Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Abstract

Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

“…In the present study, a sensitivity analysis was also performed using Yang and Zang's [57] method to assess the impact of input parameters on PPV. This method has been used in some studies [58][59][60], and is formulated as:…”

Effects of Quarry Blasting Towards the Residential Area at Kangkar Pulai, Johor, Malaysia

“…This was due to the influence of number of blast per delay which in this case was about the factor of 2. Nonetheless, increased Q values will generate extra heave and shock energy that eventually triggers excessive ground vibration towards the surrounding environment (Shirani Faradonbeh et al 2016). Thus, extra precaution must be taken by blast designers to set a suitable length of explosive column so that the charge per column does not provide an immoderate Q value which will influence the occurrence of mismatch to occur.…”

A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

“…ANFIS-adaptive neuro-fuzzy inference system; ANN-artificial neural network; FIS-fuzzy inference system; SVM-support vector machine; PSO-particle swarm optimization; ICA-imperialism competitive algorithm; CART-classification and regression trees; GEP-gene expression programming. [31] ANN, FIS 2 162 Fisne et al [69] FIS 2 33 Li et al [72] SVM 2 32 Mohamadnejad et al [71] SVM, ANN 2 37 Ghasemi et al [73] FIS 6 120 Monjezi et al [74] ANN 3 20 Jahed Armaghani et al [4] PSO-ANN 9 44 Hajihassani et al [29] ICA-ANN 7 95 100 Hajihassani et al [77] PSO-ANN 8 88 Jahed Armaghani et al [12] ANFIS 2 109 Hasanipanah et al [78] CART 2 86 Jahed Armaghani et al [79] ICA 2 73 Faradonbeh et al [80] GEP 6 102 Shahnazar et al [81] PSO-ANFIS 2 81 Ghoraba et al [82] ANN, ANFIS 2 115 Despite the vast use of soft computing and ML techniques to predict PPV, a very limited number of studies are available that investigated the use of decision trees to predict PPV [68,73]. To this end, this study develops three decision tree-based models to find the best modeling approach.…”