/10.1177/1369433216655922
4 T Ojio, C H Carey, E J O’Brien, C Doherty, S E Taylor. Contactless bridge weigh-in-motion. Journal of Bridge Engineering, 2016, 21(7): 04016032
https://doi.org/10.1061/(ASCE)BE.1943-5592.0000776
5 H Zhao, N Uddin, E J O’Brien, X Shao, P Zhu. Identification of vehicular axle weights with a bridge weigh-in-motion system considering transverse distribution of wheel loads. Journal of Bridge Engineering, 2014, 19(3): 04013008
https://doi.org/10.1061/(ASCE)BE.1943-5592.0000533
6 H Zhao, N Uddin, X Shao, P Zhu, C Tan. Field-calibrated influence lines for improved axle weight identification with a bridge weigh-in-motion system. Structure and Infrastructure Engineering, 2015, 11(6): 721–743
https://doi.org/10.1080/15732479.2014.904383
7 M Lydon, S E Taylor, D Robinson, A Mufti, E J O Brien. Recent developments in bridge weigh in motion (B-WIM). Journal of Civil Structural Health Monitoring, 2016, 6(1): 69–81
https://doi.org/10.1007/s13349-015-0119-6
8 R Snyder, F Moses. Application of in-motion weighing using instrumented bridges. Transportation Research Record: Journal of the Transportation Research Board, 1985, 1048: 83–88
9 M S Park, J Lee, S Kim, J H Jang, Y H Park. Development and application of a BWIM system in a cable-stayed bridge. In: Proceedings of the 3rd International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-3). Vancouver: International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII), 2007, 13–16
10 E J O’Brien, A Znidaric, T Ojio. Bridge weigh-in-motion—Latest developments and applications worldwide. In: Proceedings of the International Conference on Heavy Vehicles. Paris: John Wiley & Sons, Inc., 2008, 19–22
11 F Schmidt, B Jacob, C Servant, Y Marchadour. Experimentation of a bridge WIM system in France and applications for bridge monitoring and overload detection. In: The 6th International Conference on Weigh-In-Motion (ICWIM6). Dallas: International Society for Weigh in Motion (ISWIM), 2012
12 M Lydon, D Robinson, S E Taylor, G Amato, E J O Brien, N Uddin. Improved axle detection for bridge weigh-in-motion systems using fiber optic sensors. Journal of Civil Structural Health Monitoring, 2017, 7(3): 325–332
https://doi.org/10.1007/s13349-017-0229-4
13 H Sekiya, K Kubota, C Miki. Simplified portable bridge weigh-in-motion system using accelerometers. Journal of Bridge Engineering, 2018, 23(1): 04017124
https://doi.org/10.1061/(ASCE)BE.1943-5592.0001174
14 R J Peters. AXWAY—A system to obtain vehicle axle weights. In: Australian Road Research Board Conference. Hobart: ARRB Group Limited, 1984
15 R J Peters. CULWAY, an unmanned and undetectable highway speed vehicle weighing system. In: Australian Road Research Board Proceedings. Adelaide: Australian Road Research Board (ARRB), 1986, 70–83
16 E J O’Brien, A Znidaric, W Baumgärtner, A González, P McNulty. Weighing-In-Motion of Axles and Vehicles for Europe (WAVE) WP1. 2: Bridge WIM Systems. Dublin: University College Dublin, 2001
17 B Jacob, E J O’Brien, S Jehaes. Weigh-in-motion of Road Vehicles: Final Report of the Cost 323 Action. Laboratoire Central des Ponts et Chausees (LCPC), 2002
18 F Moses. Weigh-in-motion system using instrumented bridges. Journal of Transportation Engineering, 1979, 105(3): 233–249
19 E Yamaguchi, S I Kawamura, K Matuso, Y Matsuki, Y Naito. Bridge-Weigh-in-Motion by two-span continuous bridge with skew and heavy-truck flow in Fukuoka area, Japan. Advances in Structural Engineering, 2009, 12(1): 115–125
https://doi.org/10.1260/136943309787522614
20 E O’Brien, P Favai, R Corbally. Multiple-Equation Bridge Weigh-in-Motion. In: Proceedings of 2013 IABSE Symposium. Kolkata: International Association for Bridge and Structural Engineering (IABSE), 2013, 99(12): 1298–1305
21 Z Zhao, N Uddin, E J O’Brien. Bridge weigh-in-motion algorithms based on the field calibrated simulation model. Journal of Infrastructure Systems, 2017, 23(1): 04016021
https://doi.org/10.1061/(ASCE)IS.1943-555X.0000308
22 T Ojio, K Yamada. Bridge WIM by reaction force method. In: The 4th International Conference on Weigh-In-Motion. Taipei, China: International Society for Weigh in Motion (ISWIM), 2005
23 T Bao, S K Babanajad, T Taylor, F Ansari. Generalized method and monitoring technique for shear-strain-based bridge weigh-in-motion. Journal of Bridge Engineering, 2016, 21(1): 04015029
https://doi.org/10.1061/(ASCE)BE.1943-5592.0000782
24 A Znidaric, I Lavric, J Kalin. The next generation of bridge weigh-in-motion systems. In: The Third International Conference on Weigh-in-Motion (ICWIM3). Ames: Iowa State University, 2002, 219–229
25 P McNulty, E J O’Brien. Testing of bridge weigh-in-motion system in a sub-Arctic climate. Journal of Testing and Evaluation, 2003, 31(6): 497–506
26 E J O’Brien, M Quilligan, R Karoumi. Calculating an influence line from direct measurements. Proceedings of the Institution of Civil Engineers-Bridge Engineering, 2006, 159(BE1): 31–34
27 E J O’Brien, A González, J Dowling. A filtered measured influence line approach to bridge weigh-in-motion. In: The Fifth International IABMAS Conference: Bridge Maintenance, Safety Management and Life-Cycle Optimization. Philadelphia: Taylor & Francis (Routledge), 2010
28 A Znidaric, A Dempsey, I Lavric, W Baumgaertner. Bridge WIM systems without axle detectors. Weigh-in-Motion of Road Vehicles, WAVE Symposium, Final. Paris, France, 1999, 388–397
29 J Richardson, S Jones, A Brown, E J O’Brien, D Hajializadeh. On the use of bridge weigh-in-motion for overweight truck enforcement. International Journal of Heavy Vehicle Systems, 2014, 21(2): 83–104
https://doi.org/10.1504/IJHVS.2014.061632
30 R C Hibbeler. Structural Analysis. 7th ed. New Jersey: Pearson Prentice Hall, 2009
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