Developing an optimized faulting prediction model in Jointed Plain Concrete Pavement using artificial neural networks and random forest methods

References

• AASHTO, 2010. Guide for the local calibration of the mechanistic-empirical pavement design guide. Washington, DC 20001: American Association of State Highway and Transportation Officials.
   Google Scholar
• Augeri, M.G., Greco, S., and Nicolosi, V., 2019. Planning urban pavement maintenance by a new interactive multiobjective optimization approach. European Transport Research Review, 11 (1), 1–14.
   Web of Science ®Google Scholar
• Bakhsh, K.N. and Zollinger, D., 2014. Faulting prediction model for design of concrete pavement structures. Pavement Materials, Structures, and Performance: ASCE Journal of Transportation Engineering, 239, 327–342.
   Google Scholar
• Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32.
   Web of Science ®Google Scholar
• Chen, Y. and Lytton, R.L., 2019. Development of a new faulting model in Jointed Concrete Pavement using LTPP data. Transportation Research Record, 2673 (5), 407–417.
   Web of Science ®Google Scholar
• Daneshvar, D. and Behnood, A., 2020. Estimation of the dynamic modulus of asphalt concretes using random forests algorithm. International Journal of Pavement Engineering, 23 (2), 1–11.
   Web of Science ®Google Scholar
• Ehsani, M., et al., 2021. Compressive strength prediction of ordinary concrete, fly ash concrete, and slag concrete by novel techniques and presenting their optimal mixtures. Amirkabir Journal of Civil Engineering, 53 (10), 1–1.
   Google Scholar
• Esfe, M.H., et al., 2017. Multi-objective optimization of cost and thermal performance of double walled carbon nanotubes/water nanofluids by NSGA-II using response surface method. Applied Thermal Engineering, 112, 1648–1657.
   Web of Science ®Google Scholar
• Facts, L.F., 2007. National Asphalt Pavement Association (NAPA), Landham, MD, no date.
   Google Scholar
• Gong, H., et al., 2018. Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189, 890–897.
   Web of Science ®Google Scholar
• Gong, H., et al., 2019. Investigating impacts of asphalt mixture properties on pavement performance using LTPP data through random forests. Construction and Building Materials, 204, 203–212.
   Web of Science ®Google Scholar
• Grömping, U., 2009. Variable importance assessment in regression: linear regression versus random forest. The American Statistician, 63 (4), 308–319.
   Web of Science ®Google Scholar
• Guisan, A., Edwards Jr, T.C., and Hastie, T., 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157 (2-3), 89–100.
   Web of Science ®Google Scholar
• He, Z., et al., 2014. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509, 379–386.
   Web of Science ®Google Scholar
• Heidari, E., Sobati, M.A., and Movahedirad, S., 2016. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemometrics and Intelligent Laboratory Systems, 155, 73–85.
   Web of Science ®Google Scholar
• Hossain, M., Gopisetti, L.S.P., and Miah, M. S, 2020. Artificial neural network modelling to predict international roughness index of rigid pavements. International Journal of Pavement Research and Technology, 13, 229–239.
   Google Scholar
• Jeong, J.-H. and Zollinger, D.G., 2001. Characterization of stiffness parameters in design of continuously reinforced and jointed pavements. Transportation Research Record, 1778 (1), 54–63.
   Google Scholar
• Jung, Y.S. and Zollinger, D.G., 2011. New laboratory-based mechanistic–empirical model for faulting in jointed concrete pavement. Transportation Research Record, 2226 (1), 60–70.
   Google Scholar
• Ker, H.-W., Lee, Y.-H., and Lin, C.-H., 2008. Development of faulting prediction models for rigid pavements using LTPP database. Statistics, 218 (0037.0), 0037.0030.
   Google Scholar
• Kostopoulos, A. and Grapsa, T., 2009. Self-scaled conjugate gradient training algorithms. Neurocomputing, 72 (13-15), 3000–3019.
   Web of Science ®Google Scholar
• Kumar, A., et al., 2017. NSGA-II approach for multi-objective optimization of wire electrical discharge machining process parameter on inconel 718. Materials Today: Proceedings, 4 (2), 2194–2202.
   Google Scholar
• Lu, P. and Tolliver, D., 2012. Pavement treatment short-term effectiveness in IRI change using long-term pavement program data. Journal of Transportation Engineering, 138 (11), 1297–1302.
   Google Scholar
• Mao, Z., 2012. Life-cycle assessment of highway pavement alternatives in aspects of economic, environmental, and social performance. Texas A & M University. College Station, Texas, USA.
   Google Scholar
• Mapa, D.G., et al., 2020. Evaluating early-age stresses in Jointed Plain Concrete Pavement repair slabs. ACI Materials Journal, 117 (4), 119–132.
   Web of Science ®Google Scholar
• Moniri, A., et al., 2020. Investigating the ANN model for cracking of HMA in terms of temperature, RAP and fibre content. International Journal of Pavement Engineering, 23 (3), 1–13.
   Web of Science ®Google Scholar
• Mustafa, M., et al., 2012. River suspended sediment prediction using various multilayer perceptron neural network training algorithms—a case study in Malaysia. Water Resources Management, 26 (7), 1879–1897.
   Web of Science ®Google Scholar
• Naseri, H., et al., 2021. Sustainable pavement maintenance and rehabilitation planning using differential evolutionary programming and coyote optimisation algorithm. International Journal of Pavement Engineering, 1–18.
   Web of Science ®Google Scholar
• Roweis, S., 1996. Levenberg-marquardt optimization. Notes, University of Toronto.
   Google Scholar
• Safak, V., 2020. Min-Mid-Max Scaling, Limits of Agreement, and Agreement Score. arXiv preprint arXiv:2006.12904.
   Google Scholar
• Saghafi, B., et al., 2009. Artificial neural networks and regression analysis for predicting faulting in jointed concrete pavements considering base condition. International Journal of Pavement Research and Technology, 2 (1), 20–25.
   Google Scholar
• Shahin, M.Y., 2006. Pavement management for airports, roads, and parking lots. 2nd ed. New York, USA: Springer.
   Google Scholar
• Simpson, A.L., etal., 1994. Sensitivity analyses for selected pavement distresses. SHRP report No. Washington, DC, USA: Transportation research board.
   Google Scholar
• Sollazzo, G., Fwa, T., and Bosurgi, G, 2017. An ANN model to correlate roughness and structural performance in asphalt pavements. Construction and Building Materials, 134, 684–693.
   Web of Science ®Google Scholar
• Strobl, C., et al., 2008. Conditional variable importance for random forests. BMC Bioinformatics, 9 (1), 1–11.
   PubMedGoogle Scholar
• Van Wijk, A.J., 1985. Rigid pavement pumping:(1) subbase erosion and (2) economic modeling (subdrainage, rehabilitation, rigid pavements). Purdue University. West Lafayette, Indiana.
   Google Scholar
• Vo-Duy, T., et al., 2017. Multi-objective optimization of laminated composite beam structures using NSGA-II algorithm. Composite Structures, 168, 498–509.
   Web of Science ®Google Scholar
• Wang, W.-N. and Tsai, Y.-C.J., 2013. Back-propagation network modeling for concrete pavement faulting using LTPP data. International Journal of Pavement Research and Technology, 6 (5), 651.
   Google Scholar
• Yang, X., et al., 2020. Sensitivity of rigid pavement performance predictions to individual climate variables using pavement ME design. Journal of Transportation Engineering, Part B: Pavements, 146 (3), 04020028.
   Web of Science ®Google Scholar
• Yepes, V., et al., 2016. Optimal pavement maintenance programs based on a hybrid greedy randomized adaptive search procedure algorithm. Journal of Civil Engineering and Management, 22 (4), 540–550.
   Web of Science ®Google Scholar
• Younos, M., et al., 2020. Multi-input performance prediction models for flexible pavements using LTPP database. Innovative Infrastructure Solutions, 5 (1), 1–11.
   Web of Science ®Google Scholar
• Yu, H., et al., 1998. Performance of concrete pavements. volume III: improving concrete pavement performance. Washington, DC, USA: Transportation research board.
   Google Scholar
• Zanin, L. and Marra, G, 2012. A comparative study of the use of generalized additive models and generalized linear models in tourism research. International Journal of Tourism Research, 14 (5), 451–468.
   Web of Science ®Google Scholar
• Zhang, W., et al., 2021. Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15 (1), 27–40.
   Google Scholar