# Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System
###### Abstract  
Efforts were made to predict and evaluate blast-induced ground vibrations and frequencies using an adaptive network-based fuzzy inference system (ANFIS), which has a fast-learning capability and the ability to capture the non-linear response during the blasting process. For this purpose, the ground vibrations generated by the blast in a tunnel tube were monitored at a residential building located directly above the tunnel tube. To investigate the usefulness of this approach, the prediction by the ANFIS was also compared to those by three of the most commonly used vibration predictors. The efficiency criteria chosen for the comparison between the predicted and actual data were the sum of squares due to error (SSE), the root mean squared error (RMSE), and the goodness of fit (R-squared and adjusted R-squared). The results show that the ANFIS prediction model performs better than the commonly used predictors.  
p eak particle velocity; frequency; predictor equation; adaptive network-based fuzzy inference system +
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different types of ANN models showed that the distance from the blast site, the number of boreholes per delay, and the maximum charge per delay are the most effective parameters in generating ground vibrations during blasting [4; 5]. [PERSON] et al. developed a neural network model with four main blast parameters as input, namely total charge, maximum charge per delay, distance from the blast source to the measuring point and hole depth [6]. To evaluate the ground vibrations caused by the blast, the techniques of dimensional analysis and ANN were applied, taking into account the blast design parameters and rock strength [7]. The control of the blast induced vibrations during the construction of the Masjed Soleiman dam was the crucial task, therefore the general regression neural network (GRNN) and the support vector machine (SVM) were used to predict the vibrations [8]. A hybrid model of the ANN and a particle swarm optimization (PSO) algorithm was implemented to predict ground vibrations based on 88 blast events [9]. Two novel hybrid artificial intelligent models for predicting the blast-induced peak particle velocity were presented by [PERSON] et al. [10]. [PERSON] and [PERSON] [11] applied the neuro-fuzzy technique for ground vibration and frequency prediction in opencast mine while [PERSON] [12] applied the ANN. [PERSON] et al. [13] investigated the advantages and limitations of ANNs for the prediction of surface settlements generated by earth pressure balance shield tunneling. The importance of site-specific factors in the prediction model for blast-induced ground vibrations was presented by [PERSON] [14]. ANNs have also been used to analyze the surface settlements caused by shotcrete-supported tunnels [15] and to predict the Tunnel Boring Machine Penetration Rate [16]. The summary of the previous studies on the PPV, which include several soft computing and machine learning methods, was presented by [PERSON] et al. [17].  
In this study, an attempt was made to predict and evaluate blast-induced ground vibrations and frequencies by integrating the maximum charge per delay (maximum quantity of explosive charge detonated on one interval within a blast) and the distance between the blast surface and the vibration monitoring point using an adaptive network-based fuzzy inference system (ANFIS). The modeled ground vibrations were generated by blasting in a tunnel tube. [PERSON] et al. [18] also applied the ANFIS model for the PPV prediction, but for the vibrations generated by the blast in an open-pit mining. To investigate the usefulness of this approach, the prediction by the ANFIS was compared to commonly used vibration predictors. To ensure that the comparison between the conventional predictors and the ANFIS model was realistic, the ANFIS model was designed with only two inputs, as per conventional predictors.