Prediction of the crack condition of highway pavements using machine learning models

References

• Aleadelat, W., Ksaibati, K., Wright, C. H., & Saha, P. (2018). Evaluation of pavement roughness using an android-based smartphone. Journal of Transportation Engineering, Part B: Pavements, 144(3), 04018033. doi: 10.1061/JPEODX.0000058
   Web of Science ®Google Scholar
• Attoh-Okine, N. O. (1994). Predicting roughness progression in flexible pavement using adaptive neural network. In Proceedings of the Third International Conference on Managing Pavement, San Antonio, Texas, Vol. 1, pp. 52–62.
   Google Scholar
• Bektaş, B. A. (2017). Use of recursive partitioning to predict national bridge inventory condition ratings from national bridge elements condition data. Transportation Research Record: Journal of the Transportation Research Board, 2612(1), 29–38. doi: 10.3141/2612-04
   Google Scholar
• Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA, USA: Wadsworth and Brooks/Cole.
   Google Scholar
• Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 32(5), 361–378. doi: 10.1111/mice.12263
   Web of Science ®Google Scholar
• De’ath, G., & Fabricius, K. E. (2000). Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology, 81(11), 3178–3192. doi: 10.1890/0012-9658(2000)081
   Web of Science ®Google Scholar
• DeLisle, R., Sullo, P., & Grivas, D. (2003). Network-level pavement performance prediction model incorporating censored data. Transportation Research Record: Journal of the Transportation Research Board, 1853(1), 72–79. doi: 10.3141/1853-09
   Google Scholar
• Elbagalati, O., Elseifi, M. A., Gaspard, K., & Zhang, Z. (2018). Development of an enhanced decision-making tool for pavement management using a neural network pattern-recognition algorithm. Journal of Transportation Engineering, Part B: Pavements, 144(2), 04018018. doi: 10.1061/JPEODX.0000042
   Web of Science ®Google Scholar
• Fang, M., Han, C., Xiao, Y., Han, Z., Wu, S., & Cheng, M. (2018). Prediction modelling of rutting depth index for asphalt pavement using de-noising method. International Journal of Pavement Engineering, 1. doi: 10.1080/10298436.2018.1512712
   Google Scholar
• Firoozi Yeganeh, S., Golroo, A., & Jahanshahi, M. R. (2019). Automated rutting measurement using an inexpensive RGB-D sensor fusion approach. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04018061. doi: 10.1061/JPEODX.0000095
   Web of Science ®Google Scholar
• Florida Department of Transportation (2017). Flexible pavement condition survey handbook/Florida Department of Transportation, State Materials Office, Pavement Evaluation Section. Tallahassee, FL: Florida Department of Transportation.
   Google Scholar
• Funahashi, K. (1989). On the approximate realization of continuous mappings by Neural Networks. Neural Networks, 2(3), 183–192. doi: 10.1016/0893-6080(89)90003-8
   Web of Science ®Google Scholar
• George, K. P., Rajagopal, A. S., & Lim, L. K. (1989). Models for predicting pavement deterioration. Transportation Research Record: Journal of the Transportation Research Board, 1215, 1–7.
   Google Scholar
• Gunther, E. C., Stone, D. J., Gerwien, R. W., Bento, P., & Heyes, M. P. (2003). Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. Proceedings of the National Academy of Sciences, 100(16), 9608–9613. doi: 10.1073/pnas.1632587100
   PubMed Web of Science ®Google Scholar
• Haykin, S. (1994). Neural networks – A comprehensive foundation. New York: Macmillan College Publishing Company.
   Google Scholar
• Hoffman, L. L., & Kong, F. (2001). Flexible pavement performance prediction model on the basis of pavement condition data. In ARRB Transport Research Ltd Conference, 2001, Melbourne, Victoria, Australia.
   Google Scholar
• Hossain, M. I., Gopisetti, L. S. P., & Miah, M. S. (2019). International roughness index prediction of flexible pavements using Neural Networks. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04018058. doi: 10.1061/JPEODX.0000088
   Web of Science ®Google Scholar
• Inkoom, S., Sobanjo, J. O., Thompson, P. D., Kerr, R., & Twumasi-Boakye, R. (2017). Bridge health index: Study of element condition states and importance weights. Transportation Research Record: Journal of the Transportation Research Board, 2612(1), 67–75. doi: 10.3141/2612-08
   Google Scholar
• Inkoom, S. & Sobanjo, J. (2018a). Availability function as bridge element's importance weight in computing overall bridge health index. Structure and Infrastructure Engineering, 14:12, 1598–1610. doi: 10.1080/15732479.2018.1476561
   Google Scholar
• Inkoom, S. & Sobanjo, J. (2018b). Multilevel competing risks model for for the performance assessment of highway pavement. International Journal of Pavement Engineering, 1–11. doi: 10.1080/10298436.2018.1554216
   Google Scholar
• JMP (2007). JMP software manual version 7. Cary, NC: SAS Institute Inc.
   Google Scholar
• Johnson, K. D., & Cation, K. A. (1992). Performance prediction development using three indexes for North Dakota pavement management system. Transportation Research Record: Journal of the Transportation Research Board, 1344, 22–30.
   Google Scholar
• Kuo, S. S., Hoffman, L. L., Kong, F., & Gianesini, G. (2000). Flexible pavement performance prediction model on the basis of pavement condition data. Prepared for the Florida DOT, Report No. WPI 403703, University of Central Florida, Orlando, FL.
   Google Scholar
• Lawrence, M., & Petterson, A. (1993). Brainmaker professional: Neural network simulation software user's guide and reference manual. California Scientific Software, Nevada City, California.
   Google Scholar
• Li, M., & Wang, H. (2018). Prediction of asphalt pavement responses from FWD surface deflections using soft computing methods. Journal of Transportation Engineering, Part B: Pavements, 144(2), 04018014. doi: 10.1061/JPEODX.0000044
   Web of Science ®Google Scholar
• Lou, Z., Gunaratne, M., Lu, J. J., & Dietrich, B. (2001). Application of neural network model to forecast short-term pavement crack condition: Florida case study. Journal of Infrastructure Systems, 7(4), 166–171. doi: 10.1061/(ASCE)1076-0342(2001)7:4(166)
   Google Scholar
• Lunetta, K. L., Hayward, L. B., Segal, J., & Van Eerdewegh, P. (2004). Screening large-scale association study data: exploiting interactions using random forests. BMC Genetics, 5(1), 32. doi: 10.1186/1471-2156-5-32
   PubMed Web of Science ®Google Scholar
• Madanat, S., & Shin, H. (1998). Development of distress progression models using panel data sets of in-service pavements. Transportation Research Record: Journal of the Transportation Research Board, 1643(1), 20–24. doi: 10.3141/1643-04
   Google Scholar
• Mauch, M., & Madanat, S. (2001). Semiparametric hazard rate models of reinforced concrete bridge deck deterioration. Journal of Infrastructure Systems, 7(2), 49–57. doi: 10.1061/(ASCE)1076-0342(2001)7:2(49)
   Google Scholar
• Meier, R. W., & Rix, G. J. (1995). Backcalculation of flexible pavement moduli using artificial neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1448, 75–82.
   Google Scholar
• Nader, A., Abd El-Hakim, R. T., El-Badawy, S. M., & Afify, H. A. (2018). International roughness index prediction model for flexible pavements. International Journal of Pavement Engineering, 1. doi: 10.1080/10298436.2018.1441414
   Google Scholar
• Nasseri, S., Gunaratne, M., Yang, J., & Nazef, A. (2009). Application of improved crack prediction methodology in Florida's highway network. Transportation Research Record: Journal of the Transportation Research Board, 2093(1), 67–75. doi: 10.3141/2093-08
   Google Scholar
• Nader, K., Danial, M. S., Shahaboddin, S., Pouria, H., Amir, M., & Kwok-wing, C. (2019) Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road), Engineering Applications of Computational Fluid Mechanics, 13(1), 188–198, doi: 10.1080/19942060.2018.1563829
   Google Scholar
• Nivedya, M. K., & Mallick, R. B. (2018). Artificial neural network-based prediction of field permeability of hot mix asphalt pavement layers. International Journal of Pavement Engineering, 19, 1. doi: 10.1080/10298436.2018.1519189
   Google Scholar
• Pittou, M., Karlaftis, M. G., & Li, Z. (2009). Nonparametric binary recursive partitioning for deterioration prediction of infrastructure elements. Advances in Civil Engineering, 2009, Article ID 809767, 1–12. doi: 10.1155/2009/809767
   Google Scholar
• Prozzi, J., & Madanat, S. (2000). Using duration models to analyze experimental pavement failure data. Transportation Research Record: Journal of the Transportation Research Board, 1699(1), 87–94. doi: 10.3141/1699-12
   Google Scholar
• Saliminejad, S., & Gharaibeh, N. (2013). Impact of error in pavement condition data on the output of network-level pavement management systems. Transportation Research Record: Journal of the Transportation Research Board, 2366(1), 110–119. doi: 10.3141/2366-13
   Google Scholar
• Sall, J., Lehman, A., Stephens, M. L., & Creighton, L. (2012). JMP start statistics: a guide to statistics and data analysis using JMP. Cary, NC: SAS Institute Inc.
   Google Scholar
• SAS (2004). SAS/STAT user manuals: Statistics. Cary, NC: SAS Institute Inc.
   Google Scholar
• Sobanjo, J. O. (1997). A neural network approach to modeling bridge deterioration. ASCE Computing in Civil Engineering, 623–626.
   Google Scholar
• Sobanjo, J. O., & Thompson, P. D. (2013). Development of risk models for Florida's bridge management system. Florida Department of Transportation, Technical Report BDK83 977-11. Accessed from May 12, 2017 https://ntl.bts.gov/lib/47000/47800/47813/FDOT-BDK83-977-11-rpt.pdf
   Google Scholar
• Su, X., Tsai, C. L., Wang, H., Nickerson, D. M., & Li, B. (2009). Subgroup analysis via recursive partitioning. Journal of Machine Learning Research. (Feb), 10, 141–158.
   Google Scholar
• Terzi, S. (2006). Modeling the pavement present serviceability index of flexible highway pavements using data mining. Journal of Applied Sciences, 6(1), 193–197. doi: 10.3923/jas.2006.193.197
   Google Scholar
• Thomas, O., & Sobanjo, J. (2013). Comparison of Markov chain and semi-Markov models for crack deterioration on flexible pavements. Journal of Infrastructure Systems, 19(2), 186–195. doi: 10.1061/(ASCE)IS.1943-555X.0000112
   Web of Science ®Google Scholar
• Wang, W., Zhang, A., Wang, K. C., Braham, A. F., & Qiu, S. (2018). Pavement crack width measurement based on Laplace's equation for continuity and unambiguity. Computer‐Aided Civil and Infrastructure Engineering, 33(2), 110–123. doi: 10.1111/mice.12319
   Web of Science ®Google Scholar
• Yang, J., Gunaratne, M., Lu, J. J., & Dietrich, B. (2005). Use of recurrent Markov chains for modeling the crack performance of flexible pavements. Journal of Transportation Engineering, 131(11), 861–872. doi: 10.1061/(ASCE)0733-947X(2005)131:11(861)
   Google Scholar
• Yang, J., Lu, J. J., & Gunaratne, M. (2003). Application of neural network models for forecasting of pavement crack index and pavement condition rating. Transportation Research Record: Journal of the Transportation Research Board, 185, p. 152. doi: 10.3141/1853-01
   Google Scholar
• Zhang, A., Wang, K. C. P., Li, B., Yang, E., Dai, X., Peng, Y., Fei, Y., Liu, Y., Li, J. Q., & Chen, C. (2017). Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network. Computer‐Aided Civil and Infrastructure Engineering, 32(10), 805–819. doi: 10.1111/mice.12297
   Web of Science ®Google Scholar
• Zhang, Z., Sun, C., Bridgelall, R., & Sun, M. (2018). Application of a machine learning method to evaluate road roughness from connected vehicles. Journal of Transportation Engineering, Part B: Pavements, 144(4), 04018043. doi: 10.1061/JPEODX.0000074
   Web of Science ®Google Scholar