Journal
MINERALS
Volume 11, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/min11121387
Keywords
point load strength; uniaxial compression strength; correlation; support vector machine
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This study established a prediction model for Uniaxial Compression Strength (UCS) using the Point Load Strength (PLS) with high accuracy, significantly better than traditional fitting methods. The model showed great significance in predicting rock stability in actual construction environments.
Uniaxial compression strength (UCS) is a fundamental parameter to carry out geotechnical engineering design and construction. It is simple and efficient to predict UCS using point load strength (PLS) at engineering sites. However, the high dispersion of rock strength limits the accuracy of traditional fitting prediction methods. In order to improve the UCS prediction accuracy, 30 sets of regular cylindrical specimen tests between PLS and UCS are conducted on limestone mines. The correlation relationship between PLS and UCS is found by using four basic fitting functions. Then, a prediction model is established by using SVM algorithm. Multiple training test data are used to achieve high-precision prediction of UCS and the results show it is less different from the actual values. Especially, the R-2 coefficient reached 0.98. The SVM model prediction performance is significantly better than the traditional fitting function. The constructed SVM model in this study can accurately predict the UCS using the PLS obtained in the field, which has a great significance to the rock stability judgment in the actual construction environment.
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