A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges

Öz

Obtaining the side-by-side probabilities accurately is a very important procedure during two lane loaded live load factor analysis. To calculate the load factors properly, side-by-side loading events should be investigated very carefully. This study presents a statistical method to investigate the side-by-side events on the Oregon bridges. Numerical simulations were performed for this investigation. These simulations were developed in MATLAB. Gross vehicle weights (GVW) of the trucks were used during the analysis. Monte Carlo simulations were performed to analyze side-by-side loading events. Degree of correlation coefficient of GVW for side-by-side trucks were also obtained from Monte Carlo simulations. 290 bridges located at the prescribed mile markers on Interstate-5 (I-5) southbound on Oregon highways and 1-year of Oregon state-specific weigh-in-motion (WIM) data were used. 75,000 trucks were randomly selected from 1,787,612 trucks that correspond to 1-year WIM data from Woodburn NB traffic site that is located in Oregon. Inverse standard normal distribution functions and cumulative distribution functions of the truck data were generated. With respect to the statistical analysis, side-by-side loading probabilities were found to be smaller than the ones presented in American Association of State Highway and Transportation Officials LRFD calibration. 

Anahtar Kelimeler

• Bridge,civil engineering,highway,monte carlo simulation,side-by-side loading

Kaynakça

1. Moses, F (2001). “Calibration of Load Factors for LRFR Bridge Evaluation.”NCHRP Report 454, Transportation Research Board, National Research Council, Washington, D.C.3. Strunk JrW, White EB. The Elements of Style, fourth ed. Longman, New York, 2000.
2. AASHTO. (2008). The Manual For Bridge Evaluation First Edition, Washington, D.C.
3. AASHTO-LRFD Bridge Design Specifications. (2012). Customary U.S. Units, American Association of State Highway and Transportation Officials, Washington, D.C.
4. AASHTO (2003). Manual for Condition Evaluation and Load and Resistance Factor Rating (LRFR) of Highway Bridges, AASHTO, Washington, D.C.
5. Pelphrey, J., Higgins, C., Sivakumar, B., Groff, R.L., Hartman, B.H., Charbonneau, J.P., Rooper, J.W. and Johnson B.V. (2008). “State-Specific LRFR Live Load Factors Using Weigh-in-Motion Data”, Journal of Bridge Engineering-ASCE, 13, 339-350.
6. Zhou, J, Shi, X, Caprani, CC, Ruana, X, Multi-lane factor for bridge traffic load from extreme events of coincident lane load effects. Structural Safety 72 (2018): 17-29.
7. Yang, X.Y., Gong, JX , Xu, BH , Zhu, JC , Evaluation of multi-lane transverse reduction factor under random vehicle load. Computer and Concrete, (2017) 19(6) ,725-736.
8. Yanik, A. , Higgins, C. and Borello, D. (2015). Development of live load factors for rating of Oregon bridges using WIM load effects and statistical bridge models. Technical Report for Oregon Department of Transportation.

9. Millam, Jason, and Zhongguo Ma. Single-lane live load distribution factor for decked precast, prestressed concrete girder bridges. Transportation Research Record: Journal of the Transportation Research Board 1928 (2005): 142-152.
10. Enright, B., & O'Brien, E. J. (2013). Monte Carlo simulation of extreme traffic loading on short and medium span bridges. Structure and Infrastructure Engineering, 9(12), 1267-1282.
11. Sgambi, L., Garavaglia, E., Basso, N., & Bontempi, F. (2014). Monte Carlo simulation for seismic analysis of a long span suspension bridge. Engineering Structures, 78, 100-111.
12. Abu Dabous, S., & Al-Khayyat, G. (2018). A Flexible Bridge Rating Method Based on Analytical Evidential Reasoning and Monte Carlo Simulation. Advances in Civil Engineering, 2018.
13. Hacıefendioglu, K., Basaga, H. B., & Banerjee, S. (2017). Probabilistic analysis of historic masonry bridges to random ground motion by Monte Carlo Simulation using Response Surface Method. Construction and Building Materials, 134, 199-209.
14. OleOseth, Ronnquist, A, Naess, A & Sigbjornsson, R (2014). Estimation of extreme response of floating bridges by Monte Carlo simulation. EURODYN-2014: IX International Conference on Structural Dynamics, Porto-Portugal, 2905-2912.
15. Akgul, F., Frangopol, D.M. (2003). Probabilistic analysis of bridge networks based on system reliability and Monte Carlo simulation. In: Der Kiureghian, A., Madanat, S., Pestana, J.M. (eds.) Applications of Statistics and Probability in Civil Engineering, pp. 1633–1637. Millpress, Rotterdam.
16. Sivakumar, B., Moses, F., Fu, G., and Ghosn , M., (2007). “ Legal Truck Loads and AASHTO Legal Loads for Posting” NCHRP Report 575, Transportation Research Board, National Research Council, Washington, D.C.
17. Nowak, A.S. (1999). Calibration of LRFD Bridge Design Code. NCHRP Report 368, Transportation Research Board, National Research Council, Washington, D.C.
18. Moses, F. (2001). Calibration of Load Factors for LRFR Bridge Evaluation.” NCHRP Report 454, Transportation Research Board, National Research Council, Washington, D.C.