Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates ...Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance.This study presents an advanced predictive framework integrating Cat Boost(CB)with nature-inspired optimization algorithms,including the Bat Algorithm(BAT),Sparrow Search Algorithm(SSA),Butterfly Optimization Algorithm(BOA),and Grasshopper Optimization Algorithm(GOA).A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement(IoA),Nash-Sutcliffe Efficiency(NSE),and the coefficient of determination(R^(2)).The hybrid CB-BOA model outperformed other approaches,achieving the highest accuracy(R^(2)=0.989)and the lowest prediction errors.SHAP analysis identified Distance(Di)as the most influential variable affecting PPV,while uncertainty analysis confirmed CB-BOA as the most reliable model,featuring the narrowest prediction interval.These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions,contributing to improved blast design strategies,enhanced structural safety,and reduced environmental impacts in mining and geotechnical engineering.展开更多
Recycling useful materials such as Ag, Al, Sn, Cu and Si from waste silicon solar cell chips is a sustainable project to slow down the ever-growing amount of waste crystalline-silicon photovoltaic panels. However, the...Recycling useful materials such as Ag, Al, Sn, Cu and Si from waste silicon solar cell chips is a sustainable project to slow down the ever-growing amount of waste crystalline-silicon photovoltaic panels. However, the recovery cost of the above-mentioned materials from silicon chips via acid-alkaline treatments outweights the gain economically.Herein, we propose a new proof-of-concept to fabricate Si-based anodes with waste silicon chips as raw materials.Nanoparticles from waste silicon chips were prepared with the high-energy ball milling followed by introducing carbon nanotubes and N-doped carbon into the nanoparticles, which amplifies the electrochemical properties. It is explored that Al and Ag elements influenced electrochemical performance respectively. The results showed that the Al metal in the composite possesses an adverse impact on the electrochemical performance. After removing Al, the composite was confirmed to possess a pronounced durable cycling property due to the presence of Ag, resulting in significantly more superior property than the composite having both Al and Ag removed.展开更多
基金the Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the project number"NBUFFMRA-2025-2461-09"。
文摘Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance.This study presents an advanced predictive framework integrating Cat Boost(CB)with nature-inspired optimization algorithms,including the Bat Algorithm(BAT),Sparrow Search Algorithm(SSA),Butterfly Optimization Algorithm(BOA),and Grasshopper Optimization Algorithm(GOA).A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement(IoA),Nash-Sutcliffe Efficiency(NSE),and the coefficient of determination(R^(2)).The hybrid CB-BOA model outperformed other approaches,achieving the highest accuracy(R^(2)=0.989)and the lowest prediction errors.SHAP analysis identified Distance(Di)as the most influential variable affecting PPV,while uncertainty analysis confirmed CB-BOA as the most reliable model,featuring the narrowest prediction interval.These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions,contributing to improved blast design strategies,enhanced structural safety,and reduced environmental impacts in mining and geotechnical engineering.
基金Project(51774343) supported by the National Natural Science Foundation of China。
文摘Recycling useful materials such as Ag, Al, Sn, Cu and Si from waste silicon solar cell chips is a sustainable project to slow down the ever-growing amount of waste crystalline-silicon photovoltaic panels. However, the recovery cost of the above-mentioned materials from silicon chips via acid-alkaline treatments outweights the gain economically.Herein, we propose a new proof-of-concept to fabricate Si-based anodes with waste silicon chips as raw materials.Nanoparticles from waste silicon chips were prepared with the high-energy ball milling followed by introducing carbon nanotubes and N-doped carbon into the nanoparticles, which amplifies the electrochemical properties. It is explored that Al and Ag elements influenced electrochemical performance respectively. The results showed that the Al metal in the composite possesses an adverse impact on the electrochemical performance. After removing Al, the composite was confirmed to possess a pronounced durable cycling property due to the presence of Ag, resulting in significantly more superior property than the composite having both Al and Ag removed.
