Abdullah, G. M., Ahmad, M., Babur, M., Badshah, M. U., Al-Mansob, R. A., Gamil, Y., & Fawad, M. (2024). Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil. Scientific Reports, 14(1), 2323.
Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. Proceedings of the IEEE international conference on neural networks, 69-73.
Behnood, A. (2018). Soil and clay stabilization with calcium- and non-calcium-based additives: A state-of-the-art review of challenges, approaches and techniques. Transportation Geotechnics, 17, 14–32. https://doi.org/10.1016/j.trgeo.2018.08.002
Das, S. K., Samui, P., & Sabat, A. K. (2011). Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil. Geotechnical and Geological Engineering, 29(3), 329–342.
de Araújo, M. T., Ferrazzo, S. T., Chaves, H. M., da Rocha, C. G., & Consoli, N. C. (2023). Mechanical behavior, mineralogy, and microstructure of alkali-activated wastes-based binder for a clayey soil stabilization. Construction and Building Materials, 362, 129757.
Dhaliwal, S. S., Nahid, A.-A., & Abbas, R. (2018). Effective intrusion detection system using XGBoost. Information, 9, 149.
Dorigo, M., Birattari, M., & Stutzle, T. (2007). Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28-39.
Ghadir, P., & Ranjbar, N. (2018). Clayey soil stabilization using geopolymer and Portland cement. Construction and Building Materials, 188, 361–371.Ghanizadeh, A. R., & Naseralavi, S. S. (2023). Intelligent prediction of unconfined compressive strength and Young's modulus of lean clay stabilized with iron ore mine tailings and hydrated lime using Gaussian process regression. Journal of Soft Computing in Civil Engineering, 7(4).
Ghanizadeh, A. R., & Safi Jahanshahi, F. (2024). Intelligent Modeling of Unconfined Compressive Strength of Stabilized Clay Soil using Gene Expression Programming. Road, 32(119), 137-156. https://doi.org/10.22034/road.2023.399936.2171 Ghanizadeh, A. R., Heidarabadizadeh, N., Bayat, M., & Khalifeh, V. (2022). Modeling of unconfined compressive strength and Young's modulus of lime and cement stabilized clayey subgrade soil using evolutionary polynomial regression (EPR). International Journal of Mining and Geo-Engineering. https://doi.org/10.22059/IJMGE.2022.306688.594858
Ghanizadeh, A. R., Safi Jahanshahi, F., & Naseralavi, S. S. (2024). Intelligent modelling of unconfined compressive strength of cement stabilised iron ore tailings: a case study of Golgohar mine. European Journal of Environmental and Civil Engineering, 28(8), 1759-1787.
Ghanizadeh, A. R., Safi Jahanshahi, F., & Ziayi, A. (2025). Presenting a Model for Predicting CBR and UCS of Expensive Soil Stabilized with Hydrated Lime Activated with Rice Husk Ash Using the Hybrid MARS-EBS Method. Road, 33(122), 45-66. https://doi.org/10.22034/road.2024.431647.2231
Güllü, H. (2014). Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom ash. Engineering Applications of Artificial Intelligence, 35, 143–157.
Heidari Dezfuli, T., & Ghanizadeh, A. R. (2020). Prediction of compressive and tensile strength of clayey subgrade soil stabilized with Portland cement and iron ore mine tailing using computational intelligence methods. Civil Infrastructure Researches, 6(1), 73–88.
Hoy, M., Horpibulsuk, S., & Arulrajah, A. (2016). Strength development of recycled asphalt pavement–fly ash geopolymer as a road construction material. Construction and Building Materials, 117, 209–219. https://doi.org/10.1016/j.conbuildmat.2016.04.136
Javdanian, H., & Lee, S. (2019). Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: A computational intelligence approach. Engineering with Computers, 35(1), 191–199.
Johari, A., Golkarfard, H., Davoudi, F., & Fazeli, A. (2021). A predictive model based on the experimental investigation of collapsible soil treatment using nano-clay in the Sivand Dam region, Iran. Bulletin of Engineering Geology and the Environment, 80(9), 6725-6748.
Johari, A., Golkarfard, H., Davoudi, F., & Fazeli, A. (2022). Experimental investigation of collapsible soils treatment using nano-silica in the Sivand Dam Region, Iran. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(2), 1301-1310.
Johari, A., Javadi, A., & Habibagahi, G. (2011). Modelling the mechanical behaviour of unsaturated soils using a genetic algorithm-based neural network. Computers and Geotechnics, 38(1), 2-13.
Mahmoodi, K., & Momeni, H. (2024). Comparison of various machine learning algorithms for estimation of uniaxial compressive strength of cement and volcanic ash stabilized clay. Iranian Journal of Geophysics, 18(2), 19–37. (In Persian).
Mahmoodi, K., Mahbubi Motlagh, N., & Mahboubi Ardakani, A.-R. (2024). An investigation into the effects of lime-stabilization on soil–geosynthetic interface behavior. Geomechanics and Engineering, 38(3), 231.
Miao, S., Shen, Z., Wang, X., Luo, F., Huang, X., & Wei, C. (2017). Stabilization of highly expansive black cotton soils by means of geopolymerization. Journal of Materials in Civil Engineering, 29(10), 04017170.
Mozumder, R. A., & Laskar, A. I. (2015). Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Computers and Geotechnics, 69, 291–300.
Mozumder, R. A., Laskar, A. I., & Hussain, M. (2017). Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines. Construction and Building Materials, 132, 412–424.
Pordel, M., & Aminzadeh Ghavifekr, A. (2024). Analysis of factors influencing concrete resistance in construction industry: Machine learning approach. Journal of Civil and Environmental Engineering.
Ranjbar, I., Toufigh, V., & Boroushaki, M. (2022). A combination of deep learning and genetic algorithm for predicting the compressive strength of high‐performance concrete. Structural Concrete, 23(4), 2405-2418
Raschka, S., Liu, Y. H., & Mirjalili, V. (2022). Machine learning with PyTorch and Scikit-Learn.
Sukprasert, S., Hoy, M., Horpibulsuk, S., Arulrajah, A., Rashid, A. S. A., & Nazir, R. (2021). Fly ash based geopolymer stabilisation of silty clay/blast furnace slag for subgrade applications. Road Materials and Pavement Design, 22(2), 357–371.
Theobald, O. (2017). Machine learning for absolute beginners: A plain English introduction (Vol. 157). Scatterplot Press, London, UK.
Xue, X., Yang, X., & Chen, X. (2014). Application of a support vector machine for prediction of slope stability. Science China Technological Sciences, 57(12), 2379–2386. https://doi.org/10.1007/s11431-014-5699-6
Zeini, H. A., Al-Jeznawi, D., Imran, H., Bernardo, L. F. A., Al-Khafaji, Z., & Ostrowski, K. A. (2023). Random forest algorithm for the strength prediction of geopolymer stabilized clayey soil. Sustainability, 15(2), 1408.Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2021). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12(1), 469-477. https://doi.org/https://doi.org/10.1016/j.gsf.2020.03.007.