Civil Structural Health Monitoring 2, Taormina, Sicily (Italy)

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1 Determination of Live Load Factors for Reliability-Based Bridge Design and Evaluation using WIM Data Koh, Hyun-Moo Department of Civil Engineering Seoul National University, Korea Hwang, Eui-Seung Department of Civil Engineering Kyung Hee University, Korea

2 Contents 1. Research Backgrounds 2. Data Collection using WIM System 3. Determination of Live Load Factors for Design 4. Determination of Live Load Factors for Evaluation 5. Summary, Conclusions and Further Study

3 1. RESEARCH BACKGROUNDS Live load model is out-dated current model was adopted in 1978 current model did not consider traffic situation of the nation Reliability-based based bridge design code is being developed to conform with global standard rational design methodology state-of-art technology prevent too conservative design consider local environments Reliability-based based bridge evaluation procedure is being also developed to be consistent with reliability-based based design

4 Korea Bridge Design & Engineering Research Center Program for 5-Year Plan of Construction Technology Innovation, Ministry of Land, Transport and Maritime Affairs (2003) To strengthen R&D programs and improve R&D management system Advanced Bridge Design and Engineering Technology selected for immediate funding : Establishment of KBRC

5 Strategic Objectives of KBRC Research directions of KBRC : Practicability & Global Standard 400 members from 36 universities, 26 companies and 2 government-sponsored research institutes

6 2. DATA COLLECTION BY WIM SYSTEM WIM System

7 BWIM System

8 NR39 Bibong ADT=48,256 ADTT=23,352 ADTT(L)=1,177 NoData=11,489 HW15 Dogok Br ADT=55,346 ADTT=13,796 ADTT(L)=1,313 NoData=23,221 NR45 Dongchun Br ADT=13,359 ADTT=4,336 ADTT(L)=84 NoData=5,539 Data Collection Location PR56 Eunhyun Br ADT=20,405 ADTT=8,555 ADTT(L)=1,828 NoData=3,564 NR42 Munmak ADT=16,438 ADTT=5,318 ADTT(L)=244 NoData=10,687 NR20 Pohang ADT=10,425 ADTT=6,580 ADTT(L)=3,306 NoData=29,394 HW1 MaeBong Br ADT=118,232 ADTT=32,606 ADTT(L)=4,611 NoData=10,561 HW1 Songpo Br ADT=46,185 ADTT=20,988 ADTT(L)=6,356 NoData=94,721

9 Histogram of total truck weight Bibong Pohang 비봉전체 포항전체 Frequency (%) Total Weight (kn)

10 3. LIVE LOAD FACTORS FOR DESIGN Procedure to develop live load model Data Collection Determine main truck types, typical dimensions Determine probability distribution function of total weight Estimate Maximum total weights Calculate effects of single truck Calculate effects of multiple trucks Nominal Load Model Load Factors

11 Typical trucks in Korea (a) Code 40 (b) Code 50 (c) Code 60 (d) Code 70 (e) Code 91

12 Plots on Normal Probability Paper

13 Estimation of Maximum Weights of Single Truck Assume that upper portion(10% or 20%) of truck weight data has Gumbel distribution W max = µ + σ ln ( N ) where, µ : the mean of truck weight (kn kn) σ : standard deviation of truck weight (kn) N : the number of trucks in specified years N = y 365 ADTT p m ADTT : the volume of average daily truck traffic p : 0.1 or 0.2 m : mixed proportion of specific truck type Bridge lifetime(y) is assumed as 100 years

14 Plots of upper data on Gumbel Probability Paper Pohang (GPP - Code 91) - Upper 10%, 20% % y = 0,035x - 21,476 Inverse Gumbel % y = x Weight (kn)

15 Inverse Gumbel Pohang - Code years 75-years 50-years 5-years 1-year Weight (kn)

16 Code 91 Area Pohang Percentage of Truck (%) No. of Trucks 624 Slope of Linear Regression Line Y-axis intercept of Linear Regression Line Return Period 1 year inverse Gumbel Total weight(kn) Return Period 5 years inverse Gumbel Total weight(kn) Return Period 50 years inverse Gumbel Total weight(kn) Return Period 75 years inverse Gumbel Total weight(kn) Return Period 100 years inverse Gumbel Total weight(kn)

17 Code 70 Area 10% 20% GPP NPP GPP NPP Songpo Eunhyun Dongchun Dogok Maebong Pohang Munmak Bibong Average C.O.V

18 Code 91 Area 10% 20% GPP NPP GPP NPP Songpo Eunhyun Dongchun Dogok Maebong Pohang Munmak Bibong Average C.O.V

19 Load effects of different truck types ** Moment Ratio is the ratio of midspan moment by the maximum weight truck divided with current design load

20 Effects two or more trucks in a lane Probability of two or more trucks in a lane video analysis Headway distance is 5 m Area No correlation probability Full correlation Remarks Suwon (Highway) 1/10 1/100 congestion Suwon (Highway) 1/70 1/350 normal traffic Nowak s value 1/50 1/500 used in this study 1/50 1/250

21 Effects two or more trucks in a lane Estimated maximum weights of two or more trucks in a lane Number Multiple Trucks Type Code 70 Code No correlation Full correlation N-N N-F F-F N-N-N F-F-F

22 Effects two or more trucks in a lane Moment Ratio 3,5 3 2,5 2 1,5 1 1 truck 2 trucks 3 trucks 4 trucks 0, Span (m)

23 Effects of headway distance between trucks Headway distance 5m = congestion = no dynamic effects Mean of dynamic load effect is assumed as 10%

24 Nominal load model 192 kn 48 kn 192 kn OR 108 kn for moment 156 kn for shear 12.7 kn/m Truck Load (DB 24) Lane Load (DL 24) 192kN 48kN 4.2m 4.2m 192kN 9.6kN/m TL-240

25 Statistics of live load Mean-to-Nominal ratio = 1.0 ~ 1.1 Compare the mean load effect and nominal load effect Coefficient of variation c.o.v. of distribution function = 7% variation by location = 10% variation of dynamic load = 10% variation of structural analysis =10% TL-240 COV Live load factor = = 0.19 V L = 20% γ i λ ( 1+ kv ) 1.45 ~ 1.58 = i i γ L = 1.75

26 Load model vs. truck effect Moment Ratio 1,8 1,6 1,4 1,2 1 0,8 0,6 0,4 0, Load Model Truck Effect Shear Ratio 1,8 1,6 1,4 1,2 1 0,8 0,6 0,4 0,2 0 Negative Moment Ratio 2 1,8 1,6 1,4 1,2 1 0,8 0,6 0,4 0,

27 Effects of side-by-side trucks Probability of side-by-side trucks Video analysis Area No correlation Probability Full correlation Remarks Suwon 1/10 1/50 congestion Bibong 1/20 1/1,200 Pohang 1/25 1/2,250 Nowak s value 1/15 1/450

28 Probability of side-by-side trucks No. of Truck Pohang Bibong Suwon Nowak Full Correlation 1/25 1/20 1/10 1/15 No correlation 1/2,250 1/1,200 1/50 1/450 N-N 1/625 1/400 1/100 1/225 F-N N-F 1/56,250 1/24,000 1/500 1/6,750 F-F 1/5,062,500 1/1,440,000 1/2,500 1/202,500 N-N-N 1/15,625 1/8,000 1/1,000 1/3,375 N-F-N 1/1,406,250 1/480,000 1/5,000 1/101,250 F-F-N 1/126,562,500 1/28,800,000 1/25,000 1/3,037,500 F-F-F 1/11,390,625,000 1/1,728,000,000 1/125,000 1/91,125,000 N-N-N-N 1/390,625 1/160,000 1/10,000 1/50,625 F-N-N-N 1/35,156,250 1/9,600,000 1/50,000 1/1,518,750 F-F-N-N 1/3,164,062,500 1/576,000,000 1/250,000 1/45,562,500 F-F-F-N 1/284,766,000,000 1/34,560,000,000 1/1,250,000 1/1,366,875,000 F-F-F-F 1/25,628,900,000,000 1/2,073,600,000,000 1/6,250,000 1/41,006,250,000

29 Total weights of side-by-side trucks No. of trucks and cases Area Lane1 Lane2 Lane3 Lane4 Lane5 Pohang 1051 Average = 613 Bibong 1138 Average = 472 N 25 Pohang Bibong F 2,250 Pohang Bibong N-N 625 Pohang Bibong N-F 56,250 Pohang Bibong F-F 5,062,500 Pohang Bibong N-N-N 15,625 Pohang Bibong N-N-F 1,406,250 Pohang Bibong N-F-F 126,562,500 Pohang Bibong F-F-F 11,390,625,000 Pohang Bibong N-N-N-N 390,625 Pohang Bibong F-F-F-F 25,628,900,000,000 Pohang Bibong

30 Load effects of side-by-side trucks 2 lanes and 5 lanes bridge

31 Multiple presence factor Loading Lane(s) Mean Max Min Proposed

32 4. LIVE LOAD FACTORS FOR EVALUATION Differences between design and evaluation : specific bridge condition and environment use the same total weight distribution as in design specific bridge loading condition : ADTT Inspection Interval : 5 years ( vs. 100 years in design) Number of trucks (ADTT) affects probabilities of multiple presences.

33 Inspection Interval Example for estimation of extreme truck weights in Pohang (1)5-year (2)100-year

34 Total truck weights for 5 years and 100 years Location S E D P M B Ave. Code 70 Code 91 ADTT 9,424 2,316 1,266 3,469 2,978 7,506 4, years COV years years 1,080 1,217 1,261 1,051 1,341 1,138 1, years 965 1,030 1, ,165 1,002 1, Reduction factor = 0.9

35 ADTT on Multiple Presence Factor Estimate probability of multiple presence as a function of ADTT. Nowak s value : ADTT = 5,000, P s/s = 1/15 Moses and Ghosn : P s/s = ADTT Later, Moses proposed: Data from video analysis ADTT = 5,000, P s/s = 1/15 ADTT = 1,000, P s/s = 1/100 ADTT = 100, P s/s = 1/1,000 Location ADTT P s/s P 3,469 1/25 B 7,506 1/20 SW 13,589 1/10

36 The linearly fitted function of ADTT for side-by-side probability is determined and proposed as in Eq.(4) P P s/s vs. ADTT 5 s / s = ADTT (4)

37 Truck weights for different ADTT ADTT Code S E D P M B 100 1,000 5,000 Ave. Weight Reduc. factor Ave. Factor , ,

38 Statistical difference of live load Coefficient of variation for evaluation design c.o.v. of distribution function = 7% variation by location = 10% Variation of dynamic load = 10% variation of structural analysis =10% 5% COV = = 0.19 V L = 20% γ L = COV = = 0.13 V L = 15% γ L = 1.63

39 Proposed live load factors for evaluation ADTT 100 1,000 5,000 10,000 15,000 In AASHTO LRFR: γ The live load factor for strength evaluation: γ = 1.75 at Inventory rating γ = 1.35 at Operating rating For Legal load rating: the load factor varies from 1.40 (ADTT 100) to 1.80 (ADTT 5,000)

40 5-1. SUMMARY By collecting the WIM data from different areas in Korea, new live load model and load factors are proposed for reliability-based based bridge design code To keep up with design code development and have consistent theoretical background, live load factors for evaluation are being developed considering evaluation interval, ADTT and statistical difference.

41 5-2. CONCLUSIONS Nominal load model, corresponding load factors and statistical properties of live load effects are calculated and proposed. Live load factors for evaluation is different from design due to various factors and they varies from 1.21 to 1.54 depending of the volume of ADTT,, compared with the design load factors of 1.75.

42 5-3. FURTHER STUDIES New load models are under review by other researchers and practitioners. Design comparison is underway. Several evaluation procedure is being developed. Full probabilistic, semi-probabilistic and rating factor methods are considered. More data are needed. Nation-wide network of WIM system should be implemented to collect long-term, reliable and comprehensive heavy truck data for better design and evaluation of bridges.

43 Thank you very much!