Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations

Int. J. Environ. Sci. Technol.

Majid

et al. 2016

145

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.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Faradonbeh

Int. J. Environ. Sci. Technol.

Majid

et al. 2016

145

“…They compared the performance of ANN with that of the multivariate regression analysis (MVRA) and the United States Bureau of Mines (USBM) predictor and proved the superiority of their proposed model over MVRA and USBM in terms of estimation accuracy. Considering the distance from the blast-face to the monitoring point and the maximum charge per delay, Mohamed [38] applied ANN and a fuzzy inference system (FIS) to the prediction of AOp. He made a comparison between the results obtained from the predictive models and the values of regression analyses and observed field data.…”

Section: Aop Prediction Methodsmentioning

confidence: 99%

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Engineering with Computers

Hasanipanah

Mohamad

2015

126

including coefficient of determination (R 2 ), root mean square error and value account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models were selected among all constructed models. It was found that the ICA-ANN approach can provide higher performance capacity in predicting AOp compared to other predictive methods.

“…In these techniques, several other input parameters related to blast design, rock mass and explosive properties were utilized for ground vibration prediction (e.g., [6,10,22,23]). However, the application of statistical techniques is limited to a specific site and/or data and are not generic (or universal) in nature [22,24,25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mohamed [24] proposed both ANN and FIS models for estimating PPV and reported that FIS approach can provide slightly higher performance capacity in approximating the PPV. Based on the blast parameters obtained from Bakhtiari Dam, Iran, Hasanipanah et al [31] introduced a support vector machine (SVM) model to estimate PPV.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification and regression tree technique in estimating peak particle velocity caused by blasting

Khandelwal

Engineering with Computers

Faradonbeh

et al. 2016