References
- AASHTO, 2008. “Mechanistic-empirical pavement design guide: A manual of practice,” American Association of State Highway and Transportation Officials, United States, 2008.
- Al-Kandari, N. M., and Jolliffe, I. T., 2001. Variable selection and interpretation of covariance principal components. Communications in Statistics - Simulation and Computation, 30 (2), 339–354.
- Alwosheel, A., van Cranenburgh, S., and Chorus, C. G., 2018. Is your dataset big enough? sample size requirement when using artificial neural networks for discrete choice analysis. Journal of Choice Modelling.
- Azam, A. M., Cameron, D. A., and Rahman, M. M., 2013. Model for prediction of resilient modulus incorporating matric suction for recycled unbound granular materials. Canadian Geotechnical Journal, 50 (11), 1143–1158.
- Banda, T. D., and Kumarasamy, M., 2020. Application of multivariate statistical analysis in the development of a surrogate water quality index (WQI) for South African watersheds. Water, 12, 1584.
- Barua, L., et al., 2020. A gradient boosting approach to understanding airport runway and taxiway pavement deterioration. International Journal of Pavment Engineering. doi:10.1080/10298436.2020.1714616.
- Beale, R., and Jackson, T., 1990. Neural computing: An introduction. Bristol: Adam Hilger IOP publishing Ltd.
- Biau, G., Cadre, B., and Rouviere, L., 2019. Accelerated gradient boosting. Machine Learning, 108, 971–992.
- Bilodeau, J. P., Plamondon, C. O., and Dore, G., 2016. Estimation of resilient modulus of unbound granular materials used as pavement base: combined effects of grain size distribution and aggregate source frictional properties. Materials and Structures, 49 (10), 1–11.
- De Clereq, D., Wen, Z., and Fei, F., 2019. Determinants of efficiency in anaerobic bio-waste co-digestion facilities: A data envelopment analysis and gradient boosting approach. Applied Energy, 253 (113570), 1–11.
- Ekblad, J., 2008. Statistical evaluation of resilient models characterizing coarse granular materials. Materials and Structures, 41 (3), 509–525.
- Esfahani, M. A., and Goli, A., 2018. Effects of aggregate gradation on resilient modulus and CBR in unbound granular materials. International Journal of Transportation Engineering, 5 (4), 367–381.
- Esmaeili, M., et al., 2014. Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Engineering with Computers, 30, 549–558.
- Freund, Y., 1995. Boosting a weak learning algorithm by majority. Information and Computation, 121, 256–285.
- Freund, Y., Freund, Y., and Shapire, R. E., 1996. Experiments with a new boosting algorithm. In: Machine learning: proceedings of the thirteenth international conference. San Francisco: Morgan Kaufmann Publishers
- Freund, Y., and Schapire, R. E., 1997. A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55 (1), 119–139.
- Friedman, J. H., 2001. Greedy Function Approximation: a gradient boosting machine. The Annals of Statistics, 29 (3), 1189–1232.
- Friedman, J. H., 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38 (4), 367–378.
- Ghorbani, B., et al., 2020. Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils. Soils and Foundations, 60, 398–412.
- Giuliani, A., 2017. The application of principal component analysis to drug discovery and biomedical data. Drug Discovery Today, 22 (7), 1069–1076.
- Gomez-Armesto, A., et al., 2020. Modelling Hg mobility in podzols: role of soil components and environmental implications. Environmental Pollution, 260, 114040.
- Gunaydin, O., 2009. Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environmental Geology, 57, 203–215.
- NCHRP, 2004. Guide for mechanistic empirical design of new and rehabilitated pavement structure: part 2 design inputs: chapter 3 environmental effects. Washington, DC: Transportation Research Board, National Research Council.
- ARA, Inc., ERES Consultants Division, 2004. Guide for mechanistic-empirical design of new and rehabilitated pavement structures. Washington, DC: Transportation Research Board of the National Academies.
- Hanandeh, S., Ardah, A., and Abu-Farsakh, M., 2020. Using artificial neural network and genetic algorithm to estimate the resilient modulus for stabilized subgrade and propose new empirical formula. Transportation Geotechnics, 24 (100358), 1–8.
- Hardle, W. K., and Simar, L., 2015. Applied multivariate statistical analysis. Berlin: Springer-Verlag.
- Hastie, T., Tibshirani, R., and Friedman, J., 2009. The elements of statistical learning. New York, NY: Springer.
- Hecht-Nielsen, R., 1989. Neurocomputing. Boston, MA: AddisoN-Wesley Longman Publishing Co. Inc.
- Heidarabadizadeh, N., Ghanizadeh, A. R., and Behnood, A., 2021. Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm. Construction and Building Materials, 275, 122140.
- Hotelling, H., 1933. Analysis of complex of statistical variables into principal components. Journal of Educational Psychology, 24 (6), 417–441.
- Ikeagwuani, C. C., and Nwonu, D. C., 2019. Resilient modulus of lime-bamboo ash stabilized subgrade soil with different compactive energy. Geotechnical and Geological Engineering, 37, 3557–3565.
- Ikeagwuani, C. C. and Nwonu, D. C., 2020. Model performance assessment in resilient modulus modelling: a multimodel approach. Road Materials and Pavement Design. doi:10.1080/14680629.2020.1753100.
- Jang, J. S. R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, man and Cybernetics, 23 (3), 665–685.
- Jang, J. S. R., Sun, C. T., and Mitzutani, E., 1997. Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. New Delhi, India: Prentice Hall.
- Ji, R., et al., 2014. Evaluation of resilient modulus of subgrade and base materials in Indiana and its implementation in MEPDG. Scientific World Journal. doi:10.1155/2014/372838.
- Jibon, M., Mishra, D., and Kassem, E., 2020. Laboratory characterisation of fine-grained soils for pavement ME design implementation in Idaho. Transportation Geotechnics, 25, 100395.
- Jolliffe, I. T., 2002. Rotation and interpretation of principal components. In: Principal component analysis. Springer series in StatisticsNew York: Springer, pp. 269–298.
- Jolliffe, I. T., and Cadima, J., 2016. Principal component analysis: a review and recent developments. Phil Trans R Soc A, 374, 20150202.
- Kaloop, M. R., et al., 2019. Particle swarm optimization algorithm-extreme learning machine (PSO-ELM) model for predicting resilient modulus of stabilized aggregate bases. Applied Sciences, 9, 3221.
- Khasawneh, M. A., and Al-jamal, N. F., 2019. Modeling resilient modulus of fine-grained materials using different statistical techniques. Transportation Geotechnics, 21, 100263.
- Kor, K., and Altun, G., 2020. Is support vector regression method suitable for predicting rate of penetration? Journal of Petroleum Science and Engineering, 194, 1–18. doi:10.1016/j.petrol.2020.107542.
- Kuo, Y. L., et al., 2009. ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil. Computers and Geotechnics, 36 (3), 503–516.
- Lekarp, F., Isacsson, U., and Dawson, A., 2000. State of the art. I: Resilient response of unbound aggregates. Journal of Transportation Engineering, 126, 66–75.
- Li, D., and Selig, E. T., 1994. Resilient modulus for fine-grained subgrade soils. Journal of Geotechnical Engineering, 120 (6), 939–957.
- Liu, S., et al., 2016. Multivariate correlation among resilient modulus and cone penetration test parameters of cohesive subgrade soils. Engineering Geology, 209, 128–142.
- LTPP, 1996. Resilient modulus of unbound granular base/subbase materials and subgrade soils. FHWA: U.S. Department of Transportation.
- LTPP, 2001 “Guide to LTPP traffic data collection and processing,” U.S. Department of Transportation, Federal Highway Administration, McLean, Virginia, 2001.
- LTPP, 2018 “Long-term Pavement Performance IMS package [Dataset],” Long-term Pavement Performance, 2018. [Online]. https://infopave.fhwa.dot.govt/DownloadTracker/Bucket/23228.
- McCulloch, W. S., and Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.
- Mousa, E., El-Badawy, S., and Azam, A., 2020. Evaluation of reclaimed asphalt pavement as base/subbase material in Egypt. Transportation Geotechnics, 100414. doi:10.1016/j.trgeo.2020.100414.
- Mousavi, S. H., Gabr, M. A., and Borden, R. H., 2016. Subgrade resilient modulus prediction from light weight deflectometer. Canadian Geotechnical Journal, 54 (3).
- Mousavi, S. H., Gabr, M. A., and Borden, R. H., 2018. Resilient modulus prediction of soft low-plasticity Piedmont residual soil using dynamic cone penetrometer. Journal of Rock Mechanics and Geotechnical Engineering, 10 (2), 323–332.
- Murthy, V. S., 2002. Principles and practices of soil mechanics and foundation engineering. New York: Marcek Decker INC.
- Nnaji, C. C., and Agunwamba, J. C., 2013. The environmental impact of crude oil formation water: a multivariate approach. Journal of Water Chemistry and Technology, 35 (5), 222–232.
- Nnaji, C. C. and Nnam, J. P., 2019. Assessment of potability of stored rainwater and impact of environmental conditions on its quality. International Journal of Environmental Science and Technology, 16, 8471–8484.
- Nwonu, D. C., and Ikeagwuani, C. C., 2019. Evaluating the effect of agro-based admixture on lime-treated expansive soil for subgrade material. International Journal of Pavement Engineering, 1–16. doi:10.1080/10298436.2019.1703979.
- Oskooei, P. R., et al., 2020. Application of artificial neural network models for predicting the resilient modulus of recycled aggregates. International Journal of Pavement Engineering, doi:10.1080/10298436.2020.1791863.
- Pearson, K., 1901. On lines and planes of closest fit to systems of points in space. Phil Mag, 2, 559–572.
- Persson, C., et al., 2017. Multi-site solar power forecasting using gradient boosted regression. Solar Energy, 150, 423–430.
- Ranasinghe, R. A. T. M., et al., 2017. Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results. Journal of Rock Mechanics and Geotechnical Engineering, 9 (2), 340–349.
- Rashid, T., 2016. Make your own neural network: A gentle journey through the mathematics of neural networks, and making your own using python computer language. North Charleston, SC: Createspace independent publishing.
- Rawlings, J. O., Pantula, S. G., and Dickey, D. A., 1998. Applied regression analysis: A research tool. New York: Springer-Verlag.
- Ruttanaporamakul, P., 2012. Resilient moduli properties of compacted unsaturated subgrade materials. Arlington: University of Texas.
- Schapire, R. E., 1990. The strength of weak learnability. Machine Learning, 5, 197–227.
- Solanki, P., Zaman, M. M., and Dean, J., 2010. Resilient modulus of clay subgrades stabilized with lime, class C fly ash, and cement kiln dust for pavement design. Journal of the Transportation Research Board, 2186, 101–110.
- Su, Y., et al., 2021. Effect of water content on resilient modulus and damping ratio of fine/coarse soil mixtures with varying coarse grain contents. Transportation Geotechnics, 26, 100452. doi:10.1016/j.trgeo.2020.100452.
- Takagi, T., and Sugeno, M., 1985. Fuzzy identification of systems and its application to modeling and control. IEE Transactions on Systems, man and Cybernetics, 5 (1), 116–132.
- Taylor, R., 1990. Interpretation of the correlation coefficient: a basic review. Journal of Medical Sonography, 1, 35–39.
- Titi, H. H., and Matar, M. G., 2018. Estimating resilient modulus of base aggregates for mechanistic-empirical pavement design and performance evaluation. Transportation Geotechnics, 17, 141–153.
- Tutumluer, E., 2013. Practices for unbound aggregate pavement layers. Washington, DC: Transportation Research Board.
- Tutumluer, E., Mishra, D., and Butt, A., 2009. “Characterization of Illinois aggregates for subgrade replacement and subbase,” University of Illinois, Urbana-Champagne, Illinois, 2009.
- Zhalehjoo, N., et al., 2018. The effect of instrumentation on the determination of the resilient modulus of unbound granular materials using advanced repeated load triaxial testing. Transportation Geotechnics, 14, 190–201.
- Zhang, J., et al., 2020a. Back-calculation of elastic modulus of high liquid limit clay subgrades based on viscoelastic theory and multipopulation genetic algorithm. International Journal of Geomechanics, 20 (10), 04020194.
- Zhang, M., et al., 2020b. Analysis of critical factors to asphalt overlay performance using gradient boosted models. Construction and Building Materials, 263 (120083), 1–9.
- Zhang, S., Pak, R. Y., and Zhang, J.2021. Vertical time-harmonic coupling vibration of an impermeable, rigid, circular plate resting on a finite, poroelastic soil layer. Acta Geotechnica, 16, 911–935.