Prediction of blast‑induced ground vibrations in quarry sites: a comparison of GP, RSM and MARS
Introduction
Blasting operation is the most economical strategy to break the rocks in mines, tunnels, and construction [1]. Whenever an explosive operation occurs, a large amount of energy is released in the form of gas, heat, pressure, and stress waves. Only about 20–30% of explosive energy is used to break the rock and the residual energy caused the ground vibration (GV), fly rock, and noise [2]. Among the side effects caused by blasting operation, GV has the biggest share. The GV can make serious damage to the structures [3], [4]. According to many scholars [5], [6], [7], GV can be determined based on two parameters: peak particle velocity (PPV) and frequency. PPV as one of the specifications for vibration is considered as a basic parameter to control the damage level in a structure [8]. Mainly, PPV can be determined based on two factors of charge per delay and the distance from the surface of the blast [9].
From the past until now, several empirical models have been proposed by researchers to predict the PPV. The first model created to predict the PPV is done by thef United State Bureau of Mines (USBM) [10]. Afterward, a number of Predictor models of vibration such as Langefors-Kihlstrom [11], General Predictor [12], Ambraseys-Hendron [13], Bureau of Indian Standard [14], and CMRI predictor [15] have been proposed for the estimation of PPV. The empirical methods have two major constraints: lack of generalizability and a limited number of input variables. Some researchers have suggested the theoretical models based on the physics of the blast. For example, Sambuelli [16] proposed a theoretical model to predict the PPV based on blast design and rock specifications. However, due to the complex nature of the blast process and its very nonlinear transactions with the non-homogeneous and non-isotropic land, obtaining a closed-form mathematical model is almost impossible. More recently, following the rapid growth of soft computational methods, including artificial intelligence (AI), researchers have tried to use these newfound techniques [17]. From the past decades until now, AI techniques have been successful in solving the complex problems of mining [2].
Genetic Programming (GP) is a powerful optimization technique based on genetic and natural selections. The main advantage of the GP-based methods is their ability to provide simple expressions without the need to assume a base form [18]. Recently, gene expression programming (GEP), a developed version of the GP and genetic algorithms (GA), has been widely used to solve the engineering problems. Monjezi et al. [19] used GEP to predict GV. To demonstrate the ability of the GEP model to estimate GV, linear multiple regression (LMR) and nonlinear multiple regression techniques (NLMR) were developed using the same data set. The results indicated that the new proposed model was able more accurately to predict GV resulting from blast than other development techniques. In another study, GEP was used by Faradonbeh et al. [20] to predict the PPV. They applied NLMR to check GEP performance and reported GEP is more appropriate to assess the PPV compared with NLMR model.
Response surface method (RSM) algorithm provided by Tandjiria et al. [21] was also proposed in the study of researchers. The main idea of this approach is to match the performance function with an explicit function of random variables and improve the estimation through repetition. This method uses the second order polynomial with squared terms [22]. Lü [23] used the RSM method and analytical solutions to show the applications of the method of reliability for exploring underground rocks. Babu et al. [24] studied RSM method and its benefits in the analysis of geotechnical systems and concluded that one of the benefits of this method is providing comparable results and its effectiveness in the identification of sensitive parameters in control systems.
Another method that has recently been of interest to researchers is the multivariate adaptive regression spline (MARS) method. MARS is a non-linear and non-parametric regression procedure that models the nonlinear responses between inputs and outputs of a system with a series of piecewise linear segments, the closed linear parts (splines), and various gradients. There is no specific hypothesis about the functional relationship between the input and output variables. The endpoint of segments is called nodes. The MARS model is built over the course of a two-stage process. The forward phase adds the acquirer of the potential nodes to improve the performance and the back phase is effective in the modification of the minimum conditions [25]. Samui developed the MARS model to determine the elastic modulus of the jointed rock mass. The results of the MARS model were compared with artificial neural network (ANN) values using the mean absolute error (MAE) criterion and found that the MARS model is a powerful model to determine the elastic modulus of jointed rock mass [26]. In another paper by Samui et al. [27], the ANFIS and MARS were used for prediction of spatial variability of a reduced level of rock depth in Bangalore. A comparison between MARS and adaptive neuro-fuzzy inference system (ANFIS) models revealed that the MARS model outperforms the ANFIS.
Table 1 is an overview of recent research literature for PPV prediction using different methods of soft computing. The table includes the number of data, the input parameters, the type of technique used, and the coefficient of determination (R2) for each of them. As can be seen in Table 1, in relation to the build of the predictor models for PPV, almost the number of studies in which different methods of AI-based are compared together has been low. Also, up until now, the most research has been done by using the artificial neural network method (ANN) and with a large number of input variables. Hence, in the present research, a comparison between methods of GP, RSM, and MARS will be made in order to build the PPV prediction models based on two variables of distance from blasting the face (D) and charge weight (W) using PPV data for monitored blast-induced earthquakes in quarry sites from the research literature.
Section snippets
Standard genetic programming theory
GP is a population-based learning approach established on evolutionary computing approach [59]. The process of genetic programming can generally be divided into four steps: (1) an initial population of solutions for the problem is produced. (2) the solutions are all in comparison to the training data to determine "fitness" by using a predefined error metrics; (3) the best solutions to minimize the error are proposed and the most severe solutions are discarded; (4) new solutions are created
Dataset
The used dataset in recent paper has been recorded using blast seismograph, during the blast induced earthquake triggered at 10 quarry sites in Ibadan and Abeokuta areas, Nigeria (https://doi.org/10.1016/j.dib.2018.04.103) as it was shown in Fig. 2 [65]. The data include peak particle velocity, distance from the blasting face (D) and charge weight per delay (W). The patterns and protocols applied by the quarry blasters during the shots have been followed in obtaining these data. For blasting
GP model
In this study, GPTIPS MATLAB toolbox version 1.0 was used to develop an equation for PPV prediction. In GP algorithm, there are some important parameters like the rate of genetic operators, number of population, tournament size, and number of genes where they do not follow a certain pattern to assign their rates [66]. So, according to several scholars (e.g. [19], [66], [67]), it is required to achieve an optimum combination of GP parameters based on the trial-and-error procedure to develop a
Conclusions
This paper aims to explore three methods of soft computing including genetic programming (GP), response surface methodology (RSM), and multivariate adaptive regression splines (MARS) to predict the PPV values. For this purpose, a dataset of 200 published data including PPV, distance from the blasting face (D), and charge weight per delay (W) was used. The data have been recorded using blast seismograph, during the blast-induced earthquake triggered at 10 quarry sites in Ibadan and Abeokuta
Conflict of interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
References (71)
- et al.
Feasibility of indirect determination of blast induced ground vibration based on support vector machine
Measurement
(2015) - et al.
Evaluation of blast-induced ground vibration predictors
Soil Dyn Earthq Eng
(2007) - et al.
Predicting blast-induced ground vibration using various types of neural networks
Soil Dyn Earthq Eng
(2010) - et al.
Assessment of artificial neural network and genetic programming as predictive tools
Adv Eng Softw
(2015) - et al.
Reliability analysis of laterally loaded piles using response surface methods
Struct Saf
(2000) - et al.
Probabilistic analysis of underground rock excavations using response surface method and SORM
Comput Geotech
(2011) - et al.
Multivariate adaptive regression splines for analysis of geotechnical engineering systems
Comput Geotech
(2013) - et al.
Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach
J Sound Vib
(2006) - et al.
Prediction of blast-induced ground vibration using artificial neural network
Int J Rock Mech Min Sci
(2009) - et al.
Evaluation and prediction of blast induced ground vibration using support vector machine
Min Sci Technol
(2010)
Development of a model to predict peak particle velocity in a blasting operation
Int J Rock Mech Min Sci
Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations
Int J Rock Mech Min Sci
Prediction of blast-induced ground vibration using artificial neural networks
Tunn Undergr Space Technol
An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran
J Rock Mech Geotech Eng
Blast vibration analysis by different predictor approaches-A comparison
Procedia Earth Planet Sci
Bankruptcy forecasting: a hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS)
Expert Syst Appl
Peak particle velocity data acquisition for monitoring blast induced earthquakes in quarry sites
Data Brief
New Gene Expression Programming models for normalized shear modulus and damping ratio of sands
Eng Appl Artif Intell
Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate
Agric Water Manag
Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model
Eng Comput
Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS
Geotech Geol Eng
Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting
Environ Earth Sci
An intelligent approach to prediction and control ground vibration in mines
Geotech Geol Eng
The modern technique of rock blasting
Ground vibration from shallow sub-surface blasts
Engineer
Vibration control in an opencast mine based on improved blast vibration predictors
Min Sci Technol
Theoretical derivation of a peak particle velocity–distance law for the prediction of vibrations from blasting
Rock Mech Rock Eng
Peak particle velocity prediction using support vector machines: a surface blasting case study
J South Afr Inst Min Metall
Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques
Eng Comput
Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction
Int J Environ Sci Technol
Cited by (36)
Blasting vibration effect and safety evaluation method of railway cross tunnels
2024, Tunnelling and Underground Space TechnologySupport vector regression optimized by black widow optimization algorithm combining with feature selection by MARS for mining blast vibration prediction
2023, Measurement: Journal of the International Measurement ConfederationEnhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks
2023, International Journal of Coal GeologySmart prediction of liquefaction-induced lateral spreading
2023, Journal of Rock Mechanics and Geotechnical EngineeringA novel four-stage hybrid intelligent model for particulate matter prediction
2024, Modeling Earth Systems and EnvironmentExperimental investigations of the effect of millisecond-delay time on the blast vibration reduction with electronic detonators
2023, JVC/Journal of Vibration and Control