NDLI: Microparameter Prediction for a Triaxial Compression PFC3D Model of Rock Using Full Factorial Designs and Artificial Neural Networks

Content Provider	Springer Nature Link
Author	Sun, Miao jun Tang, Hui ming Hu, Xin li Ge, Yun feng Lu, Sha
Copyright Year	2013
Abstract	This paper presents an integrated approach that predicts the microparameters of the particle flow code in three dimensions (PFC3D) model in triaxial compression simulations. The new approach combines a full factorial design (FFD) with an artificial neural network (ANN). The ANN model maps the input factors (triaxial compressive strength, Poisson’s ratio, and Young’s modulus) onto output variables, which are microparameters that affect the macroscopic responses in a PFC3D model. Emphasis is placed on data collection and optimization of the ANN model using FFD. The data for training and testing the ANN model were obtained from laboratory experiments and numerical simulations of a PFC3D model according to the principles of FFD. Using a backpropagation artificial neural network (BPNN) optimized with FFD principles, the object of the current study (to reliably predict the microparameters for a PFC3D model) has been achieved because the predicting data obtained by the BPNN model were in excellent agreement with the testing data.
Starting Page	1249
Ending Page	1259
Page Count	11
File Format	PDF
ISSN	09603182
Journal	Geotechnical and Geological Engineering
Volume Number	31
Issue Number	4
e-ISSN	15731529
Language	English
Publisher	Springer Netherlands
Publisher Date	2013-04-02
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rock mechanics Prediction of microparameters Triaxial compression PFC3D model Full factorial design Artificial neural network Geotechnical Engineering & Applied Earth Sciences Hydrogeology Terrestrial Pollution Waste Management/Waste Technology Civil Engineering
Content Type	Text
Resource Type	Article
Subject	Soil Science Geology Architecture Geotechnical Engineering and Engineering Geology

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