Abstract

Boulder blasting is a different process from conventional bench blasting. Fly-rock produced in boulder blasting is a major safety concern due to the presence of 360° free-face which may result into excessive throw of the fragments radially up to 900 m distance causing accidents. Many researchers have attempted to predict the fly-rock using empirical and soft computing tools in bench blasting. But, there is paucity of literature to predict the extent of fly-rock in boulder blasting. Machine learning techniques are frequently used in bench blasting to predict ground vibrations, air overpressure, fly-rocks, but it has been rarely used in boulder blasting. In this study, an attempt has been made to use Classification and Regression Trees (CART) technique to predict the fly-rock distance in boulder blasting. Multiple linear regression (MLR) technique has been used to compare the results obtained by the CART technique. Sixty-one boulder blasting events were monitored while excavating the accident-prone slope areas of Konkan Railways. The performance of the developed models using both the techniques has been evaluated using the coefficients of determination (R²) and root-mean-square error (RSME) values. The results indicate that CART model (R² = 0.9555 and RMSE = 1.141) provides better output than MLR model. This paper suggests the use of CART technique in boulder blasting, which will be useful in execution at sensitive locations to predict and control the fly-rock distance.

摘要

巨石爆破与传统的台式爆破工艺不同。巨石爆破中产生的飞石是一个主要的安全问题，因为存在 360° 自由面，这可能导致碎片径向抛掷高达 900 m 距离，从而导致事故。许多研究人员试图在台架爆破中使用经验和软计算工具来预测飞石。但是，很少有文献可以预测巨石爆破中飞石的范围。机器学习技术经常用于台架爆破以预测地面振动、空气超压、飞石，但它很少用于巨石爆破。在这项研究中，尝试使用分类和回归树 (CART) 技术来预测巨石爆破中的飞石距离。多元线性回归 (MLR) 技术已用于比较 CART 技术获得的结果。在挖掘康坎铁路事故多发的斜坡区域时，监测了 61 次巨石爆破事件。使用这两种技术开发的模型的性能已经使用决定系数进行了评估（R ² ) 和均方根误差 (RSME) 值。结果表明，CART 模型（R ² = 0.9555 和 RMSE = 1.141）比 MLR 模型提供更好的输出。本文建议在巨石爆破中使用 CART 技术，这将有助于在敏感位置执行以预测和控制飞石距离。