Machine learning prediction model for clay electrical conductivity and its application in electroosmosis consolidation

IF 5.6 1区 工程技术 Q1 ENGINEERING, GEOLOGICAL Acta Geotechnica Pub Date : 2024-09-22 DOI:10.1007/s11440-024-02411-y
Xunli Zhang, Lingwei Zheng, Xudong Zheng, Hengyu Wang, Shangqi Ge, Xinyu Xie
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Abstract

The electrical conductivity of soil is closely associated with various physical properties of the soil, and accurately establishing the interrelationship between them has long been a critical challenge limiting its widespread application. Traditional approaches in geotechnical engineering have relied on specific conduction mechanisms and simplifying assumptions to construct theoretical models for electrical conductivity. This paper adopts a different approach by using machine learning methods to predict the electrical conductivity of clay materials. A reliable dataset was generated using the quartet structure generation set to create random clay microstructures and calculate their electrical conductivity. Based on this dataset, machine learning methods such as least squares support vector machine and backpropagation neural networks outperform theoretical models in terms of prediction accuracy and resistance to interference, with a coefficient of determination (R2) exceeding 0.995 when unaffected by disturbances. The computation of Shapley values for input features aids in explicating the machine learning model. The results reveal that saturation is a key feature in predicting electrical conductivity, while porosity and constrained diameter are relatively less important. Finally, an already trained model is applied to the one-dimensional electroosmosis-surcharge preloading consolidation theory. The results of the calculations demonstrate that neglecting changes in soil electrical conductivity during electroosmosis can lead to an overestimation of the absolute values of anode excess pore water pressure and soil settlement.

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粘土电导率的机器学习预测模型及其在电渗固结中的应用
土壤的导电性与土壤的各种物理特性密切相关,长期以来,准确确定它们之间的相互关系一直是限制其广泛应用的关键挑战。岩土工程中的传统方法依赖于特定的传导机制和简化假设来构建导电率理论模型。本文采用不同的方法,利用机器学习方法预测粘土材料的导电性。利用四元结构生成集生成可靠的数据集,以创建随机粘土微结构并计算其导电率。基于该数据集,最小二乘支持向量机和反向传播神经网络等机器学习方法在预测准确性和抗干扰性方面优于理论模型,在不受干扰的情况下,决定系数(R2)超过 0.995。输入特征 Shapley 值的计算有助于解释机器学习模型。结果显示,饱和度是预测电导率的关键特征,而孔隙度和约束直径的重要性相对较低。最后,已训练好的模型被应用于一维电渗-充填预加载固结理论。计算结果证明,忽略电渗过程中土壤导电率的变化会导致高估阳极过剩孔隙水压力和土壤沉降的绝对值。
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来源期刊
Acta Geotechnica
Acta Geotechnica ENGINEERING, GEOLOGICAL-
CiteScore
9.90
自引率
17.50%
发文量
297
审稿时长
4 months
期刊介绍: Acta Geotechnica is an international journal devoted to the publication and dissemination of basic and applied research in geoengineering – an interdisciplinary field dealing with geomaterials such as soils and rocks. Coverage emphasizes the interplay between geomechanical models and their engineering applications. The journal presents original research papers on fundamental concepts in geomechanics and their novel applications in geoengineering based on experimental, analytical and/or numerical approaches. The main purpose of the journal is to foster understanding of the fundamental mechanisms behind the phenomena and processes in geomaterials, from kilometer-scale problems as they occur in geoscience, and down to the nano-scale, with their potential impact on geoengineering. The journal strives to report and archive progress in the field in a timely manner, presenting research papers, review articles, short notes and letters to the editors.
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