AUTHOR=Cheng Cheng , Liu Jie , Wang Chaohui , Song Liang , Chen Haoyu 

TITLE=Mechanical properties of Lop Nor salt rock fillers for subgrade and its forecast model construction

JOURNAL=Frontiers in Materials

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2023.1245955

DOI=10.3389/fmats.2023.1245955

ISSN=2296-8016

ABSTRACT=To promote the engineering application process of salt rock, a deep exploration was conducted on the evolution law of the mechanical properties of salt rock fillers under the influence of different forming parameters in the Lop Nor area. The accuracy of primary regression, square regression, and Support Vector Machine (SVM) regression algorithms in predicting the mechanical properties of salt rock fillers was compared, and the more accurate prediction model for California Bearing Ratio (CBR) and rebound modulus of salt rock fillers were recommended. The results showed that the optimal brine content of SC-40, SC-20, and SC-10 salt rock fillers was between 8.2% and 9.3%, with a dry density of approximately 1.69 to 1.76 g/cm 3 . The CBR of salt rock samples gradually decreases with the increase of brine content, and the rebound modulus was higher than 90.6 MPa. As the compaction degree increased, the CBR value increased significantly, and the rebound modulus increased by about 28.1 MPa. As the immersion time increased, the mechanical properties of salt rock gradually decreased. Among various regression models, the SVM prediction model had the highest accuracy index coefficient of determination (R 2 ), while mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) are all the smallest. Therefore, it was recommended to use the SVM prediction model to accurately estimate the mechanical properties of salt rock roadbed filler and provided the reference for the regulation of compaction parameters and the guarantee of bearing capacity characteristics of salt rock roadbed.