Mahdi Hasanipanah

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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(More)
Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest(More)
The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized(More)
Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the(More)
Ground vibration is one of the most undesirable effects of blasting operation in surface mines. Therefore, it seems that the prediction of ground vibrations with a high degree of accuracy is necessary to reduce environmental effects. This article proposes a novel swarm intelligence algorithm based on cuckoo search (NSICS) to create a precise equation for(More)
The aim of the present study is to predict air-overpressure (AOp) resulting from blasting operations in the Shur river dam, Iran. AOp is considered as one of the most detrimental side effects induced by blasting. Therefore, accurate prediction of AOp is essential in order to minimize/reduce the environmental effects of blasting. This paper proposes a new(More)
Blasting operation is an inseparable operation of the rock fragmentation process in the surface mines and tunneling projects. Ground vibration is one of the most undesirable effects induced by blasting operation which can cause damage to the surrounding residents and structures. So, the ability to make precise predictions of ground vibration is very(More)
Drilling and blasting is an extensively used method for the rock fragmentation in surface mines and tunneling projects. Ground vibration is one of the most important environmental effects produced by blasting operations. In this research work, classification and regression tree (CART), multiple regression (MR), and different empirical models are used to(More)
Air overpressure is one of the most undesirable destructive effects induced by blasting operation. Hence, a precise prediction of AOp has vital importance to minimize or reduce the environmental effects. This paper presents the development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm(More)
Precise prediction of blast-induced ground vibration is an essential task to reduce the environmental effects in the surface mines, civil and tunneling works. This research investigates the potential of imperialist competitive algorithm (ICA) in approximating ground vibration as a result of blasting at three quarry sites, namely Ulu Tiram, Pengerang and(More)