Numerical analysis of energy transmission through discontinuities and fillings in Kangir Dam

Abstract A considerable amount of energy is released in the form of shock wave from explosive charge detonation. Shock wave energy is responsible for the creation of crushing and fracture zone around the blast hole. The rest of the shock wave energy is transferred to rock mass as ground vibration. Ground vibration is conveyed to the adjacent structures by body and surface waves. Geological structures like faults, fractures, and fillings play important roles in the wave attenuation. Studying the mechanism of ground wave propagation from blasts gives a better understanding about the stress wave transmission and its effect on the near structures. In this research work, the stress wave transmissions from discontinuities and fillings were evaluated using a field measurement and a Universal Distinct Element Code (UDEC). A single-hole blast was conducted in the Kangir dam, and the resulting vibrations were measured in many points before and after the faults. Numerical simulation shows the effects of geo-mechanical properties of fillings on the reflection and refraction rate of the stress wave. There are more energy reflections in the rock boundaries and soil fillings, and more energy is absorbed by soil fillings compared with rock fillings. Furthermore, there is a close correlation between the ground vibration records for the Kangir dam and the numerical results. The maximum relative error between the actual records and the simulated ones was found to be 18.5%, which shows the UDEC ability for the prediction of blast vibrations

Several non-linear models in estimating air-overpressure resulting from mine blasting

This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting. For this purpose, Miduk copper mine, Iran was investigated and results of 77 blasting works were recorded to be utilized for AOp prediction. In the modeling procedure of this study, results of distance from the blast-face and maximum charge per delay were considered as predictors. After constructing the non-linear models, several performance prediction indices, i.e. root mean squared error (RMSE), variance account for (VAF), and coefficient of determination (R2) and total ranking method are examined to choose the best predictive models and evaluation of the obtained results. It is obtained that the ANFIS model is superior to other utilized techniques in terms of R2, RMSE, VAF and ranking herein. As an example, RMSE values of 5.628, 3.937, 3.619 and 2.329 were obtained for testing datasets of empirical, ANN, fuzzy and ANFIS models, respectively, which indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods

Airblast prediction through a hybrid genetic algorithm-ANN model

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 (R2) 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 R2, 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