Research graph
References from CDM-based constitutive model incorporating strength and stiffness degradation for ULCF prediction of weld metal. Local targets link to admitted publications; unresolved targets remain external evidence.
Extremely low cycle fatigue tests on structural carbon steel and stainless steel
10.1016/j.jcsr.2009.08.004 · 2010 · External reference
Extremely low-cycle fatigue tests of thick-walled steel bridge piers
10.1061/(asce)be.1943-5592.0000429 · 2013 · External reference
Unresolved reference
2000 · External reference
Performance criteria for MR steel frames in seismic zones
10.1016/s0143-974x(03)00140-8 · 2004 · External reference
Deformation maps for bolted T-stubs
10.1061/(asce)st.1943-541x.0002584 · 2020 · External reference
Behavior of T-stub steel connections bolted to rigid bases
10.1016/j.jcsr.2022.107242 · 2022 · External reference
Ultra low cycle fatigue behavior of Q690 high-strength steel welded T-stub joints: experiments and fracture prediction analysis
10.1016/j.tws.2023.111054 · 2023 · External reference
Seismic performance of compact beam–column connections with welding defects in steel bridge piers
10.1061/(asce)be.1943-5592.0001024 · 2017 · External reference
Analysis on failure mode of Q345qC steel bridge piers with unstiffened box-section
2026 · External reference
Ultra-low cycle fatigue fracture initiation life evaluation of thick-walled steel bridge piers with microscopic damage index under bidirectional cyclic loading
2022 · External reference
Ductile crack initiation and propagation in steel bridge piers subjected to random cyclic loading
10.1016/j.engstruct.2013.12.006 · 2014 · External reference
Experimental study on seismic performance of partial penetration welded steel beam–column connections with different fillet radii
10.12989/scs.2014.17.6.851 · 2014 · External reference
Extremely low cycle fatigue life prediction based on a new cumulative fatigue damage model
10.1016/s0142-1123(01)00170-0 · 2002 · External reference
A prediction model for extremely low cycle fatigue strength of structural steel
10.1016/j.ijfatigue.2006.08.001 · 2007 · External reference
A modified Coffin-Manson model for ultra-low cycle fatigue fracture of structural steels considering the effect of stress triaxiality
10.1016/j.engfracmech.2020.107223 · 2020 · External reference
Cyclic void growth model to assess ductile fracture initiation in structural steels due to ultra low cycle fatigue
2007 · External reference
A micromechanical cyclic void growth model for ultra-low cycle fatigue
10.1016/j.ijfatigue.2014.08.010 · 2015 · External reference
Study on ultra-low cycle fatigue behavior of austenitic stainless steel
10.1016/j.tws.2019.106205 · 2019 · External reference
A stress-weighted ductile fracture model for steel subjected to ultra low cycle fatigue
10.1016/j.engstruct.2021.112964 · 2021 · External reference
Experimental and numerical investigations on extremely-low-cycle fatigue fracture behavior of steel welded joints
10.1016/j.jcsr.2015.12.015 · 2016 · External reference
A model for ultra low cycle fatigue damage prediction of structural steel
10.1016/j.jcsr.2021.106956 · 2021 · External reference
Continuous damage model for structural steels and weld metals under ultra-low-cyclic loading
10.1016/j.jcsr.2024.108562 · 2024 · External reference
A CDM-based constitutive model for structural steel considering ULCF damage accumulation
10.1016/j.jcsr.2025.109646 · 2025 · External reference
On the ductile enlargement of voids in triaxial stress fields∗
10.1016/0022-5096(69)90033-7 · 1969 · External reference
The void growth model and the stress modified critical strain model to predict ductile fracture in structural steels
10.1061/(asce)0733-9445(2006)132:12(1907) · 2006 · External reference
Very low-cycle fatigue failure behaviours of pipe elbows under displacement-controlled cyclic loading
10.1016/j.tws.2023.111261 · 2023 · External reference
A simplified prediction method on Chaboche isotropic/kinematic hardening model parameters of structural steels
2023 · External reference
A revised Chaboche model from multiscale approach to predict the cyclic behavior of type 316 stainless steel at room temperature
10.1016/j.ijfatigue.2022.107303 · 2023 · External reference
A method to calculate seismic behavior of a corroded beam-to-column joint considering distribution randomness of corrosion depth
10.1016/j.jcsr.2023.108267 · 2024 · External reference
A two-surface model for steels with yield plateau
10.2208/jscej.1992.11 · 1992 · External reference
Piecewise linear approximation of nonlinear unloading-reloading behaviors using a multi-surface approach
10.1016/j.ijplas.2017.02.004 · 2017 · External reference
Prediction of fracture behavior of beam-to-column welded joints using micromechanics damage model
10.1016/j.jcsr.2013.02.014 · 2013 · External reference
A cyclic GTN model for ultra-low cycle fatigue analysis of structural steels
10.1016/j.ijfatigue.2023.107946 · 2023 · External reference
The cyclic GTN model for ULCF fracture analysis of Q355 steel
10.1016/j.engfracmech.2024.110248 · 2024 · External reference
Machine learning for structural engineering: a state-of-the-art review
2022 · External reference
Machine-learning-assisted design of high strength steel I-section columns
10.1016/j.engstruct.2024.118018 · 2024 · External reference
Unresolved reference
1968 · External reference
Experimental and numerical characterization of ultralow-cycle fatigue behavior of steel castings
10.1061/(asce)st.1943-541x.0002497 · 2020 · External reference
Fracture prediction of Fe-SMA under monotonic and low cycle fatigue loading
10.1016/j.ijfatigue.2023.107794 · 2023 · External reference
Unresolved reference
2015 · External reference
Unresolved reference
2018 · External reference
Unresolved reference
2004 · External reference
Unresolved reference
2020 · External reference
Xgboost: a scalable tree boosting system
2016 · External reference
Unresolved reference
2012 · External reference
Wind pressure data reconstruction using neural network techniques: a comparison between BPNN and GRNN
10.1016/j.measurement.2016.04.049 · 2016 · External reference
Multiaxial fatigue life prediction method based on the back-propagation neural network
10.1016/j.ijfatigue.2022.107274 · 2023 · External reference
Creep–fatigue life prediction of a titanium alloy deep-sea submersible using a continuum damage mechanics-informed BP neural network model
10.1016/j.oceaneng.2024.118826 · 2024 · External reference
Artificial intelligence in physical sciences: symbolic regression trends and perspectives: D. Angelis et al
10.1007/s11831-023-09922-z · 2023 · External reference
Interpretable scientific discovery with symbolic regression: a review
10.1007/s10462-023-10622-0 · 2024 · External reference
Symbolic regression as a feature engineering method for machine and deep learning regression tasks
2024 · External reference
Fatigue life prediction of composite materials using polynomial classifiers and recurrent neural networks
10.1016/j.compstruct.2005.08.012 · 2007 · External reference
Alternative assessment of machine learning to polynomial regression in response surface methodology for predicting decolorization efficiency in textile wastewater treatment
10.1016/j.chemosphere.2024.143996 · 2025 · External reference
Approximation theory of the MLP model in neural networks
10.1017/s0962492900002919 · 1999 · External reference
Residual MLP network for mental fatigue classification in mining workers from brain data
2019 · External reference
Probabilistic fatigue life prediction using multi-layer perceptron with maximum entropy algorithm
10.1016/j.ijfatigue.2024.108445 · 2024 · External reference
Minimum sample size recommendations for conducting factor analyses
10.1207/s15327574ijt0502_4 · 2005 · External reference
Interpretable scientific discovery with symbolic regression: a review
10.1007/s10462-023-10622-0 · ExternalCitation · doi-reference
Artificial intelligence in physical sciences: symbolic regression trends and perspectives: D. Angelis et al
10.1007/s11831-023-09922-z · ExternalCitation · doi-reference
On the ductile enlargement of voids in triaxial stress fields∗
10.1016/0022-5096(69)90033-7 · ExternalCitation · doi-reference
Alternative assessment of machine learning to polynomial regression in response surface methodology for predicting decolorization efficiency in textile wastewater treatment
10.1016/j.chemosphere.2024.143996 · ExternalCitation · doi-reference
Fatigue life prediction of composite materials using polynomial classifiers and recurrent neural networks
10.1016/j.compstruct.2005.08.012 · ExternalCitation · doi-reference
A modified Coffin-Manson model for ultra-low cycle fatigue fracture of structural steels considering the effect of stress triaxiality
10.1016/j.engfracmech.2020.107223 · ExternalCitation · doi-reference
The cyclic GTN model for ULCF fracture analysis of Q355 steel
10.1016/j.engfracmech.2024.110248 · ExternalCitation · doi-reference
Ductile crack initiation and propagation in steel bridge piers subjected to random cyclic loading
10.1016/j.engstruct.2013.12.006 · ExternalCitation · doi-reference
A stress-weighted ductile fracture model for steel subjected to ultra low cycle fatigue
10.1016/j.engstruct.2021.112964 · ExternalCitation · doi-reference
Machine-learning-assisted design of high strength steel I-section columns
10.1016/j.engstruct.2024.118018 · ExternalCitation · doi-reference
A prediction model for extremely low cycle fatigue strength of structural steel
10.1016/j.ijfatigue.2006.08.001 · ExternalCitation · doi-reference
A micromechanical cyclic void growth model for ultra-low cycle fatigue
10.1016/j.ijfatigue.2014.08.010 · ExternalCitation · doi-reference
Multiaxial fatigue life prediction method based on the back-propagation neural network
10.1016/j.ijfatigue.2022.107274 · ExternalCitation · doi-reference
A revised Chaboche model from multiscale approach to predict the cyclic behavior of type 316 stainless steel at room temperature
10.1016/j.ijfatigue.2022.107303 · ExternalCitation · doi-reference
Fracture prediction of Fe-SMA under monotonic and low cycle fatigue loading
10.1016/j.ijfatigue.2023.107794 · ExternalCitation · doi-reference
A cyclic GTN model for ultra-low cycle fatigue analysis of structural steels
10.1016/j.ijfatigue.2023.107946 · ExternalCitation · doi-reference
Probabilistic fatigue life prediction using multi-layer perceptron with maximum entropy algorithm
10.1016/j.ijfatigue.2024.108445 · ExternalCitation · doi-reference
Piecewise linear approximation of nonlinear unloading-reloading behaviors using a multi-surface approach
10.1016/j.ijplas.2017.02.004 · ExternalCitation · doi-reference
Extremely low cycle fatigue tests on structural carbon steel and stainless steel
10.1016/j.jcsr.2009.08.004 · ExternalCitation · doi-reference
Prediction of fracture behavior of beam-to-column welded joints using micromechanics damage model
10.1016/j.jcsr.2013.02.014 · ExternalCitation · doi-reference
Experimental and numerical investigations on extremely-low-cycle fatigue fracture behavior of steel welded joints
10.1016/j.jcsr.2015.12.015 · ExternalCitation · doi-reference
A model for ultra low cycle fatigue damage prediction of structural steel
10.1016/j.jcsr.2021.106956 · ExternalCitation · doi-reference
Behavior of T-stub steel connections bolted to rigid bases
10.1016/j.jcsr.2022.107242 · ExternalCitation · doi-reference
A method to calculate seismic behavior of a corroded beam-to-column joint considering distribution randomness of corrosion depth
10.1016/j.jcsr.2023.108267 · ExternalCitation · doi-reference
Continuous damage model for structural steels and weld metals under ultra-low-cyclic loading
10.1016/j.jcsr.2024.108562 · ExternalCitation · doi-reference
A CDM-based constitutive model for structural steel considering ULCF damage accumulation
10.1016/j.jcsr.2025.109646 · ExternalCitation · doi-reference
Wind pressure data reconstruction using neural network techniques: a comparison between BPNN and GRNN
10.1016/j.measurement.2016.04.049 · ExternalCitation · doi-reference
Creep–fatigue life prediction of a titanium alloy deep-sea submersible using a continuum damage mechanics-informed BP neural network model
10.1016/j.oceaneng.2024.118826 · ExternalCitation · doi-reference
Study on ultra-low cycle fatigue behavior of austenitic stainless steel
10.1016/j.tws.2019.106205 · ExternalCitation · doi-reference
Ultra low cycle fatigue behavior of Q690 high-strength steel welded T-stub joints: experiments and fracture prediction analysis
10.1016/j.tws.2023.111054 · ExternalCitation · doi-reference
Very low-cycle fatigue failure behaviours of pipe elbows under displacement-controlled cyclic loading
10.1016/j.tws.2023.111261 · ExternalCitation · doi-reference
Extremely low cycle fatigue life prediction based on a new cumulative fatigue damage model
10.1016/s0142-1123(01)00170-0 · ExternalCitation · doi-reference
Performance criteria for MR steel frames in seismic zones
10.1016/s0143-974x(03)00140-8 · ExternalCitation · doi-reference
Approximation theory of the MLP model in neural networks
10.1017/s0962492900002919 · ExternalCitation · doi-reference
The void growth model and the stress modified critical strain model to predict ductile fracture in structural steels
10.1061/(asce)0733-9445(2006)132:12(1907) · ExternalCitation · doi-reference
Extremely low-cycle fatigue tests of thick-walled steel bridge piers
10.1061/(asce)be.1943-5592.0000429 · ExternalCitation · doi-reference
Seismic performance of compact beam–column connections with welding defects in steel bridge piers
10.1061/(asce)be.1943-5592.0001024 · ExternalCitation · doi-reference
Experimental and numerical characterization of ultralow-cycle fatigue behavior of steel castings
10.1061/(asce)st.1943-541x.0002497 · ExternalCitation · doi-reference
Deformation maps for bolted T-stubs
10.1061/(asce)st.1943-541x.0002584 · ExternalCitation · doi-reference
Minimum sample size recommendations for conducting factor analyses
10.1207/s15327574ijt0502_4 · ExternalCitation · doi-reference
Experimental study on seismic performance of partial penetration welded steel beam–column connections with different fillet radii
10.12989/scs.2014.17.6.851 · ExternalCitation · doi-reference
A two-surface model for steels with yield plateau
10.2208/jscej.1992.11 · ExternalCitation · doi-reference
