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References from Modeling high-temperature creep behavior of steel using long short-term memories and convolutional neural networks. Local targets link to admitted publications; unresolved targets remain external evidence.
Creep buckling of high strength Q460 steel columns at elevated temperatures
10.1016/j.jcsr.2019.03.003 · 2019 · External reference
Post-fire mechanical properties of high strength structural steels S460 and S690
10.1016/j.engstruct.2011.11.005 · 2012 · External reference
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Elevated-temperature mechanical properties of high strength structural steel S460N: experimental study and recommendations for fire-resistance design
10.1016/j.firesaf.2012.10.008 · 2013 · External reference
Experimental and numerical study on failure mechanism of steel-concrete composite bridge girders under fuel fire exposure
10.1016/j.engstruct.2021.113230 · 2021 · External reference
Effect of temperature on strength and elastic modulus of high-strength steel
10.1061/(asce)mt.1943-5533.0000600 · 2013 · External reference
Behavior of high strength steels under and after high temperature exposure: a review
2016 · External reference
Effect of grain size on mechanical and creep rupture properties of 253 MA austenitic stainless steel
10.3390/met12050820 · 2022 · External reference
A study of the creep behavior of modified 9Cr–1Mo steel using continuum-damage modeling
10.1016/j.ijplas.2012.04.004 · 2012 · External reference
Mechanical properties of hot-rolled and cold-formed steels after exposure to elevated temperature: a review
10.1016/j.conbuildmat.2019.04.062 · 2019 · External reference
Creep deformation and stress relaxation of a martensitic P92 steel at 650° C
10.1016/j.engfracmech.2017.02.008 · 2017 · External reference
Microstructural evolution of the TLP joints of RAFM steel during aging and creep
10.3390/met12081333 · 2022 · External reference
Unresolved reference
2003 · External reference
Unresolved reference
1929 · External reference
Creep of steel under simple and compound stress
1930 · External reference
Some fundamental experiments on high temperature creep
10.1016/0022-5096(55)90054-5 · 1955 · External reference
Primary creep of structural steel (SS 41) at high temperatures
1980 · External reference
Modeling creep of steel under transient temperature conditions of fire
10.1016/j.firesaf.2018.07.006 · 2018 · External reference
Unresolved reference
1967 · External reference
Behaviour of S 355JO steel subjected to uniaxial stress at lowered and elevated temperatures and creep
10.1007/s12034-010-0073-1 · 2010 · External reference
Creep behavior and model of high-strength steels over 500 MPa at elevated temperatures
10.1016/j.jcsr.2020.105989 · 2020 · External reference
Fracture toughness evaluation of ground granulated blast furnace slag concrete using experimental study and machine learning techniques
10.1016/j.engfracmech.2023.109577 · 2023 · External reference
The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction
2022 · External reference
Machine learning applications for building structural design and performance assessment: State-of-the-art review
2021 · External reference
Review on application of artificial intelligence in civil engineering
2019 · External reference
Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel
10.1016/j.commatsci.2008.01.039 · 2008 · External reference
Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy
10.1016/j.matdes.2012.03.002 · 2012 · External reference
Artificial neural network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping
10.1016/j.compstruc.2017.05.015 · 2017 · External reference
Neural network modelling studies of steam oxidised kinetic behaviour of advanced steels and Ni-based alloys at 800° C for 3000 h
10.1016/j.corsci.2018.01.013 · 2018 · External reference
Determination of high-temperature creep and post-creep response of structural steels
10.1016/j.jcsr.2022.107287 · 2022 · External reference
Experimental and neural networks analysis on elevated-temperature mechanical properties of structural steels
2022 · External reference
Are transformers effective for time series forecasting?
2023 · External reference
Machine-learning investigation on the creep-rupture time of heat-resistant steels
2023 · External reference
Creep life predictions by machine learning methods for ferritic heat resistant steels
10.2355/isijinternational.isijint-2023-266 · 2023 · External reference
Predicting creep life of CrMo pressure vessel steel using machine learning models with optimal feature subset selection
10.1016/j.ijpvp.2024.105349 · 2024 · External reference
Prediction of creep rupture life of ODS steels based on machine learning
2024 · External reference
Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy
10.1038/s41598-024-71431-1 · 2024 · External reference
A unified creep and fatigue life prediction approach for 316 austenitic stainless steel using machine and deep learning
10.1111/ffe.14379 · 2024 · External reference
Unresolved reference
2007 · External reference
Word embeddings and recurrent neural networks based on long-short term memory nodes in supervised biomedical word sense disambiguation
10.1016/j.jbi.2017.08.001 · 2017 · External reference
Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network
10.1016/j.physd.2019.132306 · 2020 · External reference
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A hybrid lightweight 1D CNN-LSTM architecture for automated ECG beat-wise classification
2021 · External reference
Convolutional RNN: an enhanced model for extracting features from sequential data
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CNN-LSTM vs. LSTM-CNN to predict power flow direction: a case study of the high-voltage subnet of Northeast Germany
10.3390/s23020901 · 2023 · External reference
10.1016/j.egyai.2021.100060 · 2021 · Admitted local publication
Deep learning for time series forecasting: a survey
10.1089/big.2020.0159 · 2021 · External reference
Automatic tuning of hyperparameters using Bayesian optimization
10.1007/s12530-020-09345-2 · 2021 · External reference
Building energy consumption raw data forecasting using data cleaning and deep recurrent neural networks
10.3390/buildings9090204 · 2019 · External reference
A short‐term load forecasting method based on GRU‐CNN hybrid neural network model
2020 · External reference
Long short-term memory
10.1162/neco.1997.9.8.1735 · 1997 · External reference
Short term forecasting based on hourly wind speed data using deep learning algorithms
2020 · External reference
Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market
10.1016/j.energy.2021.123060 · 2022 · External reference
A critical review of recurrent neural networks for sequence learning
2015 · External reference
Unresolved reference
2020 · External reference
Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm
10.1111/mice.12810 · 2022 · External reference
A deep LSTM network for the Spanish electricity consumption forecasting
10.1007/s00521-021-06773-2 · 2022 · External reference
ImageNet classification with deep convolutional neural networks
10.1145/3065386 · 2017 · External reference
Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling
10.1016/j.engstruct.2020.110704 · 2020 · External reference
A data-driven response virtual sensor technique with partial vibration measurements using convolutional neural network
10.3390/s17122888 · 2017 · External reference
Deep convolutional neural network for structural dynamic response estimation and system identification
2019 · External reference
Advances in neural networks and potential for their application to steel metallurgy
10.1080/02670836.2020.1839206 · 2020 · External reference
A combination of deep learning and genetic algorithm for predicting the compressive strength of high‐performance concrete
10.1002/suco.202100199 · 2022 · External reference
CNN‐LSTM model optimized by bayesian optimization for predicting single‐well production in water flooding reservoir
2023 · External reference
A novel framework based on cnn-lstm neural network for prediction of missing values in electricity consumption time-series datasets
2022 · External reference
A survey on hyperparameters optimization algorithms of forecasting models in smart grid
10.1016/j.scs.2020.102275 · 2020 · External reference
Learning search spaces for bayesian optimization: another view of hyperparameter transfer learning
2019 · External reference
Hyperparameter optimization for machine learning models based on Bayesian optimization
2019 · External reference
A comparative study of hyper-parameter optimization tools
2021 · External reference
Bayesian optimization with output-weighted optimal sampling
10.1016/j.jcp.2020.109901 · 2021 · External reference
A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting
10.1016/j.engappai.2020.103650 · 2020 · External reference
Performance prediction and Bayesian optimization of screw compressors using Gaussian process regression
10.1016/j.engappai.2024.108270 · 2024 · External reference
A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
2010 · External reference
Deep learning for load forecasting with smart meter data: Online adaptive recurrent neural network
10.1016/j.apenergy.2020.116177 · 2021 · External reference
DA-LSTM: a dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks
10.1016/j.engappai.2023.106480 · 2023 · External reference
A creep damage model for high-temperature deformation and failure of 9Cr-1Mo steel weldments
10.3390/met5031487 · 2015 · External reference
Studies on temperature induced creep in high strength Q460 steel
10.1617/s11527-016-0941-2 · 2017 · External reference
A numerical investigation of high-strength steel H-SA700 of protected beam with cavity under elevated temperature including creep behavior
2024 · External reference
Coherency loss marking the onset of degradation in high temperature creep of superalloys: phase-field simulation coupled to strain gradient crystal plasticity
10.1016/j.commatsci.2023.112069 · 2023 · External reference
Phase field analysis and simulation for remaining creep life assessment of H13 steel
10.1016/j.engfailanal.2018.07.010 · 2018 · External reference
Atomistic simulations of high-temperature creep in nanotwinned TiAl alloys
10.1016/j.eml.2021.101253 · 2021 · External reference
Structural evaluation and deformation features of interface of joint between nano-crystalline fe–ni–cr alloy and nano-crystalline Ni during creep process
10.1016/j.matdes.2016.06.086 · 2016 · External reference
The applicability of Norton's creep power law and its modified version to a single-crystal superalloy type CMSX-2
10.1016/0921-5093(96)10400-7 · 1996 · External reference
Unresolved reference
1995 · External reference
The θ projection concept—A model-based approach to design and life extension of engineering plant
10.1016/0308-0161(92)90035-e · 1992 · External reference
Rupture time under creep conditions
10.1023/a:1018671022008 · 1999 · External reference
Unresolved reference
2015 · External reference
Continuum damage mechanics and local approach to fracture: numerical procedures
10.1016/0045-7825(91)90236-y · 1991 · External reference
Creep life prediction for P91/12Cr1MoV dissimilar joint based on the omega method
10.1016/j.proeng.2015.12.281 · 2015 · External reference
Estimation of Norton-Bailey parameters from creep rupture data
10.1179/030634579790434312 · 1979 · External reference
A novel sin-hyperbolic creep damage model to overcome the mesh dependency of classic local approach Kachanov-Rabotnov model
2015 · External reference
Application of the Theta projection method to creep modelling using Abaqus
2007 · External reference
Assessment of thermal properties of various types of high-strength steels at elevated temperatures
10.1016/j.firesaf.2021.103348 · 2021 · External reference
Heating rate and boundary restraint effects on fire resistance of steel columns with creep
10.1016/j.engstruct.2005.10.009 · 2006 · External reference
High-throughput map design of creep life in low-alloy steels by integrating machine learning with a genetic algorithm
10.1016/j.matdes.2021.110326 · 2022 · External reference
Machine-learning investigation on the creep-rupture time of heat-resistant steels
2023 · External reference
Machine learning-based framework for predicting creep rupture life of modified 9cr-1mo steel
10.3390/app13084972 · 2023 · External reference
Composition, heat treatment, microstructure and loading condition based machine learning prediction of creep life of superalloys
10.1016/j.mechmat.2023.104819 · 2023 · External reference
Deterioration of mechanical properties of high strength structural steel S460N under steady state fire condition
10.1016/j.matdes.2011.11.069 · 2012 · External reference
