Multi-response prediction and uncertainty quantification of blasting vibration using WOA-optimized XGBoost and conformal inference

Abstract

Existing studies on blast-induced vibration prediction predominantly focus on a single response indicator, leaving multi-parameter characterization and predictive uncertainty quantification insufficiently addressed. This study proposes a unified framework that integrates Kennard–Stone (KS) sample partitioning, Whale Optimization Algorithm (WOA)-tuned Extreme Gradient Boosting (XGBoost), and out-of-fold (OOF) calibrated adaptive conformal prediction. Separate WOA-XGBoost models are developed to predict peak particle velocity (PPV), dominant frequency (f), and vibration duration (T). To quantify prediction uncertainty, a Random Forest-based error-scale model is embedded within the conformal prediction framework, enabling input-dependent interval widths without requiring a separately held-out calibration subset. On an independent hold-out test set, the proposed framework achieves coefficients of determination (R 2 ) of 0.899, 0.880, and 0.967 for PPV, f, and T, respectively, outperforming benchmark models in RMSE and MAE. At a nominal 90% confidence level, the prediction interval coverage probabilities are 92.86% for PPV, 92.86% for f, and 89.29% for T, with normalized interval widths and coverage-width-based criteria revealing distinct, target-dependent uncertainty structures. External validation using an independent dataset from a Xinjiang iron mine yields R 2 values of 0.865, 0.915, and 0.921, confirming robust generalization across varying geological conditions. The proposed framework provides an engineering-oriented, interpretable, and uncertainty-aware decision-support strategy for blasting vibration risk assessment, demonstrating the practical applicability of optimized ensemble learning combined with conformal inference.

The authors' abstract, as published at the source. Ain Shams Engineering Journal, 2026 · DOI ↗