Predictive modeling of dynamic fracture growth in brittle materials with machine learning

Moore, Bryan A.; Rougier, Esteban; O’Malley, Daniel; Srinivasan, Gowri; Hunter, Abigail; Viswanathan, Hari

doi:10.1016/j.commatsci.2018.01.056

Title: Predictive modeling of dynamic fracture growth in brittle materials with machine learning

Abstract

We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path to failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.

Authors:

Moore, Bryan A. ^[1]; Rougier, Esteban ^[1]; O’Malley, Daniel ^[1]; Srinivasan, Gowri ^[1]; Hunter, Abigail ^[1]; Viswanathan, Hari ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:: 2018-02-22

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1425767

Alternate Identifier(s):: OSTI ID: 1547147

Report Number(s):: LA-UR-17-30614
Journal ID: ISSN 0927-0256; TRN: US1802161

Grant/Contract Number:: AC52-06NA25396; 20170103DR

Resource Type:: Accepted Manuscript

Journal Name:: Computational Materials Science

Additional Journal Information:: Journal Volume: 148; Journal Issue: C; Journal ID: ISSN 0927-0256

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE; Computer Science; Material Science; Machine Learning, Brittle Failure, Fracture Propagation

Citation Formats


                    Moore, Bryan A., Rougier, Esteban, O’Malley, Daniel, Srinivasan, Gowri, Hunter, Abigail, and Viswanathan, Hari. Predictive modeling of dynamic fracture growth in brittle materials with machine learning.  United States: N. p., 2018. 
Web.  https://doi.org/10.1016/j.commatsci.2018.01.056.

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                    Moore, Bryan A., Rougier, Esteban, O’Malley, Daniel, Srinivasan, Gowri, Hunter, Abigail, & Viswanathan, Hari. Predictive modeling of dynamic fracture growth in brittle materials with machine learning.  United States.  https://doi.org/10.1016/j.commatsci.2018.01.056

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                    Moore, Bryan A., Rougier, Esteban, O’Malley, Daniel, Srinivasan, Gowri, Hunter, Abigail, and Viswanathan, Hari. Thu .  
"Predictive modeling of dynamic fracture growth in brittle materials with machine learning".  United States.  https://doi.org/10.1016/j.commatsci.2018.01.056.  https://www.osti.gov/servlets/purl/1425767.

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@article{osti_1425767,

  title        = {Predictive modeling of dynamic fracture growth in brittle materials with machine learning},

  author       = {Moore, Bryan A. and Rougier, Esteban and O’Malley, Daniel and Srinivasan, Gowri and Hunter, Abigail and Viswanathan, Hari},

  abstractNote = {We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path to failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.},

  doi          = {10.1016/j.commatsci.2018.01.056},

  journal      = {Computational Materials Science},

  number       = C,

  volume       = 148,

  place        = {United States},

  year         = {2018},

  month        = {2}

}

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https://doi.org/10.1016/j.commatsci.2018.01.056

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Cited by: 4 works

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Figures / Tables:

Figure 1: Visualization of the extraction of pair data from a 2D HOSS fracture simulation. In the feature vector, the Ls are lengths of initial fractures, the θs are fracture orientations, Dist is the distance between the two fractures and min DB is the minimum distance to the boundary frommore »

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Works referencing / citing this record:

Fourier amplitude sensitivity test applied to dynamic combined finite‐discrete element methods–based simulations
journal, August 2018

Godinez, Humberto C.; Rougier, Esteban; Osthus, Dave
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Similar Records

Title: Predictive modeling of dynamic fracture growth in brittle materials with machine learning

Abstract

Citation Formats

Figures / Tables:

Development of intermetallic materials for aerospace systems journal, April 1992

Where Does Water Go During Hydraulic Fracturing?: D. O'Malley et al. Groundwater xx, no. x: xx-xx journal, October 2015

Introduction to Fracture Mechanics journal, January 1983

The dynamics of rapid fracture: instabilities, nonlinearities and length scales journal, March 2014

Computational modelling of impact damage in brittle materials journal, August 1996

Experimental investigation and poroplastic modelling of saturated porous geomaterials journal, December 2012

Computational materials: Multi-scale modeling and simulation of nanostructured materials journal, December 2005

The Phenomena of Rupture and Flow in Solids journal, January 1921

A Critical Analysis of Crack Propagation Laws journal, December 1963

Modern topics and challenges in dynamic fracture journal, March 2005

Particle methods in the study of fracture journal, November 2015

Coupled-volume multi-scale modelling of quasi-brittle material journal, May 2008

Microscale and mesoscale discrete models for dynamic fracture of structures built of brittle material journal, May 2003

Effective Elastic Moduli of Two-Dimensional Brittle Solids With Interacting Microcracks, Part I: Basic Formulations journal, June 1994

Elastic moduli of a cracked body journal, May 1983

Computational mechanics at the mesoscale journal, January 2000

Analysis of crack formation and crack growth in concrete by means of fracture mechanics and finite elements journal, November 1976

Foundations of statistical natural language processing journal, September 2002

Machine learning in bioinformatics journal, March 2006

Random Forests journal, January 2001

Multilayer feedforward networks are universal approximators journal, January 1989

A combined finite‐discrete element method in transient dynamics of fracturing solids journal, February 1995

A generalized anisotropic deformation formulation for geomaterials journal, October 2015

Validation of a three-dimensional Finite-Discrete Element Method using experimental results of the Split Hopkinson Pressure Bar test journal, September 2014

Popular Ensemble Methods: An Empirical Study journal, July 1999

Fourier amplitude sensitivity test applied to dynamic combined finite‐discrete element methods–based simulations journal, August 2018

A predictive failure framework for brittle porous materials via machine learning and geometric matching methods journal, January 2020

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials journal, July 2019

FSIS: a novel fluid–solid interaction solver for fracturing and fragmenting solids journal, January 2020

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics journal, May 2019

Development of intermetallic materials for aerospace systems
journal, April 1992

Where Does Water Go During Hydraulic Fracturing?: D. O'Malley et al. Groundwater xx, no. x: xx-xx
journal, October 2015

Introduction to Fracture Mechanics
journal, January 1983

The dynamics of rapid fracture: instabilities, nonlinearities and length scales
journal, March 2014

Computational modelling of impact damage in brittle materials
journal, August 1996

Experimental investigation and poroplastic modelling of saturated porous geomaterials
journal, December 2012

Computational materials: Multi-scale modeling and simulation of nanostructured materials
journal, December 2005

The Phenomena of Rupture and Flow in Solids
journal, January 1921

A Critical Analysis of Crack Propagation Laws
journal, December 1963

Modern topics and challenges in dynamic fracture
journal, March 2005

Particle methods in the study of fracture
journal, November 2015

Coupled-volume multi-scale modelling of quasi-brittle material
journal, May 2008

Microscale and mesoscale discrete models for dynamic fracture of structures built of brittle material
journal, May 2003

Effective Elastic Moduli of Two-Dimensional Brittle Solids With Interacting Microcracks, Part I: Basic Formulations
journal, June 1994

Elastic moduli of a cracked body
journal, May 1983

Computational mechanics at the mesoscale
journal, January 2000

Analysis of crack formation and crack growth in concrete by means of fracture mechanics and finite elements
journal, November 1976

Foundations of statistical natural language processing
journal, September 2002

Machine learning in bioinformatics
journal, March 2006

Random Forests
journal, January 2001

Multilayer feedforward networks are universal approximators
journal, January 1989

A combined finite‐discrete element method in transient dynamics of fracturing solids
journal, February 1995

A generalized anisotropic deformation formulation for geomaterials
journal, October 2015

Validation of a three-dimensional Finite-Discrete Element Method using experimental results of the Split Hopkinson Pressure Bar test
journal, September 2014

Popular Ensemble Methods: An Empirical Study
journal, July 1999

Fourier amplitude sensitivity test applied to dynamic combined finite‐discrete element methods–based simulations
journal, August 2018

A predictive failure framework for brittle porous materials via machine learning and geometric matching methods
journal, January 2020

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials
journal, July 2019

FSIS: a novel fluid–solid interaction solver for fracturing and fragmenting solids
journal, January 2020

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
journal, May 2019