基于机器学习的透明土壤性质预测与性能研究

IF 2.1 3区 工程技术 Q2 ENGINEERING, CIVIL
Bo Wang, H. Hou, Z. Zhut, Wang Xiao
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引用次数: 0

摘要

透明土壤岩土工程建模的一个不可或缺的过程包括分析图像和土壤特性模拟。本研究提出了一个客观的框架来定量分析三个关键因素,即不同的集料比(DAP)、溶剂比(SR)和溶质溶液比(SSR)对透明土壤透明度和抗剪强度的影响机制。制备了125组考虑这三个因素的透明土样品,通过弹性网回归研究它们对透明度和抗剪强度的影响。对透明度和剪切强度进行了Spearman相关性分析。此外,通过比较XGBoost、GBDT、随机森林和SVR在超参数调整后预测透明度和剪切强度的性能,XGBoost被证明是MSE最低为0.0048和0.0306的最优机器学习模型,并被创新性地用于分析各种因素如何影响透明度和抗剪强度,从而增强了机器学习的可解释性。根据XGBoost的重要性得分,排序系统显示SSR是影响透明土抗剪强度和透明度的最重要因素,重要性得分分别为0.45和0.57。我们的研究有助于透明土壤的制备和性能研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-based prediction and performance study of transparent soil properties
An indispensable process of geotechnical modeling with transparent soils involves analyzing images and soil property simulations. This study proposes an objective framework for quantitative analysis of the influential mechanism of three key factors, namely, different aggregate proportions (DAP), solvent ratio (SR), and solute solution ratio (SSR) on transparent soils' transparency and shear strength. 125 groups of transparent soil samples considering these three factors were prepared to investigate their impact on transparency and shear strength through Elastic Net regression. Spearman correlation analysis was performed for transparency and shear strength. Furthermore, by comparing the performance of XGBoost, GBDT, Random Forest, and SVR after hyperparameter tuning in predicting transparency and shear strength, XGBoost proved to be the optimal machine learning model with the lowest MSE of 0.0048 and 0.0306 and was innovatively adopted to analyze how various factors affect the transparency and shear strength, thus enhancing the interpretability of machine learning. A ranking system, according to the importance scores of XGBoost, shows that SSR was the most important factor affecting both shear strength and transparency of transparent soils, with importance scores being 0.45 and 0.57, respectively. Our study may shed light on the preparation and performance study of transparent soils.
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来源期刊
Smart Structures and Systems
Smart Structures and Systems 工程技术-工程:机械
CiteScore
6.50
自引率
8.60%
发文量
0
审稿时长
9 months
期刊介绍: An International Journal of Mechatronics, Sensors, Monitoring, Control, Diagnosis, and Management airns at providing a major publication channel for researchers in the general area of smart structures and systems. Typical subjects considered by the journal include: Sensors/Actuators(Materials/devices/ informatics/networking) Structural Health Monitoring and Control Diagnosis/Prognosis Life Cycle Engineering(planning/design/ maintenance/renewal) and related areas.
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