Application of the Bayesian statistical approach to develop a Stone Mastic Asphalt (SMA) pavement performance model

Authors

  • Alireza Joshaghani Texas A&M University

DOI:

https://doi.org/10.30564/jaeser.v2i4.1671

Abstract

Stone mastic asphalt (SMA) has not been widely used in the pavement industry, and there are no detailed design specifications for this type of asphalt. Therefore, long-term performance data is not available widely, and no performance model has been developed for SMA. The main purpose of this study was to integrate expert knowledge (using the Markov-chain process) and experimental data from field investigations to propose a performance model for SMA through the incorporation of the Bayesian technique. The combination of these sources of data resulted in an efficient and effective method to develop a performance model for this type of pavement, which did not have a long-term performance database. As a result, a robust linear performance model was established to predict the service life of SMA. The service life of SMA can be estimated explicitly according to the developed performance model which has been validated using a new set of data.

Keywords:

Stone mastic asphalt (SMA), Bayesian, Markov-chain, Model Performance

References

[1] Muniandy, R. and B.B. Huat, Laboratory diameteral fatigue performance of stone matrix asphalt with cellulose oil palm fiber. American Journal of Applied Sciences, 2006. 3(9): p. 2005-2010.

[2] Asi, I.M., Laboratory comparison study for the use of stone matrix asphalt in hot weather climates. Construction and Building Materials, 2006. 20(10): p. 982-989.

[3] Wolters, O.R., Road and Airfield pavements with SMA. Minnesota Asphalt Pavement Association, New Brighton, MN, 2009.

[4] Association, N.A.P., Designing and Constructing SMA Mixtures: State of the Practice. 2002: National Asphalt Pavement Association.

[5] Brown, E., et al., Development of a mixture design procedure for stone matrix asphalt (SMA). National Center for Asphalt Technology, Report, 1997(97-03).

[6] Richardson, J., Stone mastic asphalt in the Uk symposium on stone mastic asphalt and thin surfacing. London, Richter E, 1997.

[7] Bellin, P. Development, principles and long-term performance of stone mastic asphalt in Germany. in SCI Lecture Papers. 1997.

[8] Brown, E.R., et al., Performance of stone matrix asphalt (SMA) mixtures in the United States. Journal of the Association of Asphalt Paving Technologists, 1997. 66(97): p. 426-457.

[9] Xue, Y., et al., Utilization of municipal solid waste incineration ash in stone mastic asphalt mixture: pavement performance and environmental impact. Construction and Building Materials, 2009. 23(2): p. 989-996.

[10] Association, N.A.P., Guidelines for materials, production, and placement of stone matrix asphalt (SMA). Technical Working Group (TWG), Publication No. IS, 1994. 118.

[11] Smith, K., et al., Life-Cycle Cost Analysis of SMA Pavement and SMA Application Guidelines. 2006.

[12] Buttlar, W. and Z. You, Discrete element modeling of asphalt concrete: microfabric approach. Transportation Research Record: Journal of the Transportation Research Board, 2001(1757): p. 111-118.

[13] Xue, Q., et al., Evaluation of pavement straw composite fiber on SMA pavement performances. Construction and Building Materials, 2013. 41: p. 834-843.

[14] Al, A.-H. and Y.-q. Tan, Performance of the SMA mixes made with the various binders. Construction and Building Materials, 2011. 25(9): p. 3668-3673.

[15] Choubane, B., R. McNamara, and G. Page, Evaluation of high-speed profilers for measurement of asphalt pavement smoothness in Florida. Transportation Research Record: Journal of the Transportation Research Board, 2002(1813): p. 62-67.

[16] Shafizadeh, K. and F. Mannering, Acceptability of pavement roughness on urban highways by driving public. Transportation Research Record: Journal of the Transportation Research Board, 2003(1860): p. 187-193.

[17] Hong, F. and J.A. Prozzi, Estimation of pavement performance deterioration using Bayesian approach. Journal of infrastructure systems, 2006. 12(2): p. 77-86.

[18] Board, N.R.C.H.R. and A.A.o.S.H. Officials, The AASHO Road Test: Report. 1962: National Academy of Sciences-National Research Council.

[19] Garcia-Diaz, A. and M. Riggins, Serviceability and distress methodology for predicting pavement performance. Transportation Research Record, 1984. 997: p. 56-61.

[20] Rauhut, J.B., et al., Damage Functions for Rutting, Fatigue Cracking, and Loss of Serviceability in Flexible Pavements. Transportation Research Record, 1983. 943: p. 1-9.

[21] Paterson, W.D., Road deterioration and maintenance effects: Models for planning and management. 1987.

[22] Canadian Strategic Highway Research Program-C-SHRP: 1995-96 progress report. 1996.

[23] Zellner, A., An introduction to Bayesian inference in econometrics. 1971.

[24] Winkler, R.L., An introduction to Bayesian inference and decision/by Robert L. Winkler. 1972.

[25] Park, E.S., et al., A Bayesian approach for improved pavement performance prediction. Journal of Applied Statistics, 2008. 35(11): p. 1219-1238.

[26] Saliminejad, S. and N.G. Gharaibeh, A spatial‐Bayesian technique for imputing pavement network repair data. Computer‐Aided Civil and Infrastructure Engineering, 2012. 27(8): p. 594-607.

[27] Hajek, J. and A. Bradbury, Pavement performance modeling using canadian strategic highway research program bayesian statistical methodology. Transportation Research Record: Journal of the Transportation Research Board, 1996(1524): p. 160-170.

[28] Golroo, A. and S.L. Tighe, Pervious concrete pavement performance modeling using the Bayesian statistical technique. Journal of transportation engineering, 2011. 138(5): p. 603-609.

[29] Ibrahim, J.G., M.-H. Chen, and D. Sinha, Bayesian survival analysis. 2001: Springer Science & Business Media.

[30] Gao, L., J.P. Aguiar-Moya, and Z. Zhang, Bayesian analysis of heterogeneity in modeling of pavement fatigue cracking. Journal of Computing in Civil Engineering, 2011. 26(1): p. 37-43.

[31] Haas, R.C. and T.A.o. Canada, Pavement design and management guide. 1997: Transportation Association of Canada Ottawa.

[32] Han, D., et al., Performance evaluation of advanced pavement materials by Bayesian Markov Mixture Hazard model. KSCE Journal of Civil Engineering, 2016. 20(2): p. 729-737.

[33] Li, N., W.-C. Xie, and R. Haas, Reliability-based processing of Markov chains for modeling pavement network deterioration. Transportation Research Record: Journal of the Transportation Research Board, 1996(1524): p. 203-213.

[34] Han, D., K. Kaito, and K. Kobayashi, Application of Bayesian estimation method with Markov hazard model to improve deterioration forecasts for infrastructure asset management. KSCE Journal of Civil Engineering, 2014. 18(7): p. 2107-2119.

[35] Qin, X., et al., Hierarchical Bayesian estimation of safety performance functions for two-lane highways using Markov chain Monte Carlo modeling. Journal of Transportation Engineering, 2005. 131(5): p. 345-351.

[36] Malyshkina, N.V. and F.L. Mannering, Zero-state Markov switching count-data models: An empirical assessment. Accident Analysis & Prevention, 2010. 42(1): p. 122-130.

[37] Mills, L.N., N.O. Attoh-Okine, and S. McNeil, Hierarchical Markov chain Monte Carlo simulation for modeling transverse cracks in highway pavements. Journal of Transportation Engineering, 2011. 138(6): p. 700-705.

[38] Li, N., R. Haas, and W.-C. Xie, Development of a new asphalt pavement performance prediction model. Canadian Journal of Civil Engineering, 1997. 24(4): p. 547-559.

[39] Martin, J.J., Bayesian decision problems and Markov chains. 1967: Wiley.

[40] Jackart, M., et al., C-SHRP Bayesian Modelling: A User's Guide.

[41] Raiffa, H. and R. Schlaifer, Applied Statistical Decision Theory (Division of Research, Harvard Business School, Boston, 1961). CORPORATE GROWTH UNDER UNCERTAINTY. 241.

[42] Press, S.J., Bayesian statistics: principles, models, and applications. Vol. 210. 1989: John Wiley & Sons Inc.

[43] Khouzani, A.H.E., A. Golroo, and M. Bagheri, Railway Maintenance Management Using a Stochastic Geometrical Degradation Model. Journal of Transportation Engineering, Part A: Systems, 2016. 143(1): p. 04016002.

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How to Cite

Joshaghani, A. (2020). Application of the Bayesian statistical approach to develop a Stone Mastic Asphalt (SMA) pavement performance model. Journal of Architectural Environment & Structural Engineering Research, 2(4), 18–28. https://doi.org/10.30564/jaeser.v2i4.1671

Issue

Vol. 2 , Iss. 4 (October 2019)

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