A novel fuzzy inference system for predicting roof fall rate in underground coal mines

Razani, Mojtaba; Yazdani-Chamzini, Abdolreza; Yakhchali, Siamak Haji

doi:10.1016/j.ssci.2012.11.008

Cited by 55 publications

(33 citation statements)

References 26 publications

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“…The mean absolute percentage error contains positive numbers without having an upper bound [55,56]. The normalized mean square error presents non-negative numbers, which can be any numbers between zero and one, while the value of 0 means that the model is ideal and the value of one denotes that the constructed model is non-ideal.…”

Section: Model Performancementioning

confidence: 99%

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

et al. 2017

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Abstract:Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods, and (3) analytical methods. In this paper, the multivariate regression (MVR) method, which is one of the most popular linear models, and the artificial neural network (ANN) method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy.

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Section: Model Performancementioning

confidence: 99%

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

et al. 2017

Self Cite

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“…The output is entire fuzzy set, for the above rule it is Low. This set will be later defuzzified and crisp output in terms of risk score is obtained (MATLAB help; Ross, 2010;Mohammadi & Meech, 2013;Chen, Lien, Liu, Liou, Tzeng & Yang, 2011;Dağdeviren, Yavuz & Kılınç, 2009;Razani, Yazdani-Chamzini & Yakhchali, 2013). If the antecedent is in more than one part as shown above in present case then fuzzy operators are used.…”

Section: Construction Of Fuzzy Rule Basementioning

confidence: 99%

“…The implication function modifies that fuzzy set to the degree specified by the antecedent. The implication is done either by using Min function or Prod function (MATLAB help; Ross, 2010;Mohammadi & Meech, 2013;Dağdeviren et al, 2009;Razani et al, 2013). The fuzzy inference engine shown in Figure 3, will analyze input given to the system with the help of rule base developed and fed into the system.…”

Section: Construction Of Fuzzy Rule Basementioning

confidence: 99%

Integration of fuzzy reasoning approach (FRA) and fuzzy analytic hierarchy process (FAHP) for risk assessment in mining industry

Verma

Chaudhri

2014

JIEM

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Abstract:Purpose: Mining industry has always been known for its unsafe working environment. This industry is one of the most hazard prone industries. To maintain safety in workplace timely assessment of risk associated with different operations performed to extract ore from the ore body has become necessity. To serve the said purpose, present work demonstrates a robust hybrid risk assessment approach for mining industry.Design/Methodology: Accident data from 1995 to 2012 is reviewed to identify hazards contributed to negative outcomes. The FRA approach is implemented to evaluate the risk levels associated with identified hazard factors. Thereafter AHP pairwise comparison matrix is developed to obtain priority weights for the hazard factors. Final priority of hazards based on severity of level of risk associated with them is obtained considering the outcome of FRA approach in terms of risk score for the hazards, combined with the priority weights obtained from AHP technique.Findings: Defuzzified FAHP weight of hazard factors, this weight gives priority sequence of hazards to be considered for development of plan of mitigation.

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“…Its use in issues related to the process of mining, as well as in underground and surface mines, has been described inter alia in (Benović et al, 2013;Vujic et al, 2011;Bazzazi et al, 2009;Grychowski, 2008;Bascetin & Kesimal, 1999;Nguyen, 1985;Hosseini et al, 2012;Dezyani et al, 2006;Li, 2009;Razani et al, 2013;Karadogan et al, 2008).…”

Section: Designing Of Mining Processmentioning

confidence: 99%

The Use of Fuzzy Systems in the Designing of Mining Process in Hard Coal Mines

Brzychczy¹,

Kęsek²,

Napieraj³

et al. 2014

Archives of Mining Sciences

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This article presents examples of solutions supporting the design of certain elements of the mining process in coal mines. The focus is on two fuzzy systems: the first supports the selection of equipment for longwall faces (FSES); and the second supports the estimation of production results (FSOE). System FSES generates proposals for equipment in designed longwall faces. The module of fuzzing in this system enables a fuzzing operation for the following quantitative variables: longwall length; longwall height; longitudinal and crosswise incline of the longwall, workability of the coal and thickness of rock vein in a given section of the longwall. The knowledge base includes over 100 fuzzy rules indicating possible options for equipment under specified site conditions. After a proposal of equipment is generated, it is then possible to insert the values obtained into the second system FSOE, which estimates output for a given shift time using the chosen parameters. The module of fuzzing in system FSOE includes 9 variables, which are crucial in determining shift output for the given longwall face. The knowledge base in this system contains over 2000 rules.As a result of the operation of both systems, the designer receives both a proposal of equipment for the designed longwall face and the size of shift output under the given conditions. Operation of the two systems has been presented using a case study.Keywords: coal mine, mining process, fuzzy logic, fuzzy systems, modelling Logika rozmyta pozwala na płynne i stosunkowo dokładne opisanie istotnych zależności pomiędzy zmiennymi o charakterze nieprecyzyjnym lub mało dokładnym, które są danymi wejściowymi do procesu projektowania. Prowadzony przez system rozmyty proces wnioskowania na podstawie zapisanych w bazie wiedzy reguł pozwala na uogólnienie posiadanej przez projektantów wiedzy, a także prowadzenie wnioskowania w sposób zbliżony do rozumowania eksperta.W artykule zaprezentowano przykłady opracowanych rozwiązań wspomagających projektowanie wybranych elementów procesu wydobywczego w kopalniach węgla kamiennego. Przedstawiono dwa systemy rozmyte, pierwszy wspomagający dobór wyposażenia do projektowanych wyrobisk ścianowych (FSES) oraz drugi wspomagający szacowanie wyników produkcyjnych (FSOE). 742System FSES umożliwia wyznaczenie propozycji wyposażenia dla projektowanych wyrobisk ścianowych. Moduł rozmywania w tym systemie umożliwia przeprowadzenie operacji rozmycia nastę-pujących zmiennych ilościowych: długość ściany, wysokość ściany, nachylenie podłużne i poprzeczne ściany, urabialność węgla oraz grubość przerostów w przekroju ściany. Baza wiedzy obejmuje ponad 100 reguł rozmytych wskazujących w konkluzjach możliwe do zastosowania wyposażenie, w określonych warunkach wyrobiska.Po wyznaczeniu proponowanego wyposażenia, możliwe jest wprowadzenie otrzymanych wartości do drugiego systemu FSOE, który umożliwia oszacowanie wydobycia zmianowego dla zadanych parametrów. Moduł rozmywania systemu FSOE obejmuje 9 zmiennych, które konieczne są do wyznaczenia wydobycia zmian...

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A novel fuzzy inference system for predicting roof fall rate in underground coal mines

Cited by 55 publications

References 26 publications

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks

Integration of fuzzy reasoning approach (FRA) and fuzzy analytic hierarchy process (FAHP) for risk assessment in mining industry

The Use of Fuzzy Systems in the Designing of Mining Process in Hard Coal Mines

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