146 publications from this institution
Uniaxial compressive strength (UCS) of rock is an essential parameter in geotechnical engineering. Point load strength (PLS), P-wave velocity, and Schmidt hammer rebound number (SH) are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS. This study collected 1080 datasets consisting of SH, P-wave velocity, PLS, and UCS. All datasets were integrated into three categories (sedimentary, igneous, and metamorphic rocks) according to lithology. Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types. Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types. UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks. Nonetheless, the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks. The developed predictive models can be applied to quickly predict UCS at engineering sites, which benefits the rapid and intelligent classification of rock masses. Moreover, the importance of SH, P-wave velocity, and PLS were analyzed for the estimation of UCS. SH was a reliable indicator for UCS evaluation across various rock types. P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks, but it was not reliable for assessing the UCS of metamorphic rocks.
The validity of calculating the dynamic tensile strength of rock materials based on dynamic Brazilian tests is problematic. In order to gain a deeper understanding of the effects of three typical loading methods on the damage mechanism of rock specimens in the dynamic Brazilian tests, five different rocks were selected for the study. In the constant incident energy dynamic Brazilian test, the loading modes had a significant effect on the loading rate and dynamic tensile strength of the specimen, with the highest loading rate and tensile strength of the specimens under mode-III loading, followed by mode-I loading and mode-II loading. A high-speed camera and the digital image correlation (DIC) technique were used to successfully capture the rupture process of the Brazilian disc during impact loading. The evolution of the displacement and strain fields of the specimen was obtained by DIC technique, and four typical failure patterns and two rupture characteristics in the dynamic Brazilian test were summarized. The loading mode determined the crack initiation position of the specimen in the dynamic Brazilian test. The results showed that the mode-III loading is the most consistent with the Brazilian test theory, while the mode-II loading violates the test principle.
Abstract Rock fragmentation is an important indicator for assessing the quality of blasting operations. However, accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties. For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. A total of 102 data sets with seven input parameters (spacing‐to‐burden ratio, hole depth‐to‐burden ratio, burden‐to‐hole diameter ratio, stemming length‐to‐burden ratio, powder factor, in situ block size, and elastic modulus) and one output parameter (rock fragment mean size, X 50 ) were adopted to train and validate the predictive models. The root mean square error ( RMSE ), the mean absolute error ( MAE ), and the coefficient of determination () were used as the evaluation metrics. The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models. The hybrid model consisting of gradient boosting and BOA (GBoost‐BOA) achieved the best prediction results compared with the other hybrid models, with the highest R 2 value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02, respectively. Furthermore, sensitivity analysis was carried out to study the effects of input variables on rock fragmentation. In situ block size ( X B ), elastic modulus ( E ), and stemming length‐to‐burden ratio ( T/B ) were set as the main influencing factors. The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.
In view of underground mining,an ore-rock stability evaluation was taken combining the fuzzy-grey-element,on the parameters that are capable to reflect ore-rock stability.Based on fuzzy-matter-element theory,grey system theory and correlation function calculation,a fuzzy-grey-element model is set up for evaluating ore-rock stability.And a fuzzy-grey-element of correlative coefficient is constructed by correlative transformation and processing the optimal subordinate degree.The combinational weight coefficient of evaluation index was decided by combining summation normalization method with entropy method.A fuzzy-grey-element evaluation was also taken on the grade of ore-rock stability and the sequence from good to bad at Shaba Fields of Kaiyang phosphorite Mine by calculating correlation and adopting maximum correlation theory.Results obtained are all satisfactory.
