... in the number of calibratable microparameters, range of macroscopic responses and degree of freedom in user-defined constraints. To overcome such limitations, a novel calibration method is proposed utilizing the constrained optimization of an artificial neural network (ANN). The ANN is trained with 600 PFC3D...

... categories of igneous, metamorphic and sedimentary rocks were used. Imperial equations and prediction models were determined using regression analysis and neural networking, respectively. Finally, using the proposed equation, obtained values were classified along with the different classifications for rock...

...) and igneous rocks (granitoids and dolerite). A back propagation artificial neural network was developed and trained in order to predict UCS. The input parameters were unit weight γ, (Is ( 50 )), (σ t ), and lithology. The lithology was introduced in the neural network as a qualitative input parameter...

... porosity, density and penetration rate. For prediction of UCS, artificial neural networks were developed between UCS and input data resulting a practical correlation. In this research, a long well segment possessing complete and continuous data coverage has been analysed, and collected data of the wellbore...

... been developed for the purpose of estimating important physico-mechanical properties of this limestone. The models are based on the results of many laboratory tests. Complex and simple estimation models have been mutually compared. The modelling is based on neural networks and multiple and simple...

... of RES applications in other fields such as environmental studies, road construction, marine sediments analysis, etc.). Artificial Intelligence Zare Naghadehi database metals & mining system approach interaction matrix slope instability index MSII neural network Case History msii...

... Abstract As substitution method, artificial neural network instructed based on numerical analyzed patterns except than more feasibility and speed than other methods can also reach the accuracy required in numerical modeling. In this paper a model based on Perspetron multilayer artificial...

... with the best fit relationship is reviewed. Secondly, an artificial neural network (ANN) is developed using actual data sets, and showed higher accuracy. The resulted network can be used to estimate UCS of the roof rock in coal extracting areas in the mentioned zone by performing simple in-situ Schmidt hammer...

... (ANFIS) seems to be suited successfully to model complex problems where the relationship between the model variables is unknown. ANFIS was used by various researchers worldwide. Artificial Intelligence adaptive neuro-fuzzy inference system application neural network uniaxial compressive...

... of laboratory tests on dry limestone specimen including 20 Unconfined Compression Tests and 20 Brazilian Tests have been used. Then, to apply Artificial Neural Networks, a Radial Basis Network is developed to reach a relationship between BTS and UCS. Based on the low Mean Squared Error of the network, a new...

.... However, because of its higher costs, it is important to use ID drilling in rock formations where it is strictly required. For that reason, this paper aims to develop an Artificial Neural Network (ANN) model in which rock properties and acoustic emission (AE) parameters are considered as main inputs...

... machine learning initiation stress crack damage stress Upstream Oil & Gas input parameter neural network physical property mean value compressive strength best performance porosity intelligent tool granite Artificial Intelligence resistance different combination prediction...

...Rock Engineering and Rock Mechanics: Structures in and on Rock Masses Alejano, Perucho, Olalla & Jiménez (Eds) © 2014 Taylor & Francis Group, London, 978-1-138-00149-7 Artificial Neural Network (ANN) based model for predicting of overall strength of Volcanic Bimrock H. Sonmez, A. Coskun, M...

... of the artificial neural network using unsupervised learning and an effective tool for clustering and visualizing complex high-dimensional data on a single low-dimensional (typically two and/or three dimensional) map, saving information among original data without any external criteria. The main objective...

... borehole numerical model explosive mass monitoring point statistical analysis dispersion neural network simple numerical model harbour finite-difference model Leixões Harbour vibration attenuation law Scenario velocity value Rock Mechanics in Civil and Environmental Engineering Zhao...

...Rock Mechanics in Civil and Environmental Engineering Zhao, Labiouse, Dudt & Mathier (eds) © 2010 Taylor & Francis Group, London, ISBN 978-0-415-58654-2 Analysis of crack coalescence in rock bridges using neural network A. Ghazvinian, V. Sarfarazi & S.A. Moosavi Rock Mechanics Division, Tarbiat...

... parameter infanti consideration rock mass neural network Neural Network Modeling prediction scour depth equation 477 Rock Engineering in Difficult Ground Conditions Soft Rocks and Karst Vrkljan (ed) © 2010 Taylor & Francis Group, London, ISBN 978-0-415-80481-3 Neural network modeling of scour...

... machine learning node prediction inference system consequent parameter uniaxial compressive strength neural network Artificial Intelligence strength testing data if-then rule firing strength rock sample Anfis 327 Rock Engineering in Difficult Ground Conditions Soft Rocks and Karst...

... Artificial Intelligence Scott method consolidation test square-root-of-time fitting method BP model machine learning calculation consolidation coefficient vector transfer function fitting method input vector Back Propagation neural network evaluate consolidation coefficient Neural...

... ABSTRACT: This paper is concerned with the description of the constitutive behaviour of geomaterials of wide ranging character using artificial neural networks (ANNs). The basic idea is to increase the possible options for selection of constitutive models for geotechnical analysis and design...