Tunnel Blast Design using Artificial Neural Network - a Case Study

摘要

Artificial intelligent research has produced several tools for commercial applications. Some of the techniques that are widely used today include neural network, fuzzy logic and expert systems. Artificial neural network (ANN) is an excellent predictive and data analysis tool. In the mining industry, ANN techniques are being used commercially for real-time process-control applications. Blast design in tunnel construction, is still accomplished on trial and error basis which is not only time consuming but also leads to sub-optimum results in many situations. In order to design blasts based oilfield data and standardise tunnel blasting pattern, development of an automated design program is necessary. Thus, in this study an attempt has been made to develop a new method using Artificial Intelligence (AI), such as Artificial Neural Network (ANN), based on the data generated carefully from a metal mine. To serve as a general-purpose design model more data needs to be included. This paper presents the application of standard back propagation algorithm for tunnel blast design using parallel hole cut. The developed ANN model, after training with actual field data, is used to design different tunnel blast design parameters. Performance of the model has been evaluated by comparing the values obtained using the ANN model and actual values observed in /be field. Results indicate that the average error in prediction of blast design and performance parameters is less than five percent''. Thus, the developed neural network model can be applied for intelligent tunnel blast design.