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Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks

Year 2013, Volume: 2 Issue: 1, 12 - 25, 01.03.2013

Nihat Morova Sercan Serin Serdal Terzi Mehmet Saltan

Abstract

The term ‘‘present serviceability’’ was adopted to represent the momentary ability of pavement to serve traffic, and the performance of the pavement was represented by its serviceability history in conjunction with its load application history. Serviceability was found to be influenced by longitudinal and transverse profile as well as the extent of cracking and patching. The amount of weight to assign to each element in the determination of the over-all serviceability is a matter of subjective opinion. In this study, the present serviceability index of rigid highway pavements has been predicted by an artificial neural network (ANN) model. For this model, the 49 experimental data obtained from AASHTO include slope variance, faulting, cracking, spalling and patching. The developed ANN model has a higher regression value than the AASHO model. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function for the solution.

Keywords

Artificial Neural Networks Pavement Rigid Pavements Serviceability

References

  • Kırbaş. U., and Gursoy, M., Developing the basics of pavement management system in Beşiktaş district and evaluation of the selected sections. Scientific Research and Essays. Vol. 5(8), Available online at http://www.academicjournals.org/SRE,18 April, 2010, pp. 806-812.
  • Hudson, W. R., F. N. Finn, B. F. McCullough, K. Nair, and B. A. Vallerga. Systems Approach to Pavement Design, Systems Formulation, Performance Definition, and Materials Characterization. Final Report, NCHRP Project 1-10, TRB, National Research Council Washington, D.C., March 1968.
  • Finn, F. N., C. Saraf, R. Kulkarni, K. Nair, W. Smith, and A. Abdullah. Development of Pavement Structural Subsystems. Final Report, NCHRP Project 1-10B. Washington,D.C., Feb. 1977.
  • Haas, R. A Guide to Pavement Management. Good Roads Association, Canada, 1977.
  • Karan MA, Haas R, Walker T. Illustration of pavement management: from data inventory to priority analysis. Transport Res Rec 1981, 814:22–8.
  • Hugo, F., W. J. Scholtz, M. Sinclair, and P. C. Curtayne. Management of Pavement Rehabilitation. Elsevier Science Publishers B.V., North-Holland, 1989.
  • AASHTO. AASHTO Guidelines for Pavement Management Systems. AASHTO, Washington, D.C., 1990. Lee, H., and W. R. Hudson. Reorganizing the PMS Concept. In Proceedings of PMSNorth American Conference on Managing Highways, Ontario, Canada, 1985, pp. 59–80.
  • Haas R, Hudson WR, Zaniewski J. Modern pavement management systems. USA: Krieger Publishing Company; 1994.
  • Shahin, M.Y. Pavement Management for Airports, Roads, and Parking Lots. Second Edition, Springer Science, New York, 2005.
  • Rada GR, Perl J, Witczak W. Integrated model for project-level management of flexible pavements. J Transport Eng 1985;112(4): 381–99.
  • Tavakoli A, Lapin ML, Ludwig F. PMSC: pavement management system for small communities. J Transport Eng 1992;118(2): 270–81.
  • Bandara N, Gunartne M. Current and future pavement maintenance prioritization based on rapid visual condition evaluation. J Transport Eng 2001;127(2):116–23.
  • Shoukry S, Martinelli DR, Reigle JA. Universal pavement distress evaluator based on fuzzy sets. Transport Res Rec 1997;1592:180–6.
  • Saraf CL. Pavement condition rating system. Federal Highway Administration, Report No. FHWA/OH99/00 1998.
  • Fwa TF, Sinha KC. Routine maintenance and pavement performance. J Transport Eng 1985;112(4):329–
  • Madanat S, Prozzi JA, Han M. Effect of performance model accuracy on optimal pavement design. Comput-Aided Civ Inf Eng 2002;17:22–30.
  • Colorni A, Dorigo F, Maffioli F, Maniezzo V, Righini G, Trubian M. Heuristics from nature for hard combinatorial optimization problems. Int Trans Oper Res 1996;3(1):1–21.
  • Emiroğlu, M., Beycioğlu, A., Yildiz, S., “Anfis and Statistical Based Approach to Prediction the Peak Pressure Load of Concrete Pipes Including Glass Fiber”, Expert Systems with Applications (2011), doi:1016/j.eswa.2011.08.149.
  • Attoh-Okine, N.O., (1995). "Using Adaptive Neural Networks to identify Significant Variables in Pavement Performance Prediction", Intelligent Engineering Systems through Artificial Neural Networks, ASME Press Series, Vol 5, pp. 803-808, Tutumluer, E. & R. W. Meier, (1996). Attempt at resilient modulus modeling using artifcial neural networks, Transportation Research Record, 1560, TRB, Washington, DC.
  • Tiğdemir, M., Karaşahin, M., & Sen, Z. (2002). Investigation of fatigue behaviour of asphalt concrete pavements with fuzzy logic approach. International Journal Fatigue, 24(8), 903–910.
  • Kalyoncuoglu, S. F., & Tıgdemir, M. (2004). An alternative approach for modelling and simulation of traffic data: Artificial neural networks. Simulation Modeling Practice and Theory, 12(5), 351–362.
  • Terzi, S. (2007). Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks. Construction & Building Materials, 21, 590–593.
  • Ayata, T., Çam, E., Yıldız, O. (2007). Adaptive neuro-fuzzy inference systems (ANFIS) application to investigate potential use of natural ventilation in new building designs in Turkey.Energy Conversion and Management 48, 1472–1479.
  • Saltan, M., & Terzi, S. (2008). Modeling deflection basin using artificial neural networks with cross validation technique in backcalculating flexible pavement layer moduli. Advances in Engineering Software, 39(7), 588–592
  • Taşdemir, Y. (2009). Artificial neural networks for predicting low temperature performancea of modified asphalt mixtures Indian journal of engineering & Materials Sciences Vol. 16, August, 2009 pp 237-244.
  • Topçu I.B, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic. Comput Mater Sci 41(3):305–311.
  • Alasha’ary, H., Moghtaderi, B., Page, A., Sugo, H. (2009). A neuro–fuzzy model for prediction of the indoor temperature in typical Australian residential buildings. Energy and Buildings 41, 703–710.
  • Subaşı, S. (2009). Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique. Scientific Research and Essays, 4(4), 289–297.
  • Saffarzadeh, M., & Heidaripanah, A. (2009). Transaction A: Civil Engineering Vol. 16, No. 1, pp. 98-105, Sharif University of Technology, February Effeect of Asphalt Content on the Marshall Stability of Asphalt Concrete Using Artificial Neural Networks.
  • Özgan, E. (2011). "Artificial Neural Network Based Modelling of the Marshall Stability of Asphalt Concrete", "Ercan Özgan", Expert Systems With Applications, 38, 6025-6030.
  • Yilmaz, M. Kok, . B. V. Sengoz, B.. Sengur, A Avci E. “Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System” Expert Systems with Applications, 10.1016/j.eswa.2010.07.088.(In Press), Available online 3 August 2010.
  • Saltan, M., Terzi, S., & Küçüksille E. U. (2011). Backcalculation of pavement layer moduli and Poisson’s ratio using data mining Expert Systems with Applications 38, 2600–2608.
  • Mirzahosseini, M. R., Aghaeifar, A., Alavi, A. H., Gandomi, A. H., Seyednour, R. (2011). Permanent deformation analysis of asphalt mixtures using soft computing techniques Expert Systems with Applications 38, 6081–6100.
  • Moazami, D., Behbahani H., Muniandy, R. (2011). Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic. Expert Systems with Applications 38, 12869–12879.
  • Tapkın, S., Çevik, A., Uşar, Ü. (2011). Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural Networks. Expert Systems with Applications 37, 4660–4670
  • Huhtala, M, J. Pihlajamaaki and V. Miettinen. “The Effects of Wide Based Tires on Pavements.” Proceedings, Third International Symposium on Heavy Vehicle Weights and Dimensions. Queen’s College Cambridge, United Kingdom. 1992. pp. 233-242.
  • Garber, Nicholas J., and Lester A. Houl: Traffic and Highway Engineering, West, Publishing, Company, New York, 1998.
  • Rouillard, V., Sek, M., and Perry, T.: Analysis and Simulation of Road Profiles, Journal of Transportation Engineering, Washington, D.C, USA, May/June 1996, 241-245.
  • AASHO Road Test. “The AASHO Road, Test Report 5, Pavement Research”, By the Highway Research Board Of The NAS-NRC Division Of The Engineering And Industrial Research, Special Report 61E, No: 954, National Academy Of Sciences-National Research Council Washington DC, 1962.
  • Delatte, N. Concrete Pavement Design, Construction, And Performance, British Library Cataloguing in Publication Data, 2008, New York, USA, 2008.
  • Awasthi G., Singh T. and Das A., 2003, “On Pavement Roughness Indices” CV, Vol. 84, pp. 33 – 37
  • Öztürk, F., 2006, "1986 AASHTO Design Method of Fuzzy Logic Technique and Modeling of Flexible Road Pavements", Suleyman Demirel University Institute of Science in Civil Engineering Master of Science Thesis, Isparta.
  • Topçu, İ. B., Sarıdemir, M. (2008). Prediction of rubberized concrete properties using artificial neural network and fuzzy logic Construction and Building Materials 22, 532–540
  • Saltan M, Terzi S. Back calculation of pavement layer parameters using artificial neural networks. Indian J Eng and Mater Sci 2004;11:38–42.
  • Şencan A, Kalogirou A. S, “A new approach using artificial neural networks for determination of the thermodynamic properties. Energy Conversion and Management “46 (2005) 2405–2418.
  • Demuth H, Beale M. Neural network toolbox, user guide, Version 4. Natick: The MathWorks Inc.; 2001.

Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks

Year 2013, Volume: 2 Issue: 1, 12 - 25, 01.03.2013

Nihat Morova Sercan Serin Serdal Terzi Mehmet Saltan

Abstract

Keywords

-

References

  • Kırbaş. U., and Gursoy, M., Developing the basics of pavement management system in Beşiktaş district and evaluation of the selected sections. Scientific Research and Essays. Vol. 5(8), Available online at http://www.academicjournals.org/SRE,18 April, 2010, pp. 806-812.
  • Hudson, W. R., F. N. Finn, B. F. McCullough, K. Nair, and B. A. Vallerga. Systems Approach to Pavement Design, Systems Formulation, Performance Definition, and Materials Characterization. Final Report, NCHRP Project 1-10, TRB, National Research Council Washington, D.C., March 1968.
  • Finn, F. N., C. Saraf, R. Kulkarni, K. Nair, W. Smith, and A. Abdullah. Development of Pavement Structural Subsystems. Final Report, NCHRP Project 1-10B. Washington,D.C., Feb. 1977.
  • Haas, R. A Guide to Pavement Management. Good Roads Association, Canada, 1977.
  • Karan MA, Haas R, Walker T. Illustration of pavement management: from data inventory to priority analysis. Transport Res Rec 1981, 814:22–8.
  • Hugo, F., W. J. Scholtz, M. Sinclair, and P. C. Curtayne. Management of Pavement Rehabilitation. Elsevier Science Publishers B.V., North-Holland, 1989.
  • AASHTO. AASHTO Guidelines for Pavement Management Systems. AASHTO, Washington, D.C., 1990. Lee, H., and W. R. Hudson. Reorganizing the PMS Concept. In Proceedings of PMSNorth American Conference on Managing Highways, Ontario, Canada, 1985, pp. 59–80.
  • Haas R, Hudson WR, Zaniewski J. Modern pavement management systems. USA: Krieger Publishing Company; 1994.
  • Shahin, M.Y. Pavement Management for Airports, Roads, and Parking Lots. Second Edition, Springer Science, New York, 2005.
  • Rada GR, Perl J, Witczak W. Integrated model for project-level management of flexible pavements. J Transport Eng 1985;112(4): 381–99.
  • Tavakoli A, Lapin ML, Ludwig F. PMSC: pavement management system for small communities. J Transport Eng 1992;118(2): 270–81.
  • Bandara N, Gunartne M. Current and future pavement maintenance prioritization based on rapid visual condition evaluation. J Transport Eng 2001;127(2):116–23.
  • Shoukry S, Martinelli DR, Reigle JA. Universal pavement distress evaluator based on fuzzy sets. Transport Res Rec 1997;1592:180–6.
  • Saraf CL. Pavement condition rating system. Federal Highway Administration, Report No. FHWA/OH99/00 1998.
  • Fwa TF, Sinha KC. Routine maintenance and pavement performance. J Transport Eng 1985;112(4):329–
  • Madanat S, Prozzi JA, Han M. Effect of performance model accuracy on optimal pavement design. Comput-Aided Civ Inf Eng 2002;17:22–30.
  • Colorni A, Dorigo F, Maffioli F, Maniezzo V, Righini G, Trubian M. Heuristics from nature for hard combinatorial optimization problems. Int Trans Oper Res 1996;3(1):1–21.
  • Emiroğlu, M., Beycioğlu, A., Yildiz, S., “Anfis and Statistical Based Approach to Prediction the Peak Pressure Load of Concrete Pipes Including Glass Fiber”, Expert Systems with Applications (2011), doi:1016/j.eswa.2011.08.149.
  • Attoh-Okine, N.O., (1995). "Using Adaptive Neural Networks to identify Significant Variables in Pavement Performance Prediction", Intelligent Engineering Systems through Artificial Neural Networks, ASME Press Series, Vol 5, pp. 803-808, Tutumluer, E. & R. W. Meier, (1996). Attempt at resilient modulus modeling using artifcial neural networks, Transportation Research Record, 1560, TRB, Washington, DC.
  • Tiğdemir, M., Karaşahin, M., & Sen, Z. (2002). Investigation of fatigue behaviour of asphalt concrete pavements with fuzzy logic approach. International Journal Fatigue, 24(8), 903–910.
  • Kalyoncuoglu, S. F., & Tıgdemir, M. (2004). An alternative approach for modelling and simulation of traffic data: Artificial neural networks. Simulation Modeling Practice and Theory, 12(5), 351–362.
  • Terzi, S. (2007). Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks. Construction & Building Materials, 21, 590–593.
  • Ayata, T., Çam, E., Yıldız, O. (2007). Adaptive neuro-fuzzy inference systems (ANFIS) application to investigate potential use of natural ventilation in new building designs in Turkey.Energy Conversion and Management 48, 1472–1479.
  • Saltan, M., & Terzi, S. (2008). Modeling deflection basin using artificial neural networks with cross validation technique in backcalculating flexible pavement layer moduli. Advances in Engineering Software, 39(7), 588–592
  • Taşdemir, Y. (2009). Artificial neural networks for predicting low temperature performancea of modified asphalt mixtures Indian journal of engineering & Materials Sciences Vol. 16, August, 2009 pp 237-244.
  • Topçu I.B, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic. Comput Mater Sci 41(3):305–311.
  • Alasha’ary, H., Moghtaderi, B., Page, A., Sugo, H. (2009). A neuro–fuzzy model for prediction of the indoor temperature in typical Australian residential buildings. Energy and Buildings 41, 703–710.
  • Subaşı, S. (2009). Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique. Scientific Research and Essays, 4(4), 289–297.
  • Saffarzadeh, M., & Heidaripanah, A. (2009). Transaction A: Civil Engineering Vol. 16, No. 1, pp. 98-105, Sharif University of Technology, February Effeect of Asphalt Content on the Marshall Stability of Asphalt Concrete Using Artificial Neural Networks.
  • Özgan, E. (2011). "Artificial Neural Network Based Modelling of the Marshall Stability of Asphalt Concrete", "Ercan Özgan", Expert Systems With Applications, 38, 6025-6030.
  • Yilmaz, M. Kok, . B. V. Sengoz, B.. Sengur, A Avci E. “Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System” Expert Systems with Applications, 10.1016/j.eswa.2010.07.088.(In Press), Available online 3 August 2010.
  • Saltan, M., Terzi, S., & Küçüksille E. U. (2011). Backcalculation of pavement layer moduli and Poisson’s ratio using data mining Expert Systems with Applications 38, 2600–2608.
  • Mirzahosseini, M. R., Aghaeifar, A., Alavi, A. H., Gandomi, A. H., Seyednour, R. (2011). Permanent deformation analysis of asphalt mixtures using soft computing techniques Expert Systems with Applications 38, 6081–6100.
  • Moazami, D., Behbahani H., Muniandy, R. (2011). Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic. Expert Systems with Applications 38, 12869–12879.
  • Tapkın, S., Çevik, A., Uşar, Ü. (2011). Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural Networks. Expert Systems with Applications 37, 4660–4670
  • Huhtala, M, J. Pihlajamaaki and V. Miettinen. “The Effects of Wide Based Tires on Pavements.” Proceedings, Third International Symposium on Heavy Vehicle Weights and Dimensions. Queen’s College Cambridge, United Kingdom. 1992. pp. 233-242.
  • Garber, Nicholas J., and Lester A. Houl: Traffic and Highway Engineering, West, Publishing, Company, New York, 1998.
  • Rouillard, V., Sek, M., and Perry, T.: Analysis and Simulation of Road Profiles, Journal of Transportation Engineering, Washington, D.C, USA, May/June 1996, 241-245.
  • AASHO Road Test. “The AASHO Road, Test Report 5, Pavement Research”, By the Highway Research Board Of The NAS-NRC Division Of The Engineering And Industrial Research, Special Report 61E, No: 954, National Academy Of Sciences-National Research Council Washington DC, 1962.
  • Delatte, N. Concrete Pavement Design, Construction, And Performance, British Library Cataloguing in Publication Data, 2008, New York, USA, 2008.
  • Awasthi G., Singh T. and Das A., 2003, “On Pavement Roughness Indices” CV, Vol. 84, pp. 33 – 37
  • Öztürk, F., 2006, "1986 AASHTO Design Method of Fuzzy Logic Technique and Modeling of Flexible Road Pavements", Suleyman Demirel University Institute of Science in Civil Engineering Master of Science Thesis, Isparta.
  • Topçu, İ. B., Sarıdemir, M. (2008). Prediction of rubberized concrete properties using artificial neural network and fuzzy logic Construction and Building Materials 22, 532–540
  • Saltan M, Terzi S. Back calculation of pavement layer parameters using artificial neural networks. Indian J Eng and Mater Sci 2004;11:38–42.
  • Şencan A, Kalogirou A. S, “A new approach using artificial neural networks for determination of the thermodynamic properties. Energy Conversion and Management “46 (2005) 2405–2418.
  • Demuth H, Beale M. Neural network toolbox, user guide, Version 4. Natick: The MathWorks Inc.; 2001.
There are 46 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Nihat Morova This is me

Sercan Serin This is me

Serdal Terzi This is me

Mehmet Saltan This is me

Publication Date March 1, 2013
Published in Issue Year 2013 Volume: 2 Issue: 1

Cite

APA Morova, N., Serin, S., Terzi, S., Saltan, M. (2013). Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks. İleri Teknoloji Bilimleri Dergisi, 2(1), 12-25.
AMA Morova N, Serin S, Terzi S, Saltan M. Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks. İleri Teknoloji Bilimleri Dergisi. March 2013;2(1):12-25.
Chicago Morova, Nihat, Sercan Serin, Serdal Terzi, and Mehmet Saltan. “Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks”. İleri Teknoloji Bilimleri Dergisi 2, no. 1 (March 2013): 12-25.
EndNote Morova N, Serin S, Terzi S, Saltan M (March 1, 2013) Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks. İleri Teknoloji Bilimleri Dergisi 2 1 12–25.
IEEE N. Morova, S. Serin, S. Terzi, and M. Saltan, “Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks”, İleri Teknoloji Bilimleri Dergisi, vol. 2, no. 1, pp. 12–25, 2013.
ISNAD Morova, Nihat et al. “Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks”. İleri Teknoloji Bilimleri Dergisi 2/1 (March 2013), 12-25.
JAMA Morova N, Serin S, Terzi S, Saltan M. Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks. İleri Teknoloji Bilimleri Dergisi. 2013;2:12–25.
MLA Morova, Nihat et al. “Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks”. İleri Teknoloji Bilimleri Dergisi, vol. 2, no. 1, 2013, pp. 12-25.
Vancouver Morova N, Serin S, Terzi S, Saltan M. Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks. İleri Teknoloji Bilimleri Dergisi. 2013;2(1):12-25.