Journal ArticleDOI
Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network
TLDR
It was found that the ANN model is more accurate as compared to the various empirical models available and a high conformity was observed between the measured and predicted peak particle velocity by the developed ANN model.Abstract:
The purpose of this article is to evaluate and predict blast-induced ground vibration at Shur River Dam in Iran using different empirical vibration predictors and artificial neural network (ANN) model. Ground vibration is a seismic wave that spreads out from the blasthole when explosive charge is detonated in a confined manner. Ground vibrations were recorded and monitored in and around the Shur River Dam, Iran, at different vulnerable and strategic locations. A total of 20 blast vibration records were monitored, out of which 16 data sets were used for training of the ANN model as well as determining site constants of various vibration predictors. The rest of the 4 blast vibration data sets were used for the validation and comparison of the result of ANN and different empirical predictors. Performances of the different predictor models were assessed using standard statistical evaluation criteria. Finally, it was found that the ANN model is more accurate as compared to the various empirical models available. As such, a high conformity (R
2 = 0.927) was observed between the measured and predicted peak particle velocity by the developed ANN model.read more
Citations
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Journal ArticleDOI
Feasibility of indirect determination of blast induced ground vibration based on support vector machine
TL;DR: In this article, a support vector machine (SVM) was applied and developed to predict ground vibration in blasting operations of Bakhtiari Dam, Iran, where 80 blasting works were investigated and results of peak particle velocity (PPV) as a vibration index, distance from the blast-face and maximum charge per delay were measured and monitored to utilize in the modeling.
Journal ArticleDOI
Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling
TL;DR: The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results, and the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.
Journal ArticleDOI
Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach
Mohsen Hajihassani,Danial Jahed Armaghani,Masoud Monjezi,Edy Tonnizam Mohamad,Aminaton Marto +4 more
TL;DR: In this paper, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting in a granite quarry site in Malaysia.
Journal ArticleDOI
Developing a hybrid PSO---ANN model for estimating the ultimate bearing capacity of rock-socketed piles
Danial Jahed Armaghani,Raja Shahrom Nizam Shah Raja Shoib,Koohyar Faizi,Ahmad Safuan A. Rashid +3 more
TL;DR: This analysis revealed that the hybrid PSO–ANN model offers a higher degree of accuracy compared to conventional ANN for predicting the Qu of rock-socketed piles, however, the developed model would be most useful in the preliminary stages of pile design and should be used with caution.
Journal ArticleDOI
Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm
TL;DR: Considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters.
References
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Book
Neural networks, fuzzy logic, and genetic algorithms : synthesis and applications
TL;DR: A study of Adaptive Neural Network Control System based on Differential Evolution Algorithm.
Journal ArticleDOI
Prediction of blast-induced ground vibration using artificial neural network
Manoj Khandelwal,Trilok Singh +1 more
TL;DR: In this article, an attempt has been made to evaluate and predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the artificial neural network (ANN) technique.
Journal ArticleDOI
Evaluation of blast-induced ground vibration predictors
Manoj Khandelwal,Trilok Singh +1 more
TL;DR: In this article, the authors mainly dealt with the prediction of blast-induced ground vibration level at a Magnesite mine in tecto-dynamically vulnerable hilly terrain in Himalayan region in India.
Review of criteria for estimating damage to residences from blasting vibrations
W.I. Duvall,D.E. Fogelson +1 more
Journal ArticleDOI
Prediction of blast-induced ground vibration using artificial neural networks
TL;DR: It was found that distance from blasting face is the most effective and stemming is the least effective parameter on the PPV, while Superiority of the proposed ANN model over empirical predictors and statistical model was examined by calculating coefficient of determination for predicted and measured PPV.
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