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Articles

Prediction of dynamic modulus of asphalt concrete using hybrid machine learning technique

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Pages 2083-2098 | Received 17 Jul 2020, Accepted 19 Oct 2020, Published online: 15 Nov 2020

References

  • AASHTO, 1993. Guide for the design of pavement structures. Washington, DC: American Association of State Highway and Transportation Officials.
  • AASHTO, 2003. Standard test method for determining dynamic modulus of Hot-Mix asphalt concrete mixtures, TP 62-03. Washington, DC: American Association of State Highway and Transportation Officials.
  • Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19 (6), 716–723.
  • Al-Khateeb, G., et al., 2006. A new simplistic model for dynamic modulus predictions of asphalt paving mixtures. Journal of Association of Asphalt Paving Technologists, 75, 1254–1293.
  • Alkroosh, I. and Nikraz, H., 2011. Correlation of pile axial capacity and CPT data using gene expression programming. Geotechnical and Geological Engineering, 29, 725–748.
  • ASTM, 2003. Standard test method for dynamic modulus of asphalt mixtures (withdrawn 2009), technical report, ASTM D3497-79 (2003). West Conshohocken, PA.
  • Azari, H., et al., 2007. Comparison of simple performance test |E*| of accelerated loading facility mixtures and prediction |E*|: use of NCHRP 1-37A and witczak’s new equations. Transportation Research Record: Journal of the Transportation Research Board, 1998, 1–9.
  • Bianchini, A., 2014. Pavement maintenance planning at the network level with principal component analysis. Journal of Infrastructure Systems, 20 (2), 04013013.
  • Birgisson, B., Sholar, G., and Roque, R., 2005. Evaluation of a predicted dynamic modulus for florida mixtures. Transportation Research Record: Journal of the Transportation Research Board, 1929, 200–207.
  • Ceylan, H., et al., 2009. Accuracy of predictive models for dynamic modulus of hot-mix asphalt. Journal of Materials in Civil Engineering, 21 (6), 286–293.
  • Chen, L., Kou, C.H., and Ma, S.W., 2014. Prediction of slump flow of high-performance concrete via parallel hyper-cubic gene-expression programming. Engineering Applications of Artificial Intelligence, 34, 66–74.
  • Cho, Y.H., Park, D. W., and Hwang, S.D., 2010. A predictive equation for dynamic modulus of asphalt mixtures used in korea. Construction and Building Materials, 24 (4), 513–519.
  • Chou, J.S., et al., 2014. Machine learning in concrete strength simulations: multi-nation data analytics. Construction and Building Materials, 73, 771–780.
  • Christensen, D.W., and Bonaquist, R., 2015. Improved hirsch model for estimating the modulus of hot-mix asphalt. Road Materials and Pavement Design, 16 (2), 254–274.
  • Christensen, D.W., Pellinen, T., and Bonaquist, R., 2003. Hirsch model for estimating the modulus of asphalt concrete. Journal of Association of Asphalt Paving Technologists, 72, 97–121.
  • Cook, M.C., et al., 2004. Guide for investigating and remediating distress in flexible pavements: california department of transportation's New procedure. Transportation Research Record: Journal of the Transportation Research Board, 1896, 147–161.
  • Daneshvar, D. and Behnood, A., 2020. Estimation of the dynamic modulus of asphalt concretes using random forests algorithm. International Journal of Pavement Engineering, 1–11. doi:https://doi.org/10.1080/10298436.2020.1741587.
  • Deepa S., Murali Krishnan J., 2020. An investigation on resilient modulus of bituminous mixtures. In: T. Mathew, G. Joshi, N. Velaga, S. Arkatkar, eds. Transportation research. Lecture notes in civil engineering, vol 45. Singapore: Springer. doi:https://doi.org/10.1007/978-981-32-9042-6_71.
  • Deepa, S., Saravanan, U., and Murali Krishnan, J., 2019. On measurement of dynamic modulus for bituminous mixtures. International Journal of Pavement Engineering, 20 (9), 1073–1089.
  • Dougan, C.E., et al., 2003. E*-dynamic modulus test protocol: problems and solutions. University of Connecticut. Connecticut Transportation Institute, CT, USA, No. CT-SPR-0003084-F-03-3.
  • El-Badawy, S., Abd El-Hakim, R., and Awed, A., 2018. Comparing artificial neural networks with regression models for Hot-Mix asphalt dynamic modulus prediction. Journal of Materials in Civil Engineering, 30 (7), 04018128.
  • EN 12697-26, 2018. Bituminous mixtures – test methods for hot mix asphalt. part 26: stiffness. Brussels: European Committee for Standardization.
  • Esfandiarpour, S.S. and Shalaby, A., 2017. Alternatives for calibration of dynamic modulus prediction models of asphalt concrete containing RAP. International Journal of Pavement Research and Technology, 10 (3), 203–218.
  • Esfandiarpour, S. and Shalaby, A., 2018. Effect of local calibration of dynamic modulus and creep compliance models on predicted performance of asphalt mixes containing RAP. International Journal of Pavement Research and Technology, 11 (5), 517–529.
  • Far, M.S.S., 2011. Development of new dynamic modulus (E*) Predictive models for hot mix asphalt mixtures. Doctoral dissertation. North Carolina State University.
  • Far, M.S.S., et al., 2009. Application of artificial neural networks for estimating dynamic modulus of asphalt concrete. Transportation Research Record: Journal of the Transportation Research Board, 2127 (1), 173–186.
  • Ferreira, C., 2001. Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13 (2), 87–129.
  • Gandomi, A.H., et al., 2010. Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. Journal of Materials in Civil Engineering, 23 (3), 248–263.
  • Ghasemi, P., et al., 2019a. Principal component analysis-based predictive modeling and optimization of permanent deformation in asphalt pavement: elimination of correlated inputs and extrapolation in modeling. Structural and Multidisciplinary Optimization, 59 (4), 1335–1353.
  • Ghasemi, P., et al., 2019b. Principal component neural networks for modeling, prediction, and optimization of hot mix asphalt dynamics modulus. Infrastructures, 4 (3), 53.
  • Ghasemi, P., et al., 2018. Modeling rutting susceptibility of asphalt pavement using principal component pseudo inputs in regression and neural networks. International Journal of Pavement Research and Technology, 11 (7), 679–690.
  • Ghasemi, P., et al., 2019c. Predicting dynamic modulus of asphalt mixture using data obtained from indirect tension mode of testing. arXiv preprint arXiv:1905.06810[Computational Engineering, Finance, and Science (cs.CE)].
  • Goh, S.W., et al., 2011. Preliminary dynamic modulus criteria of HMA for field rutting of asphalt pavements: Michigan’s experience. Journal of Transportation Engineering, 137 (1), 37–45.
  • Gopalakrishnan, K., et al., 2010. Natural selection of asphalt mix stiffness predictive models with genetic programming. In: Proceedings of intelligent engineering systems through artificial neural networks, vol. 20, paper 48. St. Louis, MO, USA.
  • IRC: 37, 2012. Guidelines for the design of flexible pavements. New Delhi: Indian Roads Congress.
  • Hosseini, S.S.S. and Gandomi, A.H., 2012. Short-term load forecasting of power systems by gene expression programming. Neural Computing and Applications, 21 (2), 377–389.
  • Juli-Gándara, L., et al., 2020. Effect of sodium chloride on the modulus and fatigue life of bituminous mixtures. Materials, 13 (9), 2126.
  • Kim, Y.R., King, M., and Momen, M., 2005. Typical dynamic moduli for North Carolina asphalt concrete mixes. Raleigh, NC: North Carolina Department of Transportation, Final Rep. No. FHWA/NC/2005-03.
  • Kim, Y.R., et al., 2004. Dynamic modulus testing of asphalt concrete in indirect tension mode. Transportation Research Record: Journal of the Transportation Research Board, 1891, 163–173.
  • Kim, Y.R., et al., 2011. LTPP computed parameter: dynamic modulus. United States: Federal Highway Administration, (No. FHWA-HRT-10-035).
  • Krishna Swamy, A., et al., 2011. Impact of RAP on the volumetric, stiffness, strength, and low-temperature properties of HMA. Journal of Materials in Civil Engineering, 23 (11), 1490–1497.
  • Li, S., 2010. A method to build a practical dynamic modulus testing protocol. In: Proceeedings of 2010 GeoShanghai international conference: paving materials and pavement analysis. Shanghai, China, 27–33.
  • Li, K., et al., 2015. Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy and Buildings, 108, 106–113.
  • Liu, J., et al., 2017. Prediction models of mixtures’ dynamic modulus using gene expression programming. International Journal of Pavement Engineering, 18 (11), 971–980.
  • Martinkauppi, J.B., Mäkiranta, A., and Hiltunen, E., 2016. PCA analysis of distributed temperature sensing data from an asphalt field. In: 2016 IEEE international conference on renewable energy research and applications (ICRERA). Birmingham, 370–374. doi: https://doi.org/10.1109/ICRERA.2016.7884362.
  • Mohammad, L.N., et al., 2014. Characterization of Louisiana asphalt mixtures using simple performance tests and MEPDG. Louisiana: Dept. of Transportation and Development, (No. FHWA/LA. 11/499).
  • Mollahasani, A., Alavi, A.H., and Gandomi, A.H., 2011. Empirical modeling of plate load test moduli of soil via gene expression programming. Computers and Geotechnics, 38 (2), 281–286.
  • Mousavi, S.M., et al., 2012. A new predictive model for compressive strength of HPC using gene expression programming. Advances in Engineering Software, 45 (1), 105–114.
  • Muthadi, N.R. and Kim, Y.R., 2008. Local calibration of mechanistic-empirical pavement design guide for flexible pavement design. Transportation Research Record: Journal of the Transportation Research Board, 2087, 131–141.
  • Nagendra, S.S. and Khare, M., 2003. Principal component analysis of urban traffic characteristics and meteorological data. Transportation Research Part D: Transport and Environment, 8 (4), 285–297.
  • Onifade, I. and Birgisson, B., 2020. Improved models for the prediction of asphalt binder dynamic shear modulus and phase angle. Construction and Building Materials, 250, 118753.
  • Ozcan, F., 2012. Gene expression programming based formulations for splitting tensile strength of concrete. Construction and Building Materials, 26 (1), 404–410.
  • Pellinen, T.K. and Witczak, M.W., 2002. Use of stiffness of hot-mix asphalt as a simple performance test. Transportation Research Record: Journal of the Transportation Research Board, 1789, 80–90.
  • Prasad, A.M., Iverson, L.R., and Liaw, A., 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9, 181–199.
  • Qin, X., Ma, L., and Wang, H., 2019. Comparison analysis of dynamic modulus of asphalt mixture: indirect tension and uniaxial compression test. Transportmetrica A: Transport Science, 15 (1), 165–178.
  • Rakesh, N., et al., 2006. Artificial neural networks—genetic algorithm based model for back calculation of pavement layer moduli. International Journal of Pavement Engineering, 7 (3), 221–230.
  • Sakhaeifar, M.S., Kim, Y.R., and Kabir, P., 2015. New predictive models for the dynamic modulus of hot mix asphalt. Construction and Building Materials, 76, 221–231.
  • Schwarz, G, 1978. Estimating the dimension of a model. The Annals of Statistics, 6 (2), 461–464.
  • Shook, J.F. and Kallas, B.F., 1969. Factors influencing the dynamic modulus of asphalt concrete. Journal of Association of Asphalt Paving Technologists, 38, 140–178.
  • Tarefder, R.A., White, L., and Zaman, M., 2005. Neural network model for asphalt concrete permeability. Journal of Materials in Civil Engineering, 17 (1), 19–27.
  • Teodorescu, L., 2006. Gene expression programming approach to event selection in high energy physics. IEEE Transactions on Nuclear Science, 53, 2221–2227.
  • Terzi, S., 2005. Modeling the deflection basin of flexible highway pavements by gene expression programming. Journal of Applied Sciences, 5 (2), 309–314.
  • Tran, N.H. and Hall, K.D., 2006. Evaluation of testing protocols for dynamic modulus of hot-mix asphalt. Transportation Research Record: Journal of the Transportation Research Board, 1970, 126–132.
  • Tripathi, M. and Singal, S.K., 2019. Use of principal component analysis for parameter selection for development of a novel water quality index: A case study of river ganga India. Ecological Indicators, 96, 430–436.
  • Weissman, S.L., 1999. Influence of tire-pavement contact stress distribution on development of distress mechanisms in pavements. Transportation Research Record: Journal of the Transportation Research Board, 1655, 161–167.
  • Wen, Y. and Wang, Y., 2019. Effect of oxidative aging on dynamic modulus of hot-mix asphalt mixtures. Journal of Materials in Civil Engineering, 31 (1), 04018348.
  • Witczak, M.W., 2005. Simple performance tests: summary of recommended methods and database. Washington, DC: Transportation Research Board, Research Report NCHRP 547.
  • Witczak, M.W., El-Basyouny, M., and El-Badawy, S., 2007. Incorporation of the new (2005) E* predictive model in the MEPDG, Tempe: Arizona State University, NCHRP 1-40D Inter-Team Technical Report.
  • Witczak, M.W., and Fonseca, O.A., 1996. Revised predictive model for dynamic (complex) modulus of asphalt mixtures. Transportation Research Record: Journal of the Transportation Research Board, 1540, 15–23.
  • Witczak, M. W., Kaloush, K., Pellinen, T., El-Basyouny, M., and Von Quintus, H., 2002. Simple performance test for superpave mix design. Washington, DC: Transportation Research Board, National Research Council, NCHRP Report 465.
  • Witczak, M.W., and Mirza, M.W., 1992. An assessment of in-situ time dependent field aging of asphalt cement mix/laydown conditions. College Park, MD, USA: University of Maryland, Final Report 3, SHRP A-005.
  • Yan, K. Z., Xu, H.B., and Shen, G.H., 2014. Novel approach to resilient modulus using routine subgrade soil properties. International Journal of Geomechanics, 14 (6), 04014025.
  • Zhang, Y., McDaniel, J.G., and Wang, M.L., 2014. Estimation of pavement macrotexture by principal component analysis of acoustic measurements. Journal of Transportation Engineering, 140 (2), 04013004.
  • Zhou, C., et al., 2003. Evolving accurate and compact classification rules with gene expression programming. IEEE Transactions on Evolutionary Computation, 7 (6), 519–531.

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