Application of Adaptive Neuro-Fuzzy Inference System for Modeling of International Roughness Index in Jointed Plane Concrete Pavements

Document Type : Research Paper

Author

Assistant Professor, Dept. of Civil Eng., Sirjan Univ. of Technol., I. R. Iran

Abstract

Several criteria have been developed to represent the serviceability conditions of pavement, which international roughness index (IRI) is one of the most important indices. In addition to the application of IRI in case of representing serviceability conditions of the pavement and its application in prioritizing of maintenance and rehabilitation activities, in new mechanistic-empirical methods for design of rigid pavements (e.g MEPDG 2002), it is needed to convert all independent distresses to IRI by utilizing a mathematical model. Thus, determination of IRI on the basis of independent observed distresses is very important. In this research, Adaptive Nero-Fuzzy Inference System (ANFIS) is utilized for modeling of IRI of jointed plane concrete pavements (JPCP) based on long-term pavement performance (LTPP) data. Input data for ANFIS included pavement age in year, initial IRI in m/km, percentage of slabs with transverse cracking, percentage of joints with spalling, pavement surface area with flexible and rigid patching, total joint faulting in mm/km, freezing index in oC and percent subgrade material passing the 0.075-mm sieve and output was considered as IRI in m/km. Results showed that the coefficient of determination (R2) between measured and predicted values of testing set in case of NCHRP equation and ANFIS model are 0.601 and 0.758, respectively. Thus the developed model, based on ANFIS, can be used for accurate predicting of IRI in jointed plane concrete pavements in comparison with NCHRP equation.

Keywords

References

شورایعالی فنی امور زیربنایی حمل و نقل. 1385. "دستوالعمل تحویل موقت و قطعی راه‌ها". وزارت راه و ترابری، معاونت آموزش، تحقیقات و فناوری.
Abd El-Hakim R. and El-Badawy, S. 2013. “International roughness index prediction for rigid pavements: An artificial neural network application”.  Adv. Mater. Res. 723: 854-860.
Al-omari, B. and Darter, M. I. 1992. “Relationships between IRI and PSR. University of Illinois at Urbana- Champaign”.
Al-Omari, B. and Darter, M. I. 1994. “Relationships between International Roughness Index and Present Serviceability Rating”. Transportation Research Record, 1435.
American Association of State Highway and Transportation Officials (AASHTO) .1987. “Summary results of 1987 AASHTO rideability survey”.
Cary, Jr, W. and Irick, P. 1960. “The Pavement Serviceability- Performance Concept”. Highway Research Board Bulletin, 250.
Darter, M. I. and Barenberg, E. J. 1976. “Zero-Maintenance Pavements: Results of Field Studies on the Performance Requirements and Capabilities of Conventional Pavement”. Federal Highway Administration, Report No. FHWA-RD-76-105, Washington, DC.
Federal Highway Administration. 1993. “Distress Identification Manual for Long-Term Pavement Performance Project”. SHRP-P-338, Strategic Highway Research Program, Washington, DC.
Federal Highway Administration. 1996. “Long-Term Pavement Performance Information Management System Data Users Reference Manual”. Washington, DC.
Felker, V., Najjar, Y. M. and Hossain, M. 2004. “Modeling the Roughness Progression on Kansas Portland Cement Concrete (PCC) Pavements”. Kansas Department of Transportation.
Gopalakrishnan, K. and Khaitan, S. K. 2010. “Finite element based adaptive neuro-fuzzy inference technique for parameter identification of multi-layered transportation structures”. Transport 25(1): 58-65.
Jang, J. S. R. 1993. “ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Trans. on, 23(3), 665-685.
Jang, J. S. R., Sun, C. T. and Mizutani, E. 1997. “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence”. Prentice Hall, N. J.
Janoff, M. S. and Lanham, M. D. 1991. “Pavement Smoothness”. National Asphlt Pavement Association (NAPA).
Luo, C. 2014. “Pavement deterioration modeling and design of a composite pavement distress index for Kentucky interstate highways and parkways”. MSc. Thesis, University of Louisville,Kentucky.
Morova, N., Serin, S., Terzi, S. and Saltan, M. 2013. “Prediction of the pavement serviceability ratio of rigid highway pavements by artificial neural networks”. J. Adv. Technol. Sci., 2(1): 12-25.
National Cooperative Highway Research Program (NCHRP). 2001. “Guide for Mechanistic- Empirical Design of New and Rehabilitated Pavement Structure”. Appendix PP: Smoothness prediction for rigid pavements, Final document, National Cooperative Highway Research Program (NCHRP).
National Quality Initiative (NQI). 1996. “National Highway Users Survey”. Coopers and Lybrand, L.L.P. Opinion Research Cooperation.
Pourtahmasb, M. S., Karim, M. R. and Shamshirband, S. 2015. “Resilient modulus prediction of asphalt mixtures containing recycled concrete aggregate using an adaptive neuro-fuzzy methodology”. Constr. Buil. Mater. 82: 257-263.
Queiroz, C. A. V. and Hudson, W. R. 1984. “A stable, consistent and transferable roughness scale for worldwide standardization”. Transport. Res. Rec. 997: 46-55.
Rowshan, S. and S. Harris 1993. “Long Term Pavement Performance Information Management System”. FHWA-RD-93-094, Federal Highway Administration, Washington, DC.
Sayers, M. W. and Gillespie, T. D. 1986. “The International road roughness experiments: A basis for establishing a standard scale for road roughness measurements”. Transport. Res. Rec. 1084: 76-85.
Sayers, M. W., Gillespie, T. D. and Paterson, W. D. O. 1986a. “Guidelines for Conducting and Calibrating Road Roughness Measurements”. Technical Paper 46, The World Bank, Washington, DC.
Sayers, M. W., Gillespie, T. D. and Queiroz, C. A. V. 1986b. “The International Road Roughness Experiment: Establishing Correlation and a Calibration Standard for Measurements”. Technical Paper 45, The World Bank, Washington, DC.
Shafabakhsh, G. and Tanakizadeh, A. 2015. “Investigation of loading features effects on resilient modulus of asphalt mixtures using adaptive neuro-fuzzy inference system”. Constr. Buil. Mater. 76: 256-263.
Shekharan, A. R. 2000. “Pavement Performance Prediction by Artificial Neural Network”. Proc. 2nd International Workshop, Computational Intelligence Applications in Pavement and Geomechanical Systems, A. A. Balkema.
Smith, K., Smith, K. D., Evans, L. D., Hoerner, T. E. and Darter, M. I. 1997. “Smoothness Specifications for Pavements”. Final Report NCHRP 1-31.
Strategic Highway Research Program. 1992. “SHRP Database Structure Reference Manual”. Washington, DC.
Tabatabaei, S. A., Khaledi, S. and Jahantabi, A. 2013. “Modeling the Deduct value of the pavement condition of asphalt pavement by adaptive neuro fuzzy inference system”. Int. J. Pavement Res. Technol. 6(1): 59-65.
Teomete, E., Bayrak, M. B. and Agarwal, M. 2004. “Use of Artificial Neural Networks for Predicting Rigid Pavement Roughness”. Transportation Scholars Conference, Iowa State University, Ames, Iowa, USA.
Terzi, S. 2013. “Modeling for pavement roughness using the ANFIS approach”. Adv. Eng. Software 57: 59-64.
Tigdemir, M., Karasahin, M. and Sen, Z. 2002. “Investigation of fatigue behaviour of asphalt concrete pavements with fuzzy-logic approach”. Int. J. Fatigue, 24(8): 903-910.
Yang, Y. and Zhang, Q. 1997. “Analysis for the Results of Point Load Testing with Artificial Neural Network”. Proc. Int. Conf. Comp. Methods and Adv. in Geomech., A. A. Balkema, Rotterdam.
Yu, H. T., Darter, M. I., Smith, K. D., Jiang, J. and Khazanovich, L. 1998.  “Performance of Concrete Pavements”. Volume III, Improving Concrete Pavement Performance, Report No. FHWA-RD-95-111, Federal Highway Administration, Washington, DC.

Files

History

  • Receive Date: 13 July 2015
  • Revise Date: 28 November 2015
  • Accept Date: 07 December 2015

Share

How to cite

Statistics