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Abstract

Each year, freeze–thaw cycles expose seasonally frozen soils, which deteriorates their mechanical properties. Machine learning technology is utilized to develop an anticipation pattern for soil static strength ( $S_{s}$ ) to precisely characterize the degradation of soil under various scenarios. Two strong and dependable methods were taken into consideration: random forests and least square support vector regression. Each of these algorithms has important hyperparameters that have a notable influence on the accuracy of the framework. In this exploration, the golden jackal optimization algorithm was used to enhance their prediction accuracy and generalization ability (called RFGJ and LSGJ). The results obtained indicate that the LSGJ and RFGJ methods possess a significant ability to accurately anticipate the $S_{s}$ seasonally frozen soils. During the training and testing phases, the coefficient of determination (R²) values for the LSGJ network were found to be 0.9905 and 0.9952. It was observed that LSGJ gets the fewest value on the objective function (OBJ), the lower the best, at 3.5198, which is over 30% less than RFGJ at 4.6391. Compared to existing approaches, the proposed models showed enhanced accuracy and reliability, providing a robust framework for evaluating the mechanical behavior of seasonally frozen soils.

Keywords

frozen soil static strength comparative analysis least square regression

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The Static Strength of Seasonally Frozen Soils: Application of Optimization-Based Regression Analysis

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