Enhancing Ground Vibration Prediction in Mine Blasting: A Committee Machine Intelligent System Optimized with Metaheuristic Algorithms - Hasanipanah, Mahdi; Amnieh, Hassan Bakhshandeh (Natural resources research (New York, N.Y.), 2025)
Ground vibrations resulting from mine blasting pose significant risks to nearby structures and the environment. This paper introduces an advanced framework employing Committee Machine Intelligent Systems (CMIS) optimized by cutting-edge metaheuristic algorithms to predict ground vibrations. Initially, cascaded feedforward neural networks (CFNN) were developed using Levenberg-Marquardt algorithm and Bayesian regularization (BR). These neural networks were integrated into a CMIS to enhance prediction accuracy. Optimization of the CMIS was carried out using four metaheuristic algorithms, including the grey wolf optimizer (GWO), Harris hawks optimization (HHO), artificial bee colony, and genetic algorithm. Comprehensive evaluation metrics such as quantile-quantile plots, Taylor diagrams, and error analysis were employed to assess model performance. Results indicated that the CMIS model optimized by HHO provided the most accurate ground vibration predictions, surpassing existing models in the literature. Sensitivity analysis identified weight charge per delay as the most critical factor influencing ground vibration. Moreover, the uncertainty analysis indicated that the CFNN-BR model had the narrowest uncertainty band, followed by the CMIS-GWO model. The study underscores the potential of integrating CMIS with metaheuristic algorithms for precise and reliable vibration prediction in mining operations.