Prediction of overbreak phenomenon in tunnel blasting using ORF index

Abstract

In the drill and blast method, one of the most critical issues is overbreak. Overbreak leads to decreased work efficiency and increased operational costs, and it is recognized as a problem that needs to be addressed. Although several factors contributing to overbreak have been proposed, the specific parameters with the most significant impact are still unclear. However, it is evident that the geological conditions of the rock mass have a significant influence. In this study, the Overbreak Resistance Factor (ORF) was adopted to predict the occurrence of overbreak and to create an indicator for it. As a result, we were able to predict the occurrence and grasp the trends of overbreak. Geological data were collected from a mountain tunnel in Japan, and overbreak data were gathered from the 3D CG model of the tunnel face, which was constructed using Structure from Motion (SfM). Using these datasets, an overbreak prediction model using an Artificial Neural Network (ANN) was developed, and sensitivity analysis was performed to create an overbreak chart based on the influence of different parameters