Journal Article

Airblast prediction through a hybrid genetic algorithm-ANN model

Mahdiyar, Amir, Jahed Armaghani, Danial, Bakhshandeh Amnieh, Hassan, Tahir, Mahmood M. D., Hasanipanah, Mahdi, Abd Majid, Muhd Zaimi

2018

Overview

Air overpressure is one of the most undesirable destructive effects induced by blasting operation. Hence, a precise prediction of AOp has vital importance to minimize or reduce the environmental effects. This paper presents the development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm (GA) for prediction of AOp. For this purpose, a database was compiled from 97 blasting events in a granite quarry in Penang, Malaysia. The values of maximum charge per delay and the distance from the blast-face were set as model inputs to predict AOp. To verify the quality and reliability of the ANN and GA-ANN models, several statistical functions, i.e., root means square error (RMSE), coefficient of determination ( R 2 ) and variance account for (VAF) were calculated. Based on the obtained results, the GA-ANN model is found to be better than ANN model in estimating AOp induced by blasting. Considering only testing datasets, values of 0.965, 0.857, 0.77 and 0.82 for R 2 , 96.380, 84.257, 70.07 and 78.06 for VAF, and 0.049, 0.117, 8.62 and 6.54 for RMSE were obtained for GA-ANN, ANN, USBM and MLR models, respectively, which prove superiority of the GA-ANN in AOp prediction. It can be concluded that GA-ANN model can perform better compared to other implemented models in predicting AOp.

• Publisher: Springer London,Springer Nature B.V
• Subject: Air overpressure / Artificial Intelligence / Artificial neural networks / Blasting / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Data Mining and Knowledge Discovery / Environmental effects / Genetic algorithms / Image Processing and Computer Vision / Mathematical analysis / Neural networks / Original Article / Predictions / Probability and Statistics in Computer Science