On the public citation graph
Surrogate Deep Learning-FEM Framework for Fatigue Crack Growth Forecasting Using Multi-Step Direct Forecast Model
0 signed · 63 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf00033527openalex-snapshot
The effect of mean stress on fatigue crack propagation a literature review
inferredcites →https://doi.org/10.1007/s11665-024-09565-6openalex-snapshot
Effect of Load Ratio on Fatigue Crack Growth Behavior of SS316LN using Two-Parameter (ΔK and Kmax) Model
inferredcites →https://doi.org/10.1007/s11668-020-00959-xopenalex-snapshot
Fatigue Behavior of Welded API 5L X70 Steel Used in Pipelines
inferredcites →https://doi.org/10.1007/s11709-024-1040-zopenalex-snapshot
Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning
inferredcites →https://doi.org/10.1007/s12206-021-0310-0openalex-snapshot
Fracture properties and fatigue life assessment of API X70 pipeline steel under the effect of an environment containing hydrogen
inferredcites →https://doi.org/10.1016/0013-7944(69)90003-4openalex-snapshot
Effect of stress ratio on fatigue-crack growth in 7075-T6 aluminum-alloy sheet
inferredcites →https://doi.org/10.1016/j.commatsci.2025.114207openalex-snapshot
Application of machine learning for predicting hydrogen-assisted fatigue crack growth
inferredcites →https://doi.org/10.1016/j.engfailanal.2024.108934openalex-snapshot
Failure assessment of seam-welded pipe under fatigue and thermal loading
inferredcites →https://doi.org/10.1016/j.engfracmech.2010.06.009openalex-snapshot
Abaqus implementation of extended finite element method using a level set representation for three-dimensional fatigue crack growth and life predictions
inferredcites →https://doi.org/10.1016/j.engfracmech.2012.08.016openalex-snapshot
The interrelation of the parameters in the Paris equation of fatigue crack growth
inferredcites →https://doi.org/10.1016/j.engfracmech.2015.05.005openalex-snapshot
Effect of load ratio on fatigue crack growth in the near-threshold regime: A literature review, and a combined crack closure and driving force approach
inferredcites →https://doi.org/10.1016/j.ijfatigue.2006.12.008openalex-snapshot
A study of the stress ratio effects on fatigue crack growth using the unified two-parameter fatigue crack growth driving force
inferredcites →https://doi.org/10.1016/j.ijfatigue.2011.08.010openalex-snapshot
The numerical simulation of fatigue crack growth using extended finite element method
inferredcites →https://doi.org/10.1016/j.ijfatigue.2016.02.002openalex-snapshot
Stress intensity factors for fatigue analysis of weld toe cracks in a girth-welded pipe
inferredcites →https://doi.org/10.1016/j.ijfatigue.2017.05.026openalex-snapshot
Fatigue crack propagation in complex stress fields: Experiments and numerical simulations using the Extended Finite Element Method (XFEM)
inferredcites →https://doi.org/10.1016/j.ijfatigue.2021.106356openalex-snapshot
A novel method of multiaxial fatigue life prediction based on deep learning
inferredcites →https://doi.org/10.1016/j.ijfatigue.2022.106901openalex-snapshot
Influence of residual stresses on the fatigue life of welded joints. Numerical simulation and experimental tests
inferredcites →https://doi.org/10.1016/j.ijfatigue.2023.107947openalex-snapshot
Influence of grain size on fatigue strength of austenitic stainless steel (Investigation of ultimate strength dependency of fatigue strength)
inferredcites →https://doi.org/10.1016/j.ijfatigue.2024.108368openalex-snapshot
Stress ratio influence on Paris parameter and fatigue driving force pertinent to material properties
inferredcites →https://doi.org/10.1016/j.ijfatigue.2024.108678openalex-snapshot
A physics-informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth
inferredcites →https://doi.org/10.1016/j.ijhydene.2020.12.140openalex-snapshot
High cycle fatigue behaviors of API X65 pipeline steel welded joints in air and H2S solution environment
inferredcites →https://doi.org/10.1016/j.ijmecsci.2013.09.001openalex-snapshot
Fatigue crack growth simulations of 3-D problems using XFEM
inferredcites →https://doi.org/10.1016/j.ijmecsci.2017.06.007openalex-snapshot
An X-FEM approach for simulation of 3-D multiple fatigue cracks and application to double surface crack problems
inferredcites →https://doi.org/10.1016/j.ijmecsci.2019.01.040openalex-snapshot
Experimental and XFEM modelling of high cycle fatigue crack growth in steel welded T-joints
inferredcites →https://doi.org/10.1016/j.ijpvp.2011.07.003openalex-snapshot
Predictions for fatigue crack growth life of cracked pipes and pipe welds using RMS SIF approach and experimental validation
inferredcites →https://doi.org/10.1016/j.ijpvp.2022.104837openalex-snapshot
Chemical composition and weld cooling time effects on heat-affected zone hardness of line pipe steels
inferredcites →https://doi.org/10.1016/j.jcsr.2006.06.007openalex-snapshot
Fatigue reliability of welded steel structures
inferredcites →https://doi.org/10.1016/j.jmatprotec.2017.12.007openalex-snapshot
Phase field modeling of solidification microstructure evolution during welding
inferredcites →https://doi.org/10.1016/j.jmrt.2023.04.155openalex-snapshot
A machine learning model for predicting progressive crack extension based on experimental data obtained using DCPD measurement technique
inferredcites →https://doi.org/10.1016/j.matchar.2018.12.003openalex-snapshot
Microstructure and texture evolution in warm rolled API 5L X70 pipeline steel for sour service application
inferredcites →https://doi.org/10.1016/j.matdes.2012.09.051openalex-snapshot
Effects of microstructure alteration on corrosion behavior of welded joint in API X70 pipeline steel
inferredcites →https://doi.org/10.1016/j.matdes.2024.113533openalex-snapshot
A computational framework to predict weld integrity and microstructural heterogeneity: Application to hydrogen transmission
inferredcites →https://doi.org/10.1016/j.msea.2012.11.103openalex-snapshot
General relation between tensile strength and fatigue strength of metallic materials
inferredcites →https://doi.org/10.1016/j.oceaneng.2016.03.038openalex-snapshot
Fatigue analysis on offshore pipelines with embedded cracks
inferredcites →https://doi.org/10.1016/j.proeng.2010.03.197openalex-snapshot
Experimental and numerical investigations of fatigue crack growth in various specimen geometries
inferredcites →https://doi.org/10.1016/j.proeng.2014.11.029openalex-snapshot
Fatigue Crack Growth Studies on Narrow Gap Pipe Welds of Austenitic Stainless Steel Material
inferredcites →https://doi.org/10.1016/j.prostr.2018.12.176openalex-snapshot
Finite Element Analysis of Fatigue Response of Nickel Steel Compact Tension Samples using ABAQUS
inferredcites →https://doi.org/10.1016/j.taml.2023.100477openalex-snapshot
Statistical learning prediction of fatigue crack growth via path slicing and re-weighting
inferredcites →https://doi.org/10.1016/s0013-7944(00)00099-0openalex-snapshot
Effect of load ratio and maximum stress intensity on the fatigue threshold in Ti–6Al–4V
inferredcites →https://doi.org/10.1038/s41597-024-03862-4openalex-snapshot
A deep learning dataset for metal multiaxial fatigue life prediction
inferredcites →https://doi.org/10.1088/1757-899x/276/1/012027openalex-snapshot
A review of fatigue crack propagation modelling techniques using FEM and XFEM
inferredcites →https://doi.org/10.1111/ffe.13735openalex-snapshot
Developments in remeshing‐free fatigue crack growth simulation including a new adaptive virtual crack closure technique
inferredcites →https://doi.org/10.1111/ffe.13977openalex-snapshot
Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network
inferredcites →https://doi.org/10.1111/ffe.14277openalex-snapshot
Fatigue crack growth simulation by extended finite element method: A review of case studies
inferredcites →https://doi.org/10.1111/j.1460-2695.2009.01404.xopenalex-snapshot
Mean stress effects in strain–life fatigue
inferredcites →https://doi.org/10.1111/mice.12565openalex-snapshot
Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment
inferredcites →https://doi.org/10.1115/1.3656900openalex-snapshot
A Critical Analysis of Crack Propagation Laws
inferredcites →https://doi.org/10.1162/089976600300015015openalex-snapshot
Learning to Forget: Continual Prediction with LSTM
inferredcites →https://doi.org/10.1177/1464420715627379openalex-snapshot
Finite element simulation of dynamic brittle fracture in pipeline steel: A XFEM-based cohesive zone approach
inferredcites →https://doi.org/10.1179/imr.1992.37.1.45openalex-snapshot
Fatigue crack growth in metals and alloys: mechanisms and micromechanics
inferredcites →https://doi.org/10.1533/9780857092502openalex-snapshot
Fracture and fatigue of welded joints and structures
inferredcites →https://doi.org/10.3221/igf-esis.46.28openalex-snapshot
Fatigue crack propagation in welded joints X70
inferredcites →https://doi.org/10.3390/app14010398openalex-snapshot
Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
inferredcites →https://doi.org/10.3390/app14209297openalex-snapshot
Advances in Finite Element Modeling of Fatigue Crack Propagation
inferredcites →https://doi.org/10.3390/ma10050543openalex-snapshot
A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation
inferredcites →https://doi.org/10.3390/ma15176157openalex-snapshot
Statistical Assessment of Fracture Toughness Results from the HAZ of X80 Pipeline FCAW Girth Weld
inferredcites →https://doi.org/10.3390/ma15186198openalex-snapshot
Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys
inferredcites →https://doi.org/10.3390/met12010050openalex-snapshot
Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V
inferredcites →https://doi.org/10.3390/met13111860openalex-snapshot
Very High Cycle Fatigue of Welds: A Review
inferredcites →https://doi.org/10.4028/www.scientific.net/amr.535-537.674openalex-snapshot
Creep-Fatigue Interaction Life Prediction for P91 Steel Based on Improved Elman Neural Network Model
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.