A Principal Component-Enhanced Neural Network Framework for Forecasting Blast-Induced Ground Vibrations
Journal Title: Journal of Civil and Hydraulic Engineering - Year 2024, Vol 2, Issue 4
Abstract
Blast-induced ground vibration, a by-product of rock fragmentation, presents significant challenges, particularly in areas adjacent to residential structures, where excessive vibration can cause structural damage and propagate cracks. This study proposes a novel framework integrating Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) to predict Peak Particle Velocity (PPV), a critical metric for assessing ground vibration intensity. Field data were gathered from Singareni coal mines, capturing a range of blasting parameters, including burden, spacing, explosive quantity, and maximum charge per delay. PCA was employed to identify and retain the most influential variables, reducing dimensionality while preserving essential information. The optimised subset of features was subsequently used to train the ANN model. The model’s performance was evaluated using regression analysis, yielding a high coefficient of determination (R² = 0.92), indicating its robustness and accuracy in predicting PPV. A comparative analysis with conventional empirical equations demonstrated the superiority of the ANN model, which consistently provided more precise estimates of vibration intensity. The integration of PCA not only improved model performance but also enhanced computational efficiency by eliminating redundant parameters. This research underscores the potential of combining advanced statistical techniques with machine learning models to improve the predictability of blast-induced ground vibrations. The proposed framework offers a practical tool for mine operators to mitigate the environmental impact of blasting activities, particularly in sensitive areas.
Authors and Affiliations
T. Pradeep, N. Sri Chandrahas, Yewuhalashet Fissha
Keywords
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- EP ID EP752502
- DOI https://doi.org/10.56578/jche020402
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