The specific charge (q, kg/m3) is one of the decisive technical parameters to the efficiency of tunnel construction using the drilling and blasting method. To accurately determine and calculate the specific charge (q, kg/m3), thereby improving the efficiency of tunnel construction, currently, there are many methods to determine the specific charge (q, kg/m3), such as: Pokrovsky's method, experimental method, and method of using numerical simulation software. In this paper, the authors used artificial neural network (ANN) and Random forest (RF) techniques to build artificial intelligence models capable of identifying and predicting the specific charge (q, kg/m3) with high accuracy. By using data compiled during the construction of the Deo Ca tunnel, Phu Yen, Vietnam, artificial intelligence models with ANN and RF techniques were built. Based on the prediction results of artificial intelligence models with specific data compiled from the actual construction process of Deo Ca tunnel, the accuracy of the results of the artificial intelligence models was confirmed.
Bui et al. (Sun,) studied this question.