Data mining for tunnel support stability: neural network approach

Leu, Sou-Sen; Chen, Chee-Nan; Chang, Shiu-Lin

doi:10.1016/s0926-5805(00)00078-9

Cited by 73 publications

(26 citation statements)

References 18 publications

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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: 99%

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: 99%

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

2009

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“…The procedure for data mining implementation consists of five steps [10]: (1) objective determination; (2) data preparation; (3) data transformation; (4) data mining; and (5) result analysis. There are some information techniques available for data mining implementation such as symbolic learning, case-based reasoning, and artificial neural networks.…”

Section: Data Mining Processmentioning

confidence: 99%

Mining of Conceptual Cost Estimation Knowledge with a Neuro Fuzzy System

Yu¹,

Lee²

2004

Proceedings of the 21st International Symposium on Automation and Robotics in Construction

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Conceptual cost estimation during the early stage of a construction project plays important role for feasibility analysis and project planning. Traditional approaches rely heavily on experienced engineers, and may cause loss of conceptual estimation knowledge of the firm. This paper proposes a method integrates a previous developed conceptual cost estimation method (PIREM) with the ANFIS neuro-fuzzy system for mining of cost estimation data. A case study of residential building projects in Mainland China is conducted to test the proposed method. The testing results show that the proposed method does not only achieve high system accuracy, but also provide many features desirable for estimators such as explicit fuzzy decision rules and graphical sensitivity analysis presentation.

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“…ANN approach has been used widely in geotechnical and geomechanical engineering problems [30][31][32][33][34][35][36][37][38][39][40][41][42]. Due to above mentioned defects of numerical, experimental and analytical methods in design of segmental tunnel lining, application of ANN methods seems to be a new alternative solution as prediction tools.…”

Section: Introductionmentioning

confidence: 99%