Accurate prediction of blast-induced ground vibration intensity using optimized machine learning models

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 CatBoost (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 (R²). The hybrid CB-BOA model outperformed other approaches, achieving the highest accuracy (R² = 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.