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Genetic Evolutionary Deep Learning for Fire Resistance Analysis in Frp-Strengthened Rc Beams

https://doi.org/10.2139/ssrn.4819234
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Mechanical Characterization of Fibre Reinforced Polymers Materials at High Temperature
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Software review: DEAP (Distributed Evolutionary Algorithm in Python) library
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Reinforcement bond performance in 3D concrete printing: Explainable ensemble learning augmented by deep generative adversarial networks
inferredcites →https://doi.org/10.1016/j.cemconcomp.2021.104378openalex-extracted
Machine learning prediction of structural response of steel fiber-reinforced concrete beams subjected to far-field blast loading
inferredcites →https://doi.org/10.1016/j.compositesb.2010.11.004openalex-extracted
Effect of bond degradation on fire resistance of FRP-strengthened reinforced concrete beams
inferredcites →https://doi.org/10.1016/j.compositesb.2015.05.045openalex-extracted
Fire behaviour of FRP-strengthened reinforced concrete structural elements: A state-of-the-art review
inferredcites →https://doi.org/10.1016/j.compositesb.2021.108894openalex-extracted
A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods: Deep neuron networks and genetic programming
inferredcites →https://doi.org/10.1016/j.compositesb.2021.109471openalex-extracted
Fire behaviour of CFRP-strengthened RC slabs using different techniques – EBR, NSM and CREatE
inferredcites →https://doi.org/10.1016/j.compositesb.2022.110432openalex-extracted
A genetic evolved machine learning approach for 3D DEM modelling of anisotropic materials with full consideration of particulate interactions
inferredcites →https://doi.org/10.1016/j.compstruct.2014.11.063openalex-extracted
Fire behaviour of thermally insulated RC beams strengthened with EBR-CFRP strips: Experimental study
inferredcites →https://doi.org/10.1016/j.compstruct.2017.12.051openalex-extracted
A numerical approach for modeling response of fiber reinforced polymer strengthened concrete slabs exposed to fire
inferredcites →https://doi.org/10.1016/j.compstruct.2022.115381openalex-extracted
Shear capacity prediction of FRP-RC beams using single and ensenble ExPlainable Machine learning models
inferredcites →https://doi.org/10.1016/j.compstruct.2023.117639openalex-extracted
An interpretable machine learning model for predicting bond strength of CFRP-steel epoxy-bonded interface
inferredcites →https://doi.org/10.1016/j.compstruct.2024.118045openalex-extracted
Strength models of near-surface mounted (NSM) fibre-reinforced polymer (FRP) shear-strengthened RC beams based on machine learning approaches
inferredcites →https://doi.org/10.1016/j.conbuildmat.2016.09.031openalex-extracted
Long term performance and fire safety aspect of FRP composites used in building structures
inferredcites →https://doi.org/10.1016/j.conbuildmat.2023.133553openalex-extracted
Shear strength prediction of FRP-strengthened concrete beams using interpretable machine learning
inferredcites →https://doi.org/10.1016/j.conbuildmat.2024.135083openalex-extracted
Modelling nonlinear shear creep behaviour of a structural adhesive using deep neural networks (DNN)
inferredcites →https://doi.org/10.1016/j.dib.2024.110031openalex-extracted
Dataset on fire resistance analysis of FRP-strengthened concrete beams
inferredcites →https://doi.org/10.1016/j.engappai.2023.107796openalex-extracted
Evaluating the effect of curing conditions on the glass transition of the structural adhesive using conditional tabular generative adversarial networks
inferredcites →https://doi.org/10.1016/j.engfracmech.2024.109962openalex-extracted
Data-driven estimates of the strength and failure modes of CFRP-steel bonded joints by implementing the CTGAN method
inferredcites →https://doi.org/10.1016/j.engstruct.2020.111058openalex-extracted
Structural behaviour of FRP reinforced concrete slabs in fire
inferredcites →https://doi.org/10.1016/j.engstruct.2021.112219openalex-extracted
Prediction of temperature variation in FRP-wrapped RC columns exposed to fire using artificial neural networks
inferredcites →https://doi.org/10.1016/j.engstruct.2021.112764openalex-extracted
Post-fire analysis and numerical modeling of a fire-damaged concrete bridge
inferredcites →https://doi.org/10.1016/j.ijsolstr.2021.111078openalex-extracted
Comparison of GA and topology optimization of adherend for adhesively bonded metal composite joints
inferredcites →https://doi.org/10.1016/j.istruc.2022.02.003openalex-extracted
Machine learning for structural engineering: A state-of-the-art review
inferredcites →https://doi.org/10.1016/j.istruc.2023.104887openalex-extracted
Fire endurance tests of CFRP-strengthened RC beams with different insulation schemes
inferredcites →https://doi.org/10.1016/j.jobe.2022.104315openalex-extracted
Experimental behavior of fire-exposed reinforced concrete slabs without and with FRP retrofitting
inferredcites →https://doi.org/10.1016/j.tws.2021.107499openalex-extracted
The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network
inferredcites →https://doi.org/10.1016/j.tws.2022.109985openalex-extracted
A novel genetic expression programming assisted calibration strategy for discrete element models of composite joints with ductile adhesives
inferredcites →https://doi.org/10.1016/s0141-0296(01)00106-7openalex-extracted
FRP-strengthened RC beams. II: assessment of debonding strength models
inferredcites →https://doi.org/10.1061/(asce)cc.1943-5614.0000509openalex-extracted
Finite Element Modeling of Insulated FRP-Strengthened RC Beams Exposed to Fire
inferredcites →https://doi.org/10.1061/(asce)cc.1943-5614.0000840openalex-extracted
Three-Level Fire Resistance Design of FRP-Strengthened RC Beams
inferredcites →https://doi.org/10.1080/00218464.2023.2183851openalex-extracted
Machine learning approach for analysing and predicting the modulus response of the structural epoxy adhesive at elevated temperatures
inferredcites →https://doi.org/10.1109/mci.2012.2228600openalex-extracted
Ensemble Methods: Foundations and Algorithms [Book Review]
inferredcites →https://doi.org/10.1109/tevc.2022.3225509openalex-extracted
Explainable Artificial Intelligence by Genetic Programming: A Survey
inferredcites →https://doi.org/10.1139/cjce-2012-0506openalex-extracted
Design for fire of concrete elements strengthened or reinforced with fibre-reinforced polymer: state of the art and opportunities from performance-based approaches
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-extracted
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-extracted
A Unified Approach to Interpreting Model Predictions
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