A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels

Abstract

Overbreak in tunnel construction creates additional costs, and it could put the safety conditions at potential risk. This paper is aimed to predict overbreak in order to control it before drilling and blasting operations through two intelligence systems, namely, an artificial neural network (ANN) and a hybrid genetic algorithm (GA)-ANN. To achieve this aim, a database comprising of 406 datasets were prepared in the Gardaneh Rokh tunnel, Iran. In these datasets, rock mass rating (RMR), spacing, burden, special drilling, number of delays, powder factor and advance length were considered as inputs while overbreak is set as output system. Many intelligence models were created to achieve higher levels of accuracy in accordance with several performance indices, i.e., root mean square error (RMSE), variance account for (VAF) and coefficient of determination (R2). After selection of the best models, GA-ANN model results (VAF = 90.134 and 88.030, R2 = 0.903 and 0.881 and RMSE = 0.058 and 0.074 for training and testing, respectively) were better compared to ANN model results (VAF = 70.319 and 68.731, R2 = 0.703 and 0.693 and RMSE = 0.103 and 0.108 for training and testing, respectively). As a result, the GA-ANN predictive approach can be used for overbreak prediction with high performance capacity. Moreover, results of sensitivity analysis showed that overbreak is mainly influenced by the RMR parameter compared to other inputs.