Predicting blast-induced pull using regression tree

The tunnel advance (pull) per blast considerably influences the time and expenditure incurred to construct tunnels using conventional drill and blast methods. Blasts with high pull efficiency can considerably lessen the construction time by reducing the number of drilling, blasting and mucking cycle...

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Bibliographic Details
Published in:Arabian journal of geosciences 2022, Vol.15 (2), Article 173
Main Authors: Rana, Aditya, Bhagat, Narayan Kumar, Singh, Atul, Singh, Pradeep Kumar
Format: Article
Language:English
Subjects:
Blast holes
Blasting
Data points
Datasets
Design parameters
Drilling
Earth and Environmental Science
Earth science
Earth Sciences
Empirical analysis
Hydroelectric power
Original Paper
Regression analysis
Regression models
Root-mean-square errors
Soft computing
Statistical analysis
Training
Tunnel construction
Tunnels
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Summary:The tunnel advance (pull) per blast considerably influences the time and expenditure incurred to construct tunnels using conventional drill and blast methods. Blasts with high pull efficiency can considerably lessen the construction time by reducing the number of drilling, blasting and mucking cycles. Researchers have developed some empirical and theoretical models for determining pull. However, these models are based upon hit and trial techniques. The previous works on the similar subject suggest that the pull efficiency cannot be correlated to a variable. Nevertheless, the literature suggests that the soft computing techniques can be used for solving such non-linear problems. The present study aims to develop a classification and regression tree (CART) model to predict the pull using various blast design parameters, such as cross-sectional area, number of blast holes, hole diameter, hole depth, charge per hole, maximum charge per delay, total charge and charge factor. A dataset comprising of 100 data points was compiled from tunnelling sites of Karcham Wangtoo Hydroelectric Project for model formulation. The dataset was divided in a ratio of 80:20 for training and testing, respectively. The coefficient of determination ( R 2 ) and root mean square error (RMSE) of the CART model and multivariate regression analysis were compared for the validation. The suitability of the CART model was further verified by conducting statistical hypothesis tests.
ISSN:1866-7511
1866-7538
DOI:10.1007/s12517-022-09452-1