Abstract
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 R2 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.
Highlights
Construction projects such as water and hydropower, transportation, mining, industrial and civil construction engineering all involve rock engineering
The Support vector machine (SVM) model prediction performance is significantly better than the traditional fitting function
This study only focuses on the model relationship between the point load strength of using APLS and diametral point load strength (DPLS) to predict Uniaxial compression strength (UCS) is better than using only a single type of point and the uniaxial compressive strength
Summary
Construction projects such as water and hydropower, transportation, mining, industrial and civil construction engineering all involve rock engineering. In order to accurately and promptly assess the stability of the mountains involved in the construction project and ensure the safety of the structure, rock classification methods are often used in the rock engineering project and design. To evaluate the stability of engineering rock mass, the rock classification divides engineering rock mass into several levels with different basic quality and strength. In this way, it provides an important basis and safety protection selection for the technical planning and various construction engineering [1]. Quantitative indicators of strength in rock classification are generally expressed in terms of uniaxial compression strength of rocks.
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