岩土工程
土壤水分
自适应神经模糊推理系统
抗压强度
钻孔
支持向量机
人工神经网络
模数
地质学
机器学习
土壤科学
模糊逻辑
材料科学
人工智能
模糊控制系统
计算机科学
复合材料
作者
Mahzad Esmaeili‐Falak,Hooshang Katebi,Meysam Vadiati,Jan Adamowski
标识
DOI:10.1061/(asce)cr.1943-5495.0000188
摘要
Mechanical properties of frozen soils (e.g., triaxial compressive strength, σtc and Young’s modulus, E) are important in tunnel, shaft, or open pit excavation projects. Although numerous attempts have been made to develop indirect methods to estimate unfrozen soils’ σtc and E values, this has not been done with frozen soils given the difficulty of preparing and conducting relevant laboratory tests. In this study, the accuracy of artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and support vector machine (SVM) models, developed to predict σtc and E for frozen sandy soils, was compared. To the best of the authors’ knowledge, no study has predicted frozen soils’ σtc and E using these methods. Eighty-two poorly graded sandy soil samples from an urban subway borehole in Tabriz, Iran, were used to develop these models. It was found that temperature, confining pressure, strain rate, and yielding strain improved the accuracy of σtc and E prediction. Results indicate that SVM can successfully be used in predicting the σtc and E of frozen soils.
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