期刊
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
卷 23, 期 10, 页码 3505-3521出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10298436.2021.1904237
关键词
California bearing ratio; geosynthetic reinforcement; subgrade soil; machine learning; intelligent predictive modelling
类别
资金
- Edith Cowan University (ECU), Australia and Higher Education Commision, Pakistan
This paper explores and evaluates the competency of several intelligent models in estimating the CBR of reinforced soil, with artificial neural network (ANN) being identified as the best model. The study assesses the predictive accuracy of the models using various evaluation methods and sensitivity analysis, providing valuable insights for future research.
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.
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