Predicting Triaxial Compressive Strength and Young’s Modulus of Frozen Sand Using Artificial Intelligence Methods
Publication: Journal of Cold Regions Engineering
Volume 33, Issue 3
Abstract
Mechanical properties of frozen soils (e.g., triaxial compressive strength, and Young’s modulus, ) are important in tunnel, shaft, or open pit excavation projects. Although numerous attempts have been made to develop indirect methods to estimate unfrozen soils’ and 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 and for frozen sandy soils, was compared. To the best of the authors’ knowledge, no study has predicted frozen soils’ and 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 and prediction. Results indicate that SVM can successfully be used in predicting the and of frozen soils.
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Acknowledgments
This research was supported by the University of Tabriz and the Iranian Ministry of Science. The researchers are grateful to the Tabriz Urban Railway organization for authorizing the sampling of unfrozen soils, which allowed the study to take place.
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Journal of Cold Regions Engineering
Volume 33 • Issue 3 • September 2019
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©2019 American Society of Civil Engineers.
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Received: Feb 28, 2018
Accepted: Dec 4, 2018
Published online: Jun 14, 2019
Published in print: Sep 1, 2019
Discussion open until: Nov 14, 2019
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