Implementation of an optimized binary classification by GMDH‐type neural network algorithm for predicting the blast produced ground vibration

Abstract

Ground vibration is one of the most important undesired phenomena resulting from blasting operations imposing damages to facilities and buildings on the one hand, and creating environmental problems in open pit mining on the other. Therefore, the present study aims to provide an optimized classification binary model to identify the blasting patterns with an acceptable ground vibration intensity to reduce the damages resulting from this artificial phenomenon. This study uses a binary method to provide an optimized classification model for predicting and evaluating the blasting patterns with the minimum ground vibration. Group Method of Data Handling-Type Neural Network is used as one of the most practical optimization algorithms to solve complicated and uncertain problems in this modelling. In this study, by collecting the data of 52 different blasting patterns from Soungun copper mine, some of the most important geometric properties and the amount of ammonium nitrate fuel oil consumed in each blasting pattern are recorded. In addition, based on expertise and experience of experts, the degree of ground vibration produced by each blasting is qualitatively classified into four different ranges of very high, high, normal and low in the form of unacceptable (very high and High) and acceptable (normal and low) clusters. Based on the results obtained from the analyses, the developed model has a high flexibility and ability in the binary prediction of blasting patterns with an acceptable vibration magnitude.

Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm

“…Hence, in the second step, the binary classification models are constructed based on three of the most important control parameters of the algorithm, including selection pressure (SP), maximum number of layers (MNL) and maximum number of neurons in a layer (MNNL). The SP is considered equal to 0.6 based upon previous studies [85,97]. This parameter influences the sensitivity of the modeling error, which is dimensionless; while the maximum number of layers and maximum number of neurons in a layer are selected according to the experience of experts and trial and error.…”

“…In this study, 775 accident cases were accurately evaluated and recorded from the urban and rural areas of Cosenza in southern Italy, and based on the suggestion proposed in Looney's research study, 0.75 of dataset (581 cases) were selected randomly to train, and the rest (0.25 of dataset) were used to test the developed binary model [100]. As mentioned before, there are considered three control parameters for constructing models that the SP is considered equal to 0.6, based upon previous studies [85,97], and also, the values of MNL are considered 5, 10, 15, 20 and 30 and the values of MNNL include 5, 10, 20 and 30, hence, a total of 20 models were constructed for forecasting the number of vehicles. The obtained results of 20 models are shown in Table 3.…”