Using artificial neural network (ANN) for modelling of load distribution along fully grouted cable bolts

Grayeli, Roozbeh; Moosavi, M.

doi:10.1179/174328606x98330

Cited by 3 publications

(1 citation statement)

References 8 publications

(7 reference statements)

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“…The same significant variables used in the MR models are used to train the AI model. NN have found application in mining; for example, Alehossein (2002) discusses an NN model for prediction of three Cartesian force components acting at and on the cutter-head of a rock cutting machine, Tam et al (2002) developed an artificial NN model predict excavator performance, and Grayeli and Moosavi (2006) used artificial NN modelling to predict the load distribution in mining cable bolts.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of truck cycle time prediction methods in open‐pit mining

Chanda

Gardiner

2010

Engineering, Construction and Architectural Management

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to compare the predictive capability of three methods of truck cycle time estimation in open-pit mining: computer simulation, artificial neural networks (NNs), and multiple regressions (MRs). The aim is to determine the best method. The most common method currently used is computer simulation. Design/methodology/approach -Truck cycle times at a large open pit mine are estimated using computer simulation, artificial NNs, and MRs. The estimated cycle times by each method are in turn compared to the actual cycle times recorded by a computerized mine monitoring system at the same mine. The errors associated with each method relative to the actual cycle times are documented and form the basis for comparing the three methods. Findings -The paper clearly indicates that computer simulation methods used in predicting truck cycle times in open-pit mining underestimate and overestimate the results for short and long hauls, respectively. It appears that both NN and regression models are superior in their predictive abilities compared to computer simulations.Research limitations/implications -The cycle time prediction models developed apply to a specific mine site and one has to be careful not to directly apply these models to other operations. Practical implications -The paper describes the implementation of regression and NN modelling. An opportunity exists for mines to utilise the large volumes of data generated to predict truck haulage cycle times more accurately and hence, improve the quality of mine planning. Originality/value -The paper addresses an area of need in the mining industry. Accurate prediction of cycle times is critical to mine planners as it impacts on production targets and hence, the budgets.

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Section: Introductionmentioning

confidence: 99%