## 1 Introduction  
In blasting, only 25% to 30% of the explosive energy is spent in rock mass fragmentation, and more than 70% is wasted and causes side effects such as back break, fly rock, and blast-induced ground vibration [1; 2; 3]. Among these blast-induced side effects, blast-induced ground vibration is considered as the most common, important and dangerous side effects for nearby structures, human life and the environment [4; 5; 6]. Therefore, establishing the prediction methods to predict and analyze the blast-induced ground vibration and then to optimize the blasting design and building structure by using that model is of great significance to decrease the damage caused by blast-induced ground vibration. Blast-induced ground vibration can be described by using peak particle velocity (PPV) and frequency. According to previous literature [7], the peak particle velocity is normally considered to be more important than frequency and always selected to describe the blast-induced ground vibration. After reviewing the previous literature, some empirical models [8; 9; 10; 11] were proposed and most of them predict PPV by using the distance between blast blocks to monitor location and max charge per delay. Although some studies show these empirical models can predict PPV in some mines with high precision, there are also some research works [5; 12; 13] that show these models are not sufficient for all field sites. In recent years, artificial intelligence (AI) technology has been widely developed and applied in many areas for solving special problems such as the prediction of blast-induced rock movement [14], flyrock distance [15; 16], overbreak [17; 18], air overpressure [19; 20], rockburst [21; 22; 23], mechanical properties of material [24; 25; 26; 27; 28; 29], tunnel convergence [30] and tunnel boring machine penetration rate [31]. In practice, AI technology is also applied to predict blast-induced ground vibration and yield high prediction precision [32; 33; 34]. For example, [PERSON] et al. [2] combined the particle swarm optimization (PSO) algorithm and extreme gradient boosting machine (XGBoost) for peak particle velocity prediction. In his paper, the PSO was utilized to search the optimal hyperparameters of XGBoost and a promising result was obtained. [PERSON] et al. [35] proposed a hybrid model that is combined with the firefly algorithm (FFA) and an artificial neural network (ANN). The prediction performance of FFA-ANN was compared with classification and regression tree (CART), support vector machine (SVM), and k-nearest neighbor (KNN) models, and the FFA-ANN model provided the best performance. [PERSON] [36] utilized the genetic algorithm (GA) to optimize the ANN, and have developed an efficient model for PPV prediction after comparing the performance of the neuro-fuzzy inference system by using 70 blast vibration datasets. In 2009, [PERSON] and [PERSON] [37] developed an ANN model for blast-induced ground vibration prediction, and the prediction results of the ANN model is highly encouraging. [PERSON] et al. [38] tested the feasibility of the FCM-QRNN (fuzzy C-means clustering and quantile regression neural network) model and found that the model has higher accuracy than other soft computing models such as ANN and QRNN. [PERSON] et al. [39] combined the k-means clustering algorithm (HKM) and ANN and utilized that model to predict blast-induced ground vibration. In this model, the input datasets should be clustered first by the HKM algorithm, and the result obtained will be used to train the ANN model. In addition to these studies, the blast-induced ground vibration in some famous mines such as the Nui Beo open-cast mine [7], Miduk copper mine [40] and Gol-E-Gohar iron ore mine [41] were studied by using the artificial intelligence technology in previous literature.