Author Identifier

Muhammad Nouman Amjad Raja

ORCID : 0000-0001-7463-0601

Sanjay Kumar Shukla

ORCID : 0000-0002-4685-5560

Muhammad Umer Arif Khan

ORCID : 0000-0002-3077-2875

Document Type

Journal Article

Publication Title

International Journal of Pavement Engineering


Taylor & Francis


School of Engineering




Edith Cowan University - Open Access Support Scheme 2021

Edith Cowan University

Higher Education Commission, Pakistan


Raja, M. N. A., Shukla, S. K., & Khan, M. U. A. (2022). An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil. International Journal of Pavement Engineering, 23(10), 3505-3521.


In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model trees, alternating model trees and random forest in estimating the CBR of reinforced soil. For this, all the models were calibrated and validated using the reliable pertinent historical data. The prognostic veracity of all the tools mentioned supra were assessed using the well-established traditional statistical indices, external model evaluation technique, multi-criteria assessment approach and independent experimental dataset. Due to the overall excellent performance of ANN, the model was converted into a trackable functional relationship to estimate the CBR of reinforced soil. Finally, the sensitivity analysis was performed to find the strength and relationship of the used parameters on the CBR value.



Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.