Calibration of the microparameters of the discrete element method using a relevance vector machine and its application to rockfill materials

Abstract To accurately simulate the mechanical properties of rockfill materials under triaxial stress using the discrete element method (DEM), the microparameters of the rockfill material DEM model are calibrated based on the macroparameters of a constitutive model. For the calibration model, a refined 3-D numerical triaxial test is established to obtain the deformation curves of the rockfill under different confining pressures. Deformation curves with different microparameters in the contact model are then calculated by numerical triaxial tests to determine the macroparameters of the constitutive model. Then, a nonlinear relationship between the macroparameters of the E-B model and the microparameters of the contact bond model is built by a relevance vector machine. A memetic algorithm is employed to calibrate the microparameters of the rockfill, thereby establishing a calibration model based on a relevance vector machine and a memetic algorithm. Afterwards, a numerical triaxial test example of rockfill materials is used to verify the feasibility of this calibration model, and then the fabric microparameters under different confining pressures are analysed. In addition, to determine the influence region, a failure process of a rockfill slope is simulated with the calibrated microparameters of the DEM model. In summary, this calibration method substantially improves the numerical results, thereby enhancing the ability to determine the mechanical properties of construction materials and solve various problems in engineering.

[1]  Etienne Horn.  The calibration of material properties for use in discrete element models , 2012 .

[2]  Fulvio Tonon,et al.  Modeling Lac du Bonnet granite using a discrete element model , 2009 .

[3]  Zengguang Xu,et al.  Behaviour of concrete-face rockfill dam on sand and gravel foundation , 2015 .

[4]  Gang Ma,et al.  Assessment of the crest cracks of the Pubugou rockfill dam based on parameters back analysis , 2016 .

[5]  Michael E. Tipping.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[6]  R. Bathurst,et al.  Analytical study of induced anisotropy in idealized granular materials , 1989 .

[7]  A. Fakhimi,et al.  Application of Dimensional Analysis in Calibration of a Discrete Element Model for Rock Deformation and Fracture , 2007 .

[8]  Richard J. Bathurst,et al.  Observations on stress-force-fabric relationships in idealized granular materials , 1990 .

[9]  Zhijun Zhang,et al.  DEM simulation of bionic subsoilers (tillage depth >40 cm) with drag reduction and lower soil disturbance characteristics , 2018, Adv. Eng. Softw..

[10]  Zhang Xiaoping,et al.  Research on mesomechanical parameters of rock and soil mass based on BP neural network , 2011 .

[11]  Junjie Li,et al.  System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling , 2015 .

[12]  P. Cundall,et al.  A bonded-particle model for rock , 2004 .

[13]  N. Zhang,et al.  Discrete element method simulations of offshore plate anchor keying behavior in granular soils , 2020, Marine Georesources & Geotechnology.

[14]  Huiming Tang,et al.  Microparameter Prediction for a Triaxial Compression PFC3D Model of Rock Using Full Factorial Designs and Artificial Neural Networks , 2013, Geotechnical and Geological Engineering.

[15]  S. Shafaei,et al.  Bonded-particle model calibration using response surface methodology , 2017 .

[16]  Catherine O'Sullivan,et al.  Application of Taguchi methods to DEM calibration of bonded agglomerates , 2011 .

[17]  A. S. Tawadrous,et al.  Prediction of uniaxial compression PFC3D model micro‐properties using artificial neural networks , 2009 .

[18]  Michael D. Shields,et al.  The generalization of Latin hypercube sampling , 2015, Reliab. Eng. Syst. Saf..

[19]  Fulvio Tonon,et al.  Calibration of a discrete element model for intact rock up to its peak strength , 2010 .

[20]  E. Alonso,et al.  A particle model for rockfill behaviour , 2015 .

[21]  Lipeng Liu,et al.  Development and present situation of hydropower in China , 2019, Water Policy.

[22]  Peng Chen.  Effects of Microparameters on Macroparameters of Flat-Jointed Bonded-Particle Materials and Suggestions on Trial-and-Error Method , 2017, Geotechnical and Geological Engineering.

[23]  Stefan Pirker,et al.  Identification of DEM simulation parameters by Artificial Neural Networks and bulk experiments , 2016 .

[24]  Lee Min Lee,et al.  Discrete element modeling of a rainfall-induced flowslide , 2012 .

[25]  Fulvio Tonon,et al.  Discrete Element Modeling of Drop Tests , 2012, Rock Mechanics and Rock Engineering.

[26]  Qing Yang,et al.  Determination of microscopic parameters of quartz sand through tri-axial test using the discrete element method , 2017 .

[27]  Junjie Li,et al.  Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms , 2019, Adv. Eng. Softw..

[28]  Buddhima Indraratna,et al.  Micromechanics-Based Investigation of Fouled Ballast Using Large-Scale Triaxial Tests and Discrete Element Modeling , 2017 .

[29]  David J. Williams,et al.  A calibration methodology to obtain material parameters for the representation of fracture mechanics based on discrete element simulations , 2017 .

[30]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[31]  N. Zhang,et al.  Discrete numerical simulations of torpedo anchor installation in granular soils , 2019, Computers and Geotechnics.

[32]  E. Maranha das Neves.  Advances in rockfill structures , 1991 .

[33]  Gang Ma,et al.  Macro–micro responses of crushable granular materials in simulated true triaxial tests , 2015 .

[34]  J. M. Duncan,et al.  Nonlinear Analysis of Stress and Strain in Soils , 1970 .

[35]  Lei Shao,et al.  Discrete element simulation of crushable rockfill materials , 2013 .

[36]  N. Zhang,et al.  Three-Dimensional Simulations of Plate Anchor Pullout in Granular Materials , 2019, International Journal of Geomechanics.

[37]  Xiangtao Li,et al.  An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure , 2013, Adv. Eng. Softw..

[38]  Min Wang,et al.  Calibrating the Micromechanical Parameters of the PFC2D(3D) Models Using the Improved Simulated Annealing Algorithm , 2017 .

[39]  C. J. Coetzee,et al.  Calibration of discrete element properties and the modelling of packed rock beds , 2014 .

[40]  Jeoung Seok Yoon,et al.  Application of experimental design and optimization to PFC model calibration in uniaxial compression simulation , 2007 .

[41]  D. Petley,et al.  Global fatal landslide occurrence from 2004 to 2016 , 2018, Natural Hazards and Earth System Sciences.

[42]  S. Lei,et al.  A study on the effects of microparameters on macroproperties for specimens created by bonded particles , 2006 .

[43]  M. Chi.  STUDY OF EFFECTS OF MICROPARAMETERS ON MACROPROPERTIES FOR PARALLEL BONDED MODEL , 2012 .

[44]  Klaus Thoeni,et al.  Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter , 2018 .

[45]  Björn Stenger,et al.  Pose estimation and tracking using multivariate regression , 2008, Pattern Recognit. Lett..

[46]  Chong Xu,et al.  Numerical investigation of landslide kinetics for the recent Mabian landslide (Sichuan, China) , 2019, Landslides.

[47]  Guofeng Wang,et al.  Force based tool wear monitoring system for milling process based on relevance vector machine , 2014, Adv. Eng. Softw..

[48]  C. González-Montellano,et al.  Validation and experimental calibration of 3D discrete element models for the simulation of the discharge flow in silos , 2011 .

[49]  Hongjie Chen,et al.  Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches , 2014, Landslides.

[50]  Dingena L. Schott,et al.  A calibration framework for discrete element model parameters using genetic algorithms , 2018, Advanced Powder Technology.

[51]  R. Fell,et al.  The statistics of embankment dam failures and accidents , 2000 .

[52]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.