weigh-in-motion on bridges
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WEIGH-IN-MOTION ON BRIDGES
Aleš Žnidarič
ZAG - Slovenian National Building and Civil Engineering Institute
Ljubljana, Slovenia
What is Bridge WIM?
B-WIM is a device that uses instrumented road structures – bridges or culverts – to weigh vehicles in motion.
3 facts: ■ involves existing instrumented structures
■ can be used as any other WIM system
■ gives extra information for bridge assessment
Bridge WIM
3 periods: 1. Before 1994: US B-WIM system, CULWAY 2. 1994-2000 EU research projects:
– COST 323 (Weigh-in-motion of road vehicles) – WAVE (Weighing-in-motion of axles and vehicles for
Europe)
3. After 2000: – SiWIM – developments of new algorithms – Japan, Korea
B-WIM before 1994
□ born in 1979 by F. Moses (CWRU, Cleveland, USA) □ BWS, CULWAY □ limitations:
■ limited types of bridges ■ poor axle weight accuracy ■ multiple presence of vehicles
□ advantages: ■ accurate GVW results ■ no ‘off-scale’ weighing ■ easy instrumentation with little disturbance ■ portability ■ for bridge assessments
B-WIM during European projects
□ objectives: ■ to increase their accuracy
■ to extend their applicability to: □ short slab bridges
□orthotropic deck bridges
□ longer span box girder bridges
■ to develop new algorithms
B-WIM today
□ a fully operational SiWIM system used on >1000 sites in 16 countries
□ main features: ■ accuracy, including SA and AG ■ connectivity, cameras etc. ■ portability ■ easy and fast installation ■ free-of-axle detector (FAD) installation ■ bridge assessment
□ Australia, Japan…
BWIM shema
Strain measurements Axle detection
ST500 Strain transducer
ST-500 S.N. 10122150100001
R R ST 500 SN 10122150100001
220
30 85 90 15
13
Calibration
With pre-weighed vehicles
With vehicles from traffic flow
2.5 + 3.0-m integral culvert
Canada
9-m long, integral-type slab bridge
France
Millau bridge
France
292 m long, 9-span beam-deck bridge
Oman
12+20+12 m long, 3-span beam-deck underpass
USA
SiWIM features
1. Influence lines
2. FAD – Free-of-axle Detector installation
3. Multiple presence of vehicles on the bridge
4. Influence of pavement roughness and calibration methods
1. Influence lines
□ key structural parameter that is directly related to the quality of B-WIM measurements
□ only measured/experimental influence lines
□ SiWIM calculates measured IL from any vehicle: ■ Powell’s multidimensional non-linear optimisation
■ accounts for: □ shape of the influence line
□ axle load ratio
■ several ILs are averaged
1. SiWIM influence lines
IL = Response of a structure under a moving load
2. FAD – Free-of-Axle Detector installation
□ key advantage of B-WIM systems: ■ no disturbance of traffic
■ fast and easy installation and maintenance
■ improved portability
□ not all bridges are appropriate for FAD
□ over 75% of installations in 2009 were FAD
2. FAD – Free-of-Axle Detector installation
2. FAD – Free-of-Axle Detector installation
Installation with axle detectors
2. FAD – Free-of-Axle Detector installation
Installation without axle detectors
2. FAD – Free-of-Axle Detector installation
3. Multiple presence (MP) of vehicles
□ B-WIM measures global effects thus MP is a challenge □ options:
■ selection of shorter bridges ■ measurements of local effects ■ implementation of 2D influence surface instead of IL =>
demanding calibration
□ applied solution: STRIPs ■ grouping of sensors under each lane ■ larger systems of equations but ■ unchanged calibration procedure
3. Multiple presence (MP) of vehicles
GVW Axle 1 Axle 2 Multiple
3-axle static (t) 20,56 5,16 15,4 5-axle static (t) 33,90 7,24 8,94 17,72
3-axle alone* 0,7% -2,6% 1,8% 5-axle alone** 0,0% 0,6% 3,4% -2,0%
3-axle MP 2,7% -36,2% 15,7% 5-axle MP -6,7% 7,2% -11,2% -10,2%
3-axle MP strips 2,3% 0,2% 3,0% 5-axle MP strips -0,1% 5,8% 3,2% -4,1%
Strip example: 32-m simply supported span, 2-lanes, opposing traffic
* average of 6 vehicle runs ** average of 4 vehicle runs
□ although less than pavement WIM systems, B-WIM is affected by pavement roughness
□ higher level calibration (per vehicle type and axle configuration) improves accuracy of the results
4. Pavement roughness and calibration
E(40)
D+(20)
E(30)
B(10)
Other features
□ Cameras (with LPR) – pre-selection
□ Solar or fuel-cell power supply
□ Remote controlled
□ Post-processing of data
□ Synchronised measurements between axle loads and other quantities (bridge monitoring)
Conclusions on BWIM
□ major developments in recent years □ main advantages:
■ complete portability ■ no interference with traffic ■ accuracy ■ bridges are difficult to avoid
□ disadvantages: ■ an appropriate bridge is needed (MP, FAD) ■ requires well-trained personnel (bridges can be
damaged or unusual)
Conclusions
□ key challenges: ■ Increase FAD axle identification on all types of
bridges to over 99%: □measurements other than bending strains
□new algorithms (Wavelet transformation)
■ inclusion of external algorithms
■ facilitation of its use
□ SiWIM III is coming…
BWIM AND ITS APPLICATIONS IN BRIDGE ASSESSMENT
Why SiWIM in bridge assessment?
□ It provides the same data as pavement WIM systems, plus: ■ can be used on a specific bridge to improve its
assessment
■ the only system that directly compares bridge loading with load effects
■ in addition to weighing sensors it can perform synchronised measurements with any other sensor
Safety assessment
□ verification that a structure has adequate capacity to safely carry or resist specific loading
□ Ultimate Limit State approach:
Rating factor:
AALLDDR
d GGGRSR γγγγ
⋅+⋅+⋅≥≥
DAFGGRRF
LL
DDd
×××−×Φ
=γ
γ>1.0
35
Traffic load effects
Structural behaviour ■ Influence lines
■ Load distribution factors
Loading schemes ■ Design
■ Assessment
■ Site-specific
2- axle trucks
3- axle trucks Trailers Semi-trailers Buses Others All vehicles
Traffic load modelling
Input: traffic data (axle loads, spacings), headways
2-axle trucks 3-axle trucks Trailers Semi-trailers Buses Others All vehicles
Vransko (25.9.2006 14:59:18 to 21.11.2006 12:27:19) - Lane 1
Traffic load modelling
Simulation of maximum expected load effects
0102030405060708090
100
Total moment of two vehicles [kNm]
event - 1011 kNm
1 day - 1695 kNm
1 week - 1915 kNm
1 month - 2102 kNm
1 year - 2424 kNm
5 years - 2671 kNm
10 years - 2759 kNm
25 years - 2849 kNm
50 years - 2910 kNm
75 years - 2947 kNm
100 years - 2973 kNm
Expected maximum traffic loading
0102030405060708090
100
Total shear of two vehicles [kN]
event - 323 kN
1 day - 523 kN
1 week - 582 kN
1 month - 628 kN
1 year - 707 kN
5 years - 774 kN
10 years - 801 kN
25 years - 832 kN
50 years - 851 kN
75 years - 861 kN
100 years - 868 kN
Expected maximum traffic loading
Traffic load modelling
Simulation of maximum expected load effects
Load testing
Loading of the bridge to learn about its behaviour in order to: 1. know how loading is converted to load effects 2. optimise bridge assessment by finding reserves
in structural model Types of load test: ■ proof ■ diagnostic ■ soft
Soft load testing
□ the lowest level of load application □ uses bridge WIM to provide:
■ “normal” traffic data ■ information about structural behaviour bridge ■ “quick&cheap”:
□no need for pre-weighed vehicles □no need to close the traffic
□ no risk of overloading/damaging of structure □ not suitable for ULS verifications
□ 9.2 m slab bridge
□ built in 1970‘s
□ simply supported
Experimental influence lines
557.3 kNm
□ 9.2 m slab bridge
□ built in 1970‘s
□ not simply supported
Experimental influence lines
557.3 kNm
□ 9.2 m slab bridge
□ built in 1970‘s
□ not simply supported
Experimental influence lines
260.0 kNm
□ 9.2 m slab bridge
□ built in 1970‘s
□ not simply supported
□ influence of thickness of the superstructure
Experimental influence lines
244.3 kNm M = 44% × MTHEOR
Experimental influence lines
250.9 kNm
□ 9.2 m slab bridge
□ built in 1970‘s
□ not simply supported
□ influence of thickness of the superstructure
□ influence of thickness of the superstructure
□ location of maximum moment
Experimental influence lines
□ considerable decrease in bending moments
□ some increase in shear
Load distribution factors
□ distribution of traffic loading to different structural elements Lane Factors
□ generally less dependent of wheel location at midspan than at support
□ to update torsional characteristics of the model
Dynamic loading on bridges
□ DAF: ratio between maximum (dynamic) and static loadings
□ problem: combining the extremes of dead load and dynamic effects => very high DAF
□ options: ■ DAF from (design) codes – conservative ■ realistic values (SAMARIS and ARCHES projects):
□ theoretical studies on extreme expected DAF (UCD) □measurements of thousands DAFs with SiWIM (ZAG)
Dynamic amplification
1 day = 4120 events
DAF – 24.8 m beam/deck bridge
DAF – 11.65 m slab
DAF – 7×25.0m beam/deck bridge
Result of using BWIM
□ Realistic traffic load model □ Realistic DAF □ SLT – optimised influence lines and load factors □ Higher reliability of inputs
DAFGGRRF
LL
DDd
×××−×Φ
=γ
γ
Soft load testing results
□ 20 posted pre-analysed bridges: ■ without SLT RF from 0.35 to 0.87
■ with SLT RF from 0.73 to 1.52
■ increase of RF from 1.30× to 2.84×
□ 18 bridges rated as safe for normal traffic loads after applying SLT
□ biggest savings on smaller older bridges with questionalbe boundary conditions
Conclusions – Bridge Assessment
B-WIM measurements can be extremely efficient when updating the structural model: □ Structural behaviour:
experimental (measured) influence lines, load distribution factors and dynamic amplifications can significantly differ from the theoretical ones
□ Traffic loading generally « than in theory: ■ site-specific static load models ■ measured DAF will likely be below 1.10
ARCHES 2009, arches.fehrl.org
SiWIM in Brazil
5 fixed installations for DataTax Pre-checking of weights of vehicles on the borders between the states at normal speed, to confirm the weights with the documents
Thanks for listening!
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