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Artificial neural network modelling to predict international roughness index of rigid pavements

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Abstract

This research focuses on predicting the International Roughness Index (IRI) of rigid pavements using the Artificial Neural Network (ANN) model that uses climate and traffic parameters as inputs. A Long-Term Pavement Performance (LTPP) database is used to extract data from wet-freeze, wet no-freeze, dry-freeze, and dry no-freeze climatic zones. The climate and traffic parameters are Mean Annual Air Temperature, Annual Average Freezing Index, Annual Average Maximum and Minimum Humidity, Annual Average Precipitation, Annual Average Daily Traffic, and Annual Average Daily Truck Traffic. The ANN model is trained with 70% of climate, traffic and IRI data, rest 15% data is used to test the model, and remaining 15% data is used to validate the model. The trained and the validated models are compared by calculating Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among many results, the datasets that are tested with 7–9–9–1 ANN structure with hyperbolic tangent sigmoidal transfer function generated the best prediction models with an RMSE value of 0.01 and MAPE value of 0.01 (1% error) for a rigid pavement located in the wet no-freeze climatic zone.

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References

  1. B. Saghafi., A. Hassani., R. Noori., M. G. Bustos, Artificial Neural Networks and Regression Analysis for Predicting Faulting in Jointed Concrete Pavements Considering Base Condition, Int. J. Pavement Res. Technol. 2 (1) (2009) 20–25.

    Google Scholar 

  2. K. Ksaibati., R. McNamara., W. Miley., J. M. Armaghani, Evaluating the ride number as a pavement management roughness index, Florida Department of Transportation, Gainesville, FL, USA, 1998.

    Google Scholar 

  3. T. Wang., J. Harvey., J. Lea., C. Kim., Impact of Pavement Roughness on Vehicle Free-Flow Speed, J. Transp. Eng. 140 (9) (2014) 1–11. doi:10.1061/(ASCE)TE.1943–5436.0000689.

    Google Scholar 

  4. J. Lin., J.-T. Yau., L.-H. Hsiao., Correlation Analysis Between International Roughness Index (IRI) and Pavement Distress by Neural Network, in: 82nd Transp. Res. Board Annu. Meet., Transportation Research Board, Washington, D.C., USA, 2003.

    Google Scholar 

  5. A. Bin., A. Latif., Relationship Between International Roughness Index (IRI) and Present Serviceability Index (PSI), (Master of Science Thesis), Universiti Teknologi Malaysia, Malaysia, 2009.

    Google Scholar 

  6. K. Park., N. E. Thomas, K. Wayne Lee, Applicability of the International Roughness Index as a Predictor of Asphalt Pavement Condition, J. Transp. Eng. 133 (12) (2007) 706–709. doi:10.1061/(ASCE)0733–947X(2007)133:12(706).

    Google Scholar 

  7. S. A. Arhin, L. N. Williams, A. Ribbiso., M. F. Anderson, Predicting Pavement Condition Index Using International Roughness Index in a Dense Urban Area, J. Civ. Eng. Res. 5 (1) (2015) 10–17. doi:10.5923/j.jce.20150501.02.

    Google Scholar 

  8. Y. Du., C. Liu., D. Wu., S. Jiang., Measurement of International Roughness Index by Using Z-Axis Accelerometers and GPS, Math. Probl. Eng. 2014 (2014) 1–10. doi:10.1155/2014/928980.

    Google Scholar 

  9. M. W. Sayers, S. M. Karamihas, Interpretation of Road Roughness Profile Data—Final Report, Federal Highway Administration, McLean, VA, USA, 1996.

    Google Scholar 

  10. J. Chang., Y. Su., T. Huang., S. Kang., S. Hsieh., Measurement of the International Roughness Index (IRI) using an Autonomous Robot (P3-AT), in: 26th Int. Symp. Autom. Robot. Constr., International Association for Automation and Robotics in Construction, Austin, Texas, USA, 2009, pp. 325–331.

    Google Scholar 

  11. F. Bayomy., H. Salem., L. Vosti., Analysis of the Long-Term Pavement Performance Data for the Idaho GPS and SPS Sections, Idaho Transportation Department, Boise, Idaho, USA, 2007.

    Google Scholar 

  12. N. J. Santero, A. Horvath., Global Warming Potential of Pavements, Environ. Res. Lett. 4 (3) (2009) 4–11. doi:10.1088/1748–9326/4/3/034011.

    Google Scholar 

  13. N. Yadav., A. Yadav., M. Kumar., History of Neural Networks, in: An Introd. to Neural Netw. Methods Differ. Equations., Springer, Dordrecht, 2015: pp. 13–15. doi:10.1007/978–94–017–9816–7.

    Google Scholar 

  14. J. A. Bullinaria, Introduction to Neural Networks and Their History, University of Birmingham, UK, 2004. doi:10.1108/eb007822.

    Google Scholar 

  15. Y. LeCun., A Theoretical Framework for Back-Propagation, Proc. 1988 Connect. Model. Summer Sch., Carnegie-Mellon University, Pittsburgh, PA, USA, 1988, pp. 21–28. doi:10.1007/978–3–642–35289–8.

    Google Scholar 

  16. M. B. Bayrak, E. Teomete., M. Agarwal., Use of Artificial Neural Networks for Predicting Rigid Pavement Roughness, in: Midwest Transp. Consort., Ames, Iowa, USA, 2004: pp. 1–18.

    Google Scholar 

  17. J. Yang., J. J. Lu, M. Gunaratne., Application of Neural Network Models for Forecasting of Pavement Crack Index and Pavement Condition Rating, Tallahassee, FL, 2003.

    Google Scholar 

  18. J. S. Miller, W. Y. Bellinger, Distress Identification Manual for the Long-Term Pavement Performance Program, McLean, Virginia, USA, 2003.

    Google Scholar 

  19. G. E. Elkins, T. Thomson., A. Simpson., B. Ostrom., Long-Term Pavement Performance Information Management System: Pavement Performance Database User Reference Guide, McLean, Virginia, USA, 2012.

    Google Scholar 

  20. G. E. Elkins, Ba. Ostrom., B. Visintine., J. Groeger., Long-Term Pavement Performance Ancillary Information Management System (AIMS) Reference Guide, Federal Highway Administration, McLean, VA, USA, 2012.

    Google Scholar 

  21. A. N. Hanna, S. D. Tayabji, J. S. Miller, SHRP-LTPP Specific Pavement Studies: Five-Year Report, Washington, D.C., USA, 1994.

    Google Scholar 

  22. K. K. Mantravadi, LTPP-Distress Due to Environment, in: MTC Transp. Sch. Conf., Ames, Iowa, USA, 2000: pp. 83–90.

    Google Scholar 

  23. M. B. Bayrak, Analysis of Jointed Plain Concrete Pavement Systems with Nondestructive Test Results using Artificial Neural Networks, Iowa State University, 2008.

    Google Scholar 

  24. S. K. Suman, S. Sinha., Pavement Condition Forecasting Through Artificial Neural Network Modelling, Int. J. Emerg. Technol. Adv. Eng. 2 (11) (2012) 474–478.

    Google Scholar 

  25. D. T. Thube, Artificial Neural Network (ANN) based pavement deterioration models for low volume roads in India, Int. J. Pavement Res. Technol. 5 (2) (2012) 115–120.

    Google Scholar 

  26. R.A. El-Hakim, S. El-Badawy, International Roughness Index Prediction for Rigid Pavements: An Artificial Neural Network Application, Adv. Mater. Res. 723 (2013) 854–860. doi:10.4028/http://www.scientific.net/AMR.723.854.

    Google Scholar 

  27. Z. Wu., S. Hu., F. Zhou., Expert Systems with Applications Prediction of Stress Intensity Factors in Pavement Cracking with Neural Networks Based on Semi-Analytical FEA, Expert Syst. Appl. 41 (4) (2014) 1021–1030. doi:10.1016/j.eswa.2013.07.063.

    Google Scholar 

  28. F. Gu., X. Luo., Y. Zhang., Y. Chen., R. Luo., R. L. Lytton, Prediction of Geogrid-Reinforced Flexible Pavement Performance using Artificial Neural Network Approach, Road Mater. Pavement Des. Des. 19 (5) (2018) 1147–1163. doi:10.1080/14680629.2017.1302357.

    Google Scholar 

  29. J. S. Daniel, J. M. Jacobs, E. Douglas., R. B. Mallick, K. Hayhoe., Impact of Climate Change on Pavement Performance: Preliminary Lessons Learned through the Infrastructure and Climate Network (ICNet), in: Int. Symp. Clim. Eff. Pavement Geotech. Infrastruct., ASCE, 2013: pp. 1–9.

    Google Scholar 

  30. M. W. Sayers, S. M. Karamihas, The little book of profiling, Basic Inf. about Meas. Interpret. Road Profiles. (1998) 100.

    Google Scholar 

  31. R. Machemehl., C. E. Lee, Dynamic Traffic Loading of Pavements, Austin, Texas, USA, 1974.

    Google Scholar 

  32. M. Cilimkovic., Neural Networks and Back Propagation Algorithm, Institute of Technology Blanchardstown, Dublin, Ireland, 2010.

    Google Scholar 

  33. T. P. Vogl, J. K. Mangis, A. K. Rigler, W. T. Zink, D. L. Alkon, Biological Cybernetics Accelerating the Convergence of the Back-Propagation Method, Biol. Cybern. 59 (4–5) (1988) 257–263.

    Google Scholar 

  34. M. I. Hossain, L.S. P. Gopisetti, M. S. Miah, Prediction of International Roughness Index of Flexible Pavements from Climatic and Traffic Data Using Artificial Neural Network Modeling, in: I.L. Al-Qadi (Ed.), Proc. Int. Conf. Highw. Pavements Airf. Technol. Airf. Highw. Pavements 2017, ASCE, Philadelphia, Pennsylvania, USA, 2017: pp. 256–267. doi:10.1061/9780784410059.

    Google Scholar 

  35. C. Chang., C. Liao., Parameter Sensitivity Analysis of Artificial Neural Network for Predicting Water Turbidity, Transfer. 3 (3) (2012) 4–5.

    Google Scholar 

  36. Y.-S. Hwang., S.-Y. Bang., Determination of the Weights of an RBF Network using Linear Discriminant Analysis, Pohang University of Science and Technology, Pohang, Korea, 2007.

    Google Scholar 

  37. J. Kaur., Techniques Used in Hypothesis Testing in Research Methodology–A Review, 4 (2) (2015) 2013–2016.

    Google Scholar 

  38. A. U. Stata, H. M. Park, Comparing Group Means: The T-test and One-way, Indiana University, Bloomington, IN, USA, 2005.

    Google Scholar 

  39. R. H. Walpole, R. H. Myers, S. L. Myers, K. Ye., Probability & Statistics for Engineers & Scientists, Eight Edit, Pearson Prentice Hall, NY, USA, 2007. doi:10.2307/2288012.

    MATH  Google Scholar 

  40. M. I. Hossain, L.S. P. Gopisetti, M. S. Miah, International Roughness Index Prediction of Flexible Pavements using Neural Networks, J. Transp. Eng. Part B Pavement. 145 (1) (2018). doi:10.1061/JPEODX.0000088.

    Google Scholar 

  41. T. Shaikhina., N. A. Khovanova, Handling Limited Datasets with Neural Networks in Medical Applications: A Small-Data Approach, Artif. Intell. Med. 75 (2017) 51–63. doi:10.1016/j.artmed.2016.12.003.

    Google Scholar 

  42. Alharbi, Fawaz, Predicting pavement performance utilizing artificial neural network (ANN) models, (Graduate Theses and Dissertations), Iowa State University, Ames, IA, USA, 2018.

    Google Scholar 

  43. H. Ceylan., O. Kaya., A. R. Tarahomi., K. Gopalakrishnan., S. Kim., D. R. Brill., Developing Rigid Airport Pavement Multiple-Slab Response Models for Top-Down Cracking Mode using Artificial Neural Networks, Civil, Construction and Environmental Engineering Conference Presentations and Proceedings, Iowa State University, Ames, IA, USA, 37, 2017.

    Google Scholar 

  44. O. Kaya., N. Garg; H. Ceylan., S. Kim., Development of Artificial Neural Networks Based Predictive Models for Dynamic Modulus of Airfield Pavement Asphalt Mixtures, International Conference on Transportation and Development 2018: Airfield and Highway Pavements, American Society of Civil Engineers, Reston, VA, 2018.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Civil Engineering and Construction, Bradley University, 1501 W. Bradley Ave, Peoria, Illinois, 61625, USA

    Mohammad Hossain

  2. Department of Civil, Construction and Environmental Engineering, Iowa State University, 403 Town Engineering Building, Ames, Iowa, 50011, USA

    Leela Sai Praveen Gopisetti

  3. Department of Electrical and Computer Engineering, Bradley University, 1501 W. Bradley Ave, Peoria, Illinois, 61625, USA

    Md. Suruz Miah

Authors
  1. Mohammad Hossain
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  2. Leela Sai Praveen Gopisetti
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  3. Md. Suruz Miah
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Correspondence to Mohammad Hossain.

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Peer review under responsibility of Chinese Society of Pavement Engineering.

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Hossain, M., Gopisetti, L.S.P. & Miah, M.S. Artificial neural network modelling to predict international roughness index of rigid pavements. Int. J. Pavement Res. Technol. 13, 229–239 (2020). https://doi.org/10.1007/s42947-020-0178-x

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  • DOI: https://doi.org/10.1007/s42947-020-0178-x

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