Analysis of powder factors for tunnel blasting using neural networks

Leu, Sou-Sen; Lin, Sheng-Feng; Chen, Ching-Kuang; Wang, Shih‐Wen

doi:10.1080/13855149809408787

Cited by 8 publications

(10 citation statements)

References 20 publications

(14 reference statements)

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“…BEM back-analysis was utilized to calculate far-field stress state (Li et al 2009). In this paper, more efficient methods such as neural network-based techniques may be preferred over traditional methods (Jaiswal et al 2004;Lee and Sterling 1992;Leu et al 1998Leu et al , 2001Singh 2002, 2005;Monjezi et al 2006a;Monjezi and Dehghani 2008).…”

Section: Introductionmentioning

confidence: 97%

Superiority of neural networks for pillar stress prediction in bord and pillar method

2009

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Estimation of pillar stress is a crucial task in underground mining. This is used to determine pillar dimensions, room width, roof conditions, and general mine layout. There are several methods for estimating induced stresses due to underground excavations, i.e., empirical methods, numerical solutions, and currently artificial intelligence (AI). AI based techniques are gradually gaining popularity especially for problems involving uncertainty. In this paper, an attempt has been made to predict stresses developed in the pillars of bord and pillar mining using artificial neural network. A comparison has also been done to compare the obtained results with the boundary element method as well as measured field values. For this purpose, a multilayer perceptron neural network model was developed. A number of architectures with different hidden layers and neurons were tried to get the best solution, and the architecture 5-20-8-1 was found to be an optimum solution. Sensitivity analysis was also carried out to understand the influence of important input parameters on pillar stress concentration.

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Section: Introductionmentioning

confidence: 97%

Superiority of neural networks for pillar stress prediction in bord and pillar method

2009

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“…Because the data that were used during this study were collected at a single tunnel construction site, so that the locations of the blastholes and ignition patterns were constant, the blasting conditions mentioned in this study were not required to be used as input parameters to the NN. The input parameters selected for this study, were based on those previously identified (Lilly, 1986;Scott, 1996;Leu et al, 1998Leu et al, , 2001. Fourteen input parameters were used to predict the powder factors.…”

Section: Nn Training and Parameter Determinationmentioning

confidence: 99%

“…In particular, NNs were applied to failure modes in tunnels by Lee and Sterling (1992), tunnel blasting by Leu et al (1998) and tunnel support by Leu et al (2001).…”

Section: Introductionmentioning

confidence: 99%

Influence of geological conditions on the powder factor for tunnel blasting

Jong

Lee

2004

International Journal of Rock Mechanics and Mining Sciences

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“…A large number of tools have been developed in AI to solve the most difficult problems and ANN is one of them. Literature review indicates that a lot of work has been done on the application of ANN to predict various aspects of blast-induced ground vibrations [21][22][23][24], air overpressure [25], fly rock [26][27][28], back break [29][30][31][32], powder factor [33][34][35], estimation of blast geometry [36][37][38], estimation of fragmentation [32,[39][40][41][42][43][44][45] etc.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Effect of Blast Hole Diameter on the Number of Oversize Boulders Using ANN Model

Dhekne

Pradhan

Jade

2015

J. Inst. Eng. India Ser. D

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Now-a-days, blasts are planned using large diameter blast holes. The loading density (kg/m) and subsequently the energy available for the breakage of the rockmass increase with the diameter. The in-hole velocity of detonation (VoD) of non-ideal explosive also boosts up with the increase in diameter till the optimum diameter is reached. The increase in the energy content and in-hole VoD cause a sizable effect on the rock fragmentation. The effect can be assessed by counting the number of oversize boulders. This paper explains as to how the technique of artificial neural network modeling was used to predict the number of oversize boulders resulting from ANFO and SME blasts with blast holes of different diameters. The results from ANFO blasts indicated that there was no significant variation in the number of oversize boulders with the diameter whereas a perceptible variation was noticed in case of SME blasts with the change in the diameter. The change in the number of oversize boulders in ANFO blasts was negligible because mean energy factor remained almost same even when the diameter of the blast holes was altered. The decrease in the number of oversize boulders in SME blasts was on account of increase in mean energy factor when the blast hole diameter was increased. The increase in the in-hole VoD due to increase in the diameter of the hole was not found to have an effect on the generation of oversize boulders as this increase was not substantial both in SME and ANFO blasts.

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Analysis of powder factors for tunnel blasting using neural networks

Cited by 8 publications

References 20 publications

Superiority of neural networks for pillar stress prediction in bord and pillar method

Superiority of neural networks for pillar stress prediction in bord and pillar method

Influence of geological conditions on the powder factor for tunnel blasting

Assessment of the Effect of Blast Hole Diameter on the Number of Oversize Boulders Using ANN Model

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