首页|Changchun Institute of Technology Researcher Highlights Research in Machine Lear ning (Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Indu ced by Microwave Radiation and Their Associated Strength Reduction Patterns: ... )
Changchun Institute of Technology Researcher Highlights Research in Machine Lear ning (Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Indu ced by Microwave Radiation and Their Associated Strength Reduction Patterns: ... )
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on artificial intelligence are discussed in a new report. According to news reporting from Changchun, People's Republic of China, by NewsRx journalists, research stated, "Microwave-assisted m echanical rock breaking represents an innovative technology in the realm of mini ng excavation. The intricate and variable characteristics of geological formatio ns necessitate a comprehensive understanding of the interplay between microwave- induced rock damage and the subsequent deterioration in rock strength." Funders for this research include Natural Science Foundation of Jilin Province. Our news journalists obtained a quote from the research from Changchun Institute of Technology: "This study conducted microwave irradiation damage assessments o n 78 distinct rock samples, encompassing granite, sandstone, and marble. A total of ten critical parameters were identified: Microwave Irradiation Time (MIT), M icrowave Irradiation Power (MIP), Longitudinal Wave Velocity prior to Microwave Treatment (LWVB), Longitudinal Wave Velocity post-Microwave Treatment (LWVA), Pe rcentage Decrease in Longitudinal Wave Velocity (LWVP), Porosity before Microwav e Treatment (PB), Porosity after Microwave Treatment (PA), Percentage Increase i n Porosity (PP), and Uniaxial Compressive Strength following Microwave Treatment (UCSA). Utilizing the Pied Kingfisher Optimizer (PKO) alongside Extreme Gradien t Boosting (XGBoost), we developed a PKO-XGBoost machine learning model to eluci date the relationship between UCSA and the nine additional parameters. This mode l was benchmarked against other prevalent machine learning frameworks, with Shap ley additive explanatory methods employed to assess each parameter's influence o n UCSA. The findings reveal that the PKO-XGBoost model provides superior accurac y in delineating relationships among rock physical properties, microwave irradia tion variables, microscopic attributes of rocks, and UCSA. Notably, PA emerged a s having the most significant effect on UCSA, indicating that microwave-induced microscopic damage is a primary contributor to reductions in rock strength."
Changchun Institute of TechnologyChang chunPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging Technologi esMachine LearningOptimization Algorithms
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