Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

• Published in: Buildings (Basel) Vol. 14; no. 3; p. 641
• Main Authors: Zhao, Huiwei, Bing, Hui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2024
• Subjects: Accuracy; Classification; Compressive strength; Desalination; Design engineering; Engineering; Experimental methods; Frozen ground; Hypotheses; Learning algorithms; Machine learning; machine learning model; macro–meso–micro three-level characteristic parameter; Mathematical models; Mechanical engineering; Mechanical properties; Moisture content; Optimization; Parameter sensitivity; Performance evaluation; Performance prediction; Physical properties; Porous materials; Radial basis function; Research methodology; Salinity; Salt; Scanning electron microscopy; Sensitivity analysis; Soil mechanics; Soil strength; Soil structure; Soils; Stability analysis; sulfuric salinized frozen soil; Ultrasonic tests; unconfined compressive strength; China
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings14030641

Unconfined compressive strength (UCS) is an important parameter of rock and soil mechanical behavior in foundation engineering design and construction. In this study, salinized frozen soil is selected as the research object, and soil GDS tests, ultrasonic tests, and scanning electron microscopy (SEM) tests are conducted. Based on the classification method of the model parameters, 2 macroscopic parameters, 38 mesoscopic parameters, and 19 microscopic parameters are selected. A machine learning model is used to predict the strength of soil considering the three-level characteristic parameters. Four accuracy evaluation indicators are used to evaluate six machine learning models. The results show that the radial basis function (RBF) has the best UCS predictive performance for both the training and testing stages. In terms of acceptable accuracy and stability loss, through the analysis of the gray correlation and rough set of the three-level parameters, the total amount and proportion of parameters are optimized so that there are 2, 16, and 16 macro, meso, and micro parameters in a sequence, respectively. In the simulation of the aforementioned six machine learning models with the optimized parameters, the RBF still performs optimally. In addition, after parameter optimization, the sensitivity proportion of the third-level parameters is more reasonable. The RBF model with optimized parameters proved to be a more effective method for predicting soil UCS. This study improves the prediction ability of the UCS by classifying and optimizing the model parameters and provides a useful reference for future research on salty soil strength parameters in seasonally frozen regions.