首页|Researchers from Indian School of Mines Report New Studies and Findings in the Area of Machine Learning (A Generalized Failure Mode Model for Transversely Isotropic Rocks Using a Machine Learning Classification Approach)
Researchers from Indian School of Mines Report New Studies and Findings in the Area of Machine Learning (A Generalized Failure Mode Model for Transversely Isotropic Rocks Using a Machine Learning Classification Approach)
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NETL
NSTL
Springer Nature
Data detailed on Machine Learning have been presented. According to news reporting out of Dhanbad, India, by NewsRx editors, research stated, “Understanding and predicting the failure modes of transversely isotropic (TI) rocks is essential for designing stable structures and evaluating rock failure incidents, including ground falls, slope stability, and landslides. Compared to isotropic intact rock, TI intact rock exhibits distinct failure modes.” Our news journalists obtained a quote from the research from the Indian School of Mines, “Until now, the failure mode of TI rocks has often been related to layer orientation and confinement pressure, with little consideration given to micro-scale parameters. This study investigates how layer orientation and grain size influence the failure mode of layered sandstone under confined and unconfined conditions. A comprehensive laboratory experiments involved 105 layered sandstone samples, featuring variations in grain sizes (fine, medium, and coarse), application of five distinct confining pressures, and testing at seven different orientations. Through rigorous analyses of the post-failure layered sandstone using a random forest classification method, this study developed a predictive chart capable of determining TI rock failure mode based on layer orientation and confinement pressure. Intriguingly, the study also underscores that grain size exerts an insignificant impact on influencing these failure modes. The resultant predictive chart is expected to significantly enhance our comprehension and interpretation of rock failure in both laboratory and field applications, offering invaluable insights for engineering structures associated with TI rocks.”
DhanbadIndiaAsiaCyborgsEmerging TechnologiesMachine LearningIndian School of Mines
NETL
NSTL
Springer Nature
