Reference health
A prediction method of stress intensity factor for mode-I crack in coal rock based on deep learning
25/25 checkable references clean · checked 2026-07-22
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 25 checked references that resolve
resolves10.1115/1.4011547
Analysis of Stresses and Strains Near the End of a Crack Traversing a Plate
resolves10.1016/j.engfracmech.2007.09.008
Theoretical analysis of the effects of relative crack length and loading angle on the experimental results for cracked Brazilian disk testing
resolves10.1016/j.ijrmms.2017.01.003
Stress intensity factors for a Brazilian disc with a central crack subjected to compression
resolves10.1007/s00603-015-0794-y
‘Mathematical’ Cracks Versus Artificial Slits: Implications in the Determination of Fracture Toughness
resolves10.1002/nme.3037
Direct estimation of generalized stress intensity factors using a three‐scale concurrent multigrid X‐FEM
resolves10.1002/nme.5157
Modeling arbitrary crack propagation in coupled shell/solid structures with X‐FEM
resolves10.1016/j.engfracmech.2020.107308
High-order stable generalized/eXtended finite element approximations for accurate stress intensity factors
resolves10.1007/s10704-020-00426-6
A comparison between some fracture modelling approaches in 2D LEFM using finite elements
resolves10.1016/j.enganabound.2011.11.010
Dynamic fracture analysis of a smoothly inhomogeneous plane containing defects by BEM
resolves10.1016/j.enganabound.2019.04.029
Boundary element analysis of the stress intensity factors of plane interface cracks between dissimilarly adjoined anisotropic materials
resolves10.1016/j.engfracmech.2020.107302
Novel special crack-tip elements for interface crack analysis by an efficient boundary element method
resolves10.1016/j.enganabound.2019.06.009
Solution of the 3-D elastodynamic contact problem for body with cracks using the BIEM and constrained optimization algorithm
resolves10.1016/j.cma.2019.01.005
Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning
resolves10.1007/s10596-009-9154-x
Textural identification of basaltic rock mass using image processing and neural network
resolves10.1190/geo2020-0121.1
An unsupervised deep-learning method for porosity estimation based on poststack seismic data
resolves10.1038/s41467-020-17841-x
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
resolves10.1016/j.fuel.2020.117416
Identification of rock pore structures and permeabilities using electron microscopy experiments and deep learning interpretations
resolves10.1016/j.ijrmms.2012.12.043
Fracture of sandstone characterized by digital image correlation
resolves10.1364/AO.32.001839
Digital speckle-displacement measurement using a complex spectrum method
resolves10.1117/1.2714926
Full-field strain measurement using a two-dimensional Savitzky-Golay digital differentiator in digital image correlation
resolves10.1016/j.jeurceramsoc.2016.07.007
Analysis of wedge splitting test on refractory castable via integrated DIC
resolves10.1016/j.jeurceramsoc.2018.07.039
Comparison of two full-field identification methods for the wedge splitting test on a refractory
resolves10.1111/j.1475-1305.2007.00345.x
Stress Intensity Factor Gauging by Digital Image Correlation: Application in Cyclic Fatigue
The 6 references without a DOI — listed, not checked
no DOI — not checkedEdge-cracked beams under four-point bending: approximate analytic solutions for stresses and displacements
no DOI — not checkedVery deep convolutional networks for large-scale image recognition
no DOI — not checkedV. Nair, G.E. Hinton, Rectified linear units improve restricted Boltzmann machines, In Proc. 27th International Conference on Machine Learning, 2010, 807–814.
no DOI — not checkedBatch normalization: accelerating deep network training by reducing internal covariate shift
no DOI — not checkedB. Yegnanarayana, Artificial Neural Networks, PHI Learning Pvt. Ltd., 2009.
no DOI — not checkedD.P. Kingma, J. Ba, Adam: A method for stochastic optimization, International Conference on Learning Representations (2014).
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