Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

Abstract

Predicting and reducing blast-induced ground vibrations is a common concern among engineers and mining enterprises. Dealing with these vibrations is a challenging issue as they may result in the instability of the surrounding structures, highways, water pipes, railways, and residential areas. In this study, the effects of blasting in a quarry mine in Vietnam were examined. A total of 25 blasting events were investigated with the help of an unmanned aerial vehicle, micromate instruments, and blast patterns, and 83 observations were recorded. Subsequently, the fuzzy C-means clustering (FCM) algorithm was applied to classify the 83 observations based on the blast parameters. Finally, based on the classification of the blasts, quantile regression neural network (QRNN) models were developed. The combination of FCM and QRNN models resulted in a novel, hybrid model (FCM-QRNN) for predicting blast-induced ground vibration. The US Bureau of Mines (USBM), random forest (RF), QRNN (without clustering), and artificial neural network (ANN) models were also considered and compared with the FCM-QRNN model to obtain a comprehensive assessment of the proposed model. The results indicate that the proposed FCM-QRNN model has a higher accuracy than the other models: USBM, QRNN, RF, and ANN. The proposed model can be used to control the undesirable effects of blast-induced ground vibration. Although this study and the proposed FCM-QRNN model are original works with positive results, the performance of this model in other locations still needs to be considered as a case study for further scientific information.