A neuro-fuzzy model for modulus of deformation of jointed rock masses

Gökçeoğlu, Candan; Yeşilnacar, Ertan; Sönmez, H.; Kayabaşı, Ali

doi:10.1016/j.compgeo.2004.05.001

Cited by 97 publications

(27 citation statements)

References 21 publications

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“…, x n ) and the corresponding output variable (y) in a set of linguistic fuzzy rules that can be easily interpreted, such as IF (x 1 is high AND x 2 is low) THEN (y is high), c = 0.9, where (c = 0.9) is the rule confidence, which indicates the degree to which the above rule has contributed to the output. Examples of such applications in geotechnical engineering include Ni et al [100], Shahin et al [16], Gokceoglu et al [62], Provenzano et al [19], and Padmini et al [18].…”

Section: Model Transparency and Knowledge Extractionmentioning

confidence: 99%

“…Other applications of ANNs in geotechnical engineering include earth retaining structures [56], dams [57,58], blasting [59], mining [60], environmental geotechnics [61], rock mechanics [62][63][64][65][66][67], site characterization [68], tunnels and underground openings [69][70][71][72][73][74], slope stability and landslides [71,[75][76][77][78][79], and deep excavation [80].…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Shahin

Jaksa

Maier

2009

Advances in Artificial Neural Systems

120

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Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

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Section: Model Transparency and Knowledge Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Shahin

Jaksa

Maier

2009

Advances in Artificial Neural Systems

120

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“…It takes the advantage of recognizing patterns and adapting the method to cope with the changing environment from neural networks and it takes the advantage of incorporating human knowledge and expertise to deal with uncertainty and imprecision taken from fuzzy logic. Due to these advantages, the ANFIS has been increasingly used in earth sciences in the applications complicated by high uncertainty (Gokceoglu et al 2004;Singh et al 2007;Iphar et al 2008;Yilmaz and Yuksek 2009;Dagdelenler et al 2011;Kucuk et al 2011;Yilmaz and Kaynar 2011;Basarir et al 2014;Asrari et al 2015;Basarir and Karpuz 2016;Fattahi 2016). Basarir et al used one of the evolving soft computing methods, ANFIS, for the interpretation and analysis of the MWD (Measurement While Drilling) data for the prediction of Rock Quality Designation (RQD) , percentage of drill core pieces in length of 10 cm or more.…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Computational monitoring in real time: review of methods and applications

Dyskin

Başarır

Doherty

et al. 2018

Geomech. Geophys. Geo-energ. Geo-resour.

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Continuous monitoring of mechanical parameters determining the state of natural and manmade systems is essential in a wide range of engineering disciplines from mechanical to civil and geotechnical engineering. To be effective, the monitoring response time needs to be commensurate with the characteristic time of variation of the processes being monitored e.g. from seconds as for example in some machinery, to weeks and months for mining excavations and years in the case of structures. The methods of measurement can therefore differ significantly for different applications. In spite of this, the methods of information processing-the computational monitoring-have considerable commonality. This review focuses on the methods of computational monitoring in solid and structural mechanics problems in different engineering applications. The traditional methods of monitoring are reviewed along with the corresponding computational and measurement methods. The basic principles of the computational monitoring in real time are established.

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“…FL and ANFIS were used by different authors to predict target parameters from a set of input data. Gokceoglu et al (2004) used neurofuzzy model for modulus of deformation of jointed rock masses. Kadkhodaie-Ilkhchi et al (2009) used a committee fuzzy inference system to predict petrophysical data from seismic attributes.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

Jafari

Kadkhodaie

Sharghi

et al. 2013

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Résumé -Intégration de système d'inférence adaptative de neurone flou (ANFIS), de réseaux de neurones (NN) et de méthodes géostatistiques pour la modélisation de la densité de fractures -Les images de diagraphies fournissent des informations utiles pour l'étude de fractures dans des réservoirs naturellement fracturés. L'inclinaison, l'azimut, l'ouverture et la densité de fractures peuvent être obtenus à partir des images de diagraphies et ces éléments ont une grande importance dans la caractérisation des réservoirs naturellement fracturés. L'imagerie de toutes les parties fracturées des réservoirs d'hydrocarbures et l'interprétation des résultats sont des processus longs et coûteux. Dans cette étude, une méthode améliorée pour faire une corrélation quantitative entre les densités de fractures obtenues à partir des images de diagraphies et de données conventionnelles, a été proposée par l'intégration des différents systèmes d'intelligence artificielle. Pour l'estimation globale de la densité de fractures à partir de données de diagraphies conventionnelles, la méthode proposée combine les résultats obtenus à partir d'algorithmes du système d'inférence adaptative flou de neurones (ANFIS) et du réseau de neurones (NN). Une méthode simple de moyenne a été utilisée pour obtenir un meilleur résultat en combinant les résultats de l'ANFIS et NN. L'algorithme a été appliqué à d'autres puits du champ pour obtenir la densité de fracture. Afin de modéliser la densité de fractures dans le réservoir, nous avons utilisé des algorithmes de simulation et de variographie séquentiels comme la Simulation à Indicateurs Séquentiels (SIS) et la Simulation Gaussienne Tronquée (TGS). L'algorithme global a été appliqué au réservoir d'Asmari de l'un des champs pétroliers du sud-ouest iranien. L'analyse de l'histogramme a été appliquée au contrôle de la qualité des modèles obtenus. Les résultats de cette étude montrent que pour nombre plus élevé de fractures, l'algorithme de faciès TGS fonctionne mieux que le SIS, mais que pour un petit nombre de faciès de fractures les deux algorithmes fournissent des résultats à peu près identiques. Abstract

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A neuro-fuzzy model for modulus of deformation of jointed rock masses

Cited by 97 publications

References 21 publications

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Computational monitoring in real time: review of methods and applications

Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

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