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

Year 2020, Volume: 23 Issue: 1, 53 - 60, 01.03.2020
https://doi.org/10.2339/politeknik.469495

Abstract

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. 

References

  • 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.
  • AASHTO. (2008). The Manual For Bridge Evaluation First Edition, Washington, D.C.
  • AASHTO-LRFD Bridge Design Specifications. (2012). Customary U.S. Units, American Association of State Highway and Transportation Officials, Washington, D.C.
  • AASHTO (2003). Manual for Condition Evaluation and Load and Resistance Factor Rating (LRFR) of Highway Bridges, AASHTO, Washington, D.C.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Nowak, A.S. (1999). Calibration of LRFD Bridge Design Code. NCHRP Report 368, Transportation Research Board, National Research Council, Washington, D.C.
  • Moses, F. (2001). Calibration of Load Factors for LRFR Bridge Evaluation.” NCHRP Report 454, Transportation Research Board, National Research Council, Washington, D.C.

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

Year 2020, Volume: 23 Issue: 1, 53 - 60, 01.03.2020
https://doi.org/10.2339/politeknik.469495

Abstract

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. 

References

  • 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.
  • AASHTO. (2008). The Manual For Bridge Evaluation First Edition, Washington, D.C.
  • AASHTO-LRFD Bridge Design Specifications. (2012). Customary U.S. Units, American Association of State Highway and Transportation Officials, Washington, D.C.
  • AASHTO (2003). Manual for Condition Evaluation and Load and Resistance Factor Rating (LRFR) of Highway Bridges, AASHTO, Washington, D.C.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Nowak, A.S. (1999). Calibration of LRFD Bridge Design Code. NCHRP Report 368, Transportation Research Board, National Research Council, Washington, D.C.
  • Moses, F. (2001). Calibration of Load Factors for LRFR Bridge Evaluation.” NCHRP Report 454, Transportation Research Board, National Research Council, Washington, D.C.
There are 18 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Arcan Yanik 0000-0002-2527-4812

Christopher Higgins This is me 0000-0002-2443-0369

Publication Date March 1, 2020
Submission Date October 11, 2018
Published in Issue Year 2020 Volume: 23 Issue: 1

Cite

APA Yanik, A., & Higgins, C. (2020). A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges. Politeknik Dergisi, 23(1), 53-60. https://doi.org/10.2339/politeknik.469495
AMA Yanik A, Higgins C. A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges. Politeknik Dergisi. March 2020;23(1):53-60. doi:10.2339/politeknik.469495
Chicago Yanik, Arcan, and Christopher Higgins. “A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges”. Politeknik Dergisi 23, no. 1 (March 2020): 53-60. https://doi.org/10.2339/politeknik.469495.
EndNote Yanik A, Higgins C (March 1, 2020) A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges. Politeknik Dergisi 23 1 53–60.
IEEE A. Yanik and C. Higgins, “A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges”, Politeknik Dergisi, vol. 23, no. 1, pp. 53–60, 2020, doi: 10.2339/politeknik.469495.
ISNAD Yanik, Arcan - Higgins, Christopher. “A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges”. Politeknik Dergisi 23/1 (March 2020), 53-60. https://doi.org/10.2339/politeknik.469495.
JAMA Yanik A, Higgins C. A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges. Politeknik Dergisi. 2020;23:53–60.
MLA Yanik, Arcan and Christopher Higgins. “A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges”. Politeknik Dergisi, vol. 23, no. 1, 2020, pp. 53-60, doi:10.2339/politeknik.469495.
Vancouver Yanik A, Higgins C. A Monte-Carlo Simulation for the Estimation of Side-by-Side Loading Events on Oregon Bridges. Politeknik Dergisi. 2020;23(1):53-60.