Discussion of ‘Strength Evaluation of Expansive Soil Stabilized with Lead–Zinc Mine Tailings and Cement: An Artificial Intelligence Approach’ [DOI: 10.1007/s42947-024–00450-y]

Author:

Soltani AminORCID,Azimi MahdiehORCID

Funder

Federation University Australia

Publisher

Springer Science and Business Media LLC

Reference30 articles.

1. Odumade, A. O., Ikeagwuani, C. C., & Alexander, T. C. (2024). Strength evaluation of expansive soil stabilized with lead–zinc mine tailings and cement: An artificial intelligence approach. International Journal of Pavement Research and Technology. https://doi.org/10.1007/s42947-024-00450-y. (In Press).

2. Ghorbani, B., Yaghoubi, E., & Arulrajah, A. (2022). Thermal and mechanical characteristics of recycled concrete aggregates mixed with plastic wastes: Experimental investigation and mathematical modeling. Acta Geotechnica, 17(7), 3017–3032. https://doi.org/10.1007/s11440-021-01370-y

3. Baghbani, A., Nguyen, M. D., Kafle, B., Baghbani, H., & Shirani Faradonbeh, R. (2023). AI grey box model for alum sludge as a soil stabilizer: An accurate predictive tool. International Journal of Geotechnical Engineering, 17(5), 480–494. https://doi.org/10.1080/19386362.2023.2258749

4. Onyelowe, K. C., Ebid, A. M., Aneke, F. I., & Nwobia, L. I. (2023). Different AI predictive models for pavement subgrade stiffness and resilient deformation of geopolymer cement-treated lateritic soil with ordinary cement addition. International Journal of Pavement Research and Technology, 16(5), 1113–1134. https://doi.org/10.1007/s42947-022-00185-8

5. Ghorbani, B., Yaghoubi, E., Wasantha, P. L. P., van Staden, R., Guerrieri, M., & Fragomeni, S. (2024). Machine learning-based prediction of resilient modulus for blends of tire-derived aggregates and demolition wastes. Road Materials and Pavement Design, 25(4), 694–715. https://doi.org/10.1080/14680629.2023.2222176

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