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.

摘要

近来，土工合成增强土 (GRS) 技术在建造安全和可持续的路面结构方面变得流行起来。路基土壤的强度通常根据其加州承载比 (CBR) 进行评估。然而，在过去，没有努力开发一种评价加筋路基土的CBR的方法。本文的主要目的是探索和评估几种智能模型的能力，如人工神经网络（ANN）、最小二乘回归、高斯过程回归、弹性网络正则化回归、惰性K-star、M-5 模型树、交替模型树和随机森林估计加筋土的 CBR。为此，所有模型都使用可靠的相关历史数据进行了校准和验证。使用完善的传统统计指标、外部模型评估技术、多标准评估方法和独立实验数据集评估了上述所有工具的预后准确性。由于人工神经网络的整体优异性能，将模型转换为可跟踪的函数关系来估计加筋土的 CBR。最后，进行敏感性分析，找出所用参数对 CBR 值的强度和关系。