Application of Back-Propagation Neural Network in the Post-Blast Re-Entry Time Prediction

Abstract

Predicting the post-blast re-entry time precisely can improve productivity and reduce accidents significantly. The empirical formulas for the time prediction are practical to implement, but lack accuracy. In this study, a novel method based on the back-propagation neural network (BPNN) was proposed to tackle the drawbacks. A numerical model was constructed and 300 points of sample data were recorded, with consideration to fresh air volume, occupational exposure limit, toxic gas volume per kg of explosives and roadway length. The BPNN model with six neurons in a hidden layer was then developed and prediction performance was discussed in terms of four indicators, namely, the root mean square error (RMSE), the coefficient of determination (R2), the mean absolute error (MAE) and the sum of squares error (SSE). Furthermore, one representative empirical formula was introduced and calibrated for the comparison. The obtained results showed that the BPNN model had a more remarkable performance, with RMSE of 21.45 (R2: 0.99, MAE: 10.78 and SSE: 40934), compared to the empirical formula, with RMSE of 76.89 (R2: 0.90, MAE: 42.06 and SSE: 526147). Hence, the BPNN model is a superior method for predicting the post-blast re-entry time. For better practical application, it was then embedded into the software.