Proposing of a new soft computing-based model to predict peak particle velocity induced by blasting

Mokfi, Taha; Shahnazar, Azam; Bakhshayeshi, Ivan; Derakhsh, Ali Mahmodi; Tabrizi, Omid

doi:10.1007/s00366-018-0578-6

Cited by 34 publications

(3 citation statements)

References 45 publications

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“…Their proposed ANN model results can estimate the ground vibrations more accurately as compared with the various conventional predictor models available. Mokfi et al 41 introduced a group method of data handling (GDMH) NN to estimate the ground vibration conducted quarry in Penang, Malaysia, in 2018. The authors found that the GDMH provides more accurate prediction over other techniques.…”

Section: Summary Of the Previous Investigationmentioning

confidence: 99%

Estimation of peak particle velocity using soft computing technique approaches: a review

Ragam

Nimaje

2018

Noise & Vibration Worldwide

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Ground vibrations caused by blasting are undesirable consequences in the mining industry. It can cause serious damage to the nearby buildings and facilities. Hence, such vibrations have to be controlled to reduce the damage to the environment, and this may be achieved by predicting the blast peak particle velocity. The induced peak particle velocity has influenced by a number of parameters. They are spacing, burden, distance from the blast site, maximum explosive charge per delay, number of holes, stemming, and hole diameter. Existed empirical predictor approaches were used to evaluate the ground vibrations based on two parameters: distance and maximum explosive charge per hole only. To overcome the limitations of empirical methods, soft computing techniques are employed to estimate the peak particle velocity accurately. Since the late 1990s, several soft computing techniques such as artificial neural networks, fuzzy logic, and genetic algorithms have been proposed for achieving accurate prediction. This article presents a summary review of the developed soft computing in the 10 years following 2006 with particular emphasis on the number of influenced parameters and coefficient of determination (R 2).

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Section: Summary Of the Previous Investigationmentioning

confidence: 99%

Estimation of peak particle velocity using soft computing technique approaches: a review

Ragam

Nimaje

2018

Noise & Vibration Worldwide

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“…A large number of studies have shown that the vibration velocity and vibration frequency of the particle are closely related to the damage of the building [23][24][25]. It can directly reflect the vibration energy and play an important role in the evaluation of the vibration of building.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Analysis and Prediction of Ground Vibrations Due to Heavy Haul Railway Viaduct

Luo

2019

Mathematical Problems in Engineering

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The ground vibration wave induced by the viaduct section of the heavy freight wagons is transformed into the ground vibration problem under the condition of point source excitation after it is transmitted to soil through pier. When the speed and axle load of wagon become larger and larger, the impact on the surrounding buildings will also increase. In this paper, the ground vibration around Shenshan Village in section of Shuo-Huang railway line is monitored and numerical analyzed; the 3D numerical model of the Bridge-Pier-Field-House system is established. The relationship between peak vibration velocity (PPV) and the distance to the pier caused by heavy freight wagons at different speeds and different type of wagons is analyzed. The power function relationship between the two when measuring line perpendicular to the centerline of railway is verified. Based on the modified Sodev’s equation, the relationship between PPV of ground points and wagon speed, axle load, and soil properties is proposed, and the value of every parameter in the formula is discussed in detail. The concept of energy index is put forward for the first time in the formula, and the relationship between energy index and wagon speed and wagon weight is analyzed by regression analysis. The accuracy of the calculation model and prediction formula is verified by comparing field test results; an analytical method is proposed to predict the ground vibration induced by viaduct.

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“…In recent years, various advanced techniques and approaches have been developed to predict and reduce the undesirable effects of blast-induced PPV in open-cast [38] ANN 182 R 2 = 0.949 Armaghani et al [39] PSO-ANN 44 R 2 = 0.930; MSE = 10.71 Saadat et al [21] ANN 69 R 2 = 0.957; MSE = 0.000722 Hasanipanah et al [6] SVM 80 R 2 = 0.957; RMSE = 0.340 Amiri et al [40] ANN-KNN 75 R 2 = 0.880; RMSE = 0.540 Hasanipanah et al [41] PSO 80 R 2 = 0.938; RMSE = 0.240 Faradonbeh and Monjezi [42] GEP 115 R 2 = 0.874; RMSE = 6.732 Taheri et al [23] ABC-ANN 89 R 2 = 0.920; RMSE = 0.220 Armaghani et al [43] ICA 73 R 2 = 0.940; RMSE = 0.370 Behzadafshar et al [44] ICA 76 R 2 = 0.939; RMSE = 0.320 Abbaszadeh Shahri and Asheghi [45] ANN 37 R 2 = 0.954; RMSE = 0.157 Mokfi et al [46] GMDH 102 R 2 = 0.911; RMSE = 0.889 Torres et al [47] MLR-Empirical 178 R 2 = 0.898 [26] used random forest (RF) and support vector machine (SVM) algorithms to predict blast-induced PPV; 93 blasting events were used for development of the RF and SVM models in their study. Their results showed that the RF and SVM models were acceptable and the SVM model was better than the RF model throughout the PPV predicted values on the testing dataset.…”

Section: Introductionmentioning

confidence: 99%

Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam

et al. 2018

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Blasting is one of the cheapest and effective methods for breaking rock mass in open-pit mines. However, its side effects are not small such as ground vibration (PPV), air overpressure, fly rock, back break, dust, and toxic. Of these side effects, blast-induced PPV is the most dangerous for the human and surrounding environment. Therefore, evaluating and accurately forecasting blast-induced PPV is one of the most challenging issues facing open-pit mines today. In this paper, a series of artificial neural network models were applied to predict blast-induced PPV in an open-pit coal mine of Vietnam; 68 blasting events were used in this study for development of the ANN models. Of the whole dataset, 80% (approximately 56 observations) were used for the training process, and the rest of 20% (12 observations) were used for the testing process. Five ANN models were developed in this study with the difference in the number of hidden layers.

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Proposing of a new soft computing-based model to predict peak particle velocity induced by blasting

Cited by 34 publications

References 45 publications

Estimation of peak particle velocity using soft computing technique approaches: a review

Estimation of peak particle velocity using soft computing technique approaches: a review

Experimental and Numerical Analysis and Prediction of Ground Vibrations Due to Heavy Haul Railway Viaduct

Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam

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