Abstract
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.
RAS ID
35984
Document Type
Journal Article
Date of Publication
2022
Funding Information
Edith Cowan University - Open Access Support Scheme 2021
Edith Cowan University
Higher Education Commission, Pakistan
School
School of Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
Taylor & Francis
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
Comments
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.
https://doi.org/10.1080/10298436.2021.1904237