利用机器学习和神经模糊推理系统技术预测路基土加州承载比:城市基础设施开发中的可持续方法

S. Gowda, Vaishakh Kunjar, Aakash Gupta, G. Kavitha, B. K. Shukla, P. Sihag
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引用次数: 0

摘要

在城市岩土基础设施开发领域,准确估算加州承载比(CBR)是路面设计的关键,而加州承载比是衡量无粘结颗粒材料和基层土壤强度的关键指标。获取 CBR 值的传统实验室方法耗时耗力,因此需要探索新的计算策略。本文阐述了机器学习技术--多元线性回归(MLR)、人工神经网络(ANN)和自适应神经模糊推理系统(ANFIS)--的开发和应用,以根据土壤类型、塑性指数(PI)和最大干密度(MDD)间接预测 CBR。我们的研究利用理论计算和大数据分析,对 2191 个土壤样本进行了参数分析,包括塑性指数、最大干密度、粒度分布和 CBR。ANFIS 在 CBR 预测方面表现出色,R2 值为 0.81,超过了 MLR 和 ANN。敏感性分析表明,PI 是影响 CBR 的最重要参数,其相对重要性为 46%。研究结果凸显了机器学习和神经模糊推理系统在不可再生城市资源可持续管理方面的巨大潜力,并为城市规划、建筑材料选择和基础设施发展提供了重要启示。这项研究在计算技术与岩土工程之间架起了一座桥梁,预示着智能城市资源管理的新时代即将到来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development
In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R2 value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management.
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