On the public citation graph

Machine Learning Approach to Predicating Bending Capacity of Ecc-Concrete Composite Beams Hybrid Reinforced with Steel and Frp Bars

https://doi.org/10.2139/ssrn.4812832
0 signed · 27 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.
Claim with ORCID
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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1006/jcss.1997.1504openalex-extracted
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
inferredcites →https://doi.org/10.1007/s00521-021-05836-8openalex-extracted
Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC
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.conbuildmat.2019.04.059openalex-extracted
Behaviors of hybrid beams composed of seawater sea-sand concrete (SWSSC) and a prefabricated UHPC shell reinforced with FRP bars
inferredcites →https://doi.org/10.1016/j.conbuildmat.2020.121117openalex-extracted
Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete
inferredcites →https://doi.org/10.1016/j.conbuildmat.2023.131640openalex-extracted
Prediction of shear strength of RC deep beams based on interpretable machine learning
inferredcites →https://doi.org/10.1016/j.cscm.2022.e01008openalex-extracted
Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites
inferredcites →https://doi.org/10.1016/j.cscm.2023.e02076openalex-extracted
Machine learning-based model for recognizing the failure modes of FRP-strengthened RC beams in flexure
inferredcites →https://doi.org/10.1016/j.cscm.2023.e02201openalex-extracted
Prediction of compressive strength of rice husk ash concrete: A comparison of different metaheuristic algorithms for optimizing support vector regression
inferredcites →https://doi.org/10.1016/j.engstruct.2014.01.001openalex-extracted
Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks
inferredcites →https://doi.org/10.1016/j.engstruct.2015.08.020openalex-extracted
Theoretical stress–strain model for circular concrete columns confined by GFRP spirals and hoops
inferredcites →https://doi.org/10.1016/j.engstruct.2020.111109openalex-extracted
Machine learning model for predicting structural response of RC slabs exposed to blast loading
inferredcites →https://doi.org/10.1016/j.engstruct.2022.113903openalex-extracted
Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
inferredcites →https://doi.org/10.1016/j.engstruct.2022.115156openalex-extracted
Prediction of FRP-concrete interfacial bond strength based on machine learning
inferredcites →https://doi.org/10.1016/j.engstruct.2023.115723openalex-extracted
Interpretable machine-learning models for maximum displacements of RC beams under impact loading predictions
inferredcites →https://doi.org/10.1016/j.eswa.2023.120649openalex-extracted
Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications
inferredcites →https://doi.org/10.1016/j.istruc.2018.12.012openalex-extracted
Maximum axial load carrying capacity of Fibre Reinforced-Polymer (FRP) bar reinforced concrete columns under axial compression
inferredcites →https://doi.org/10.1016/j.istruc.2023.105053openalex-extracted
Efficient prediction of the load-carrying capacity of ECC-strengthened RC beams – An extra-gradient boosting machine learning method
inferredcites →https://doi.org/10.1016/j.renene.2015.11.073openalex-extracted
Machine learning ensembles for wind power prediction
inferredcites →https://doi.org/10.1016/j.ress.2020.107284openalex-extracted
Fault diagnosis based on extremely randomized trees in wireless sensor networks
inferredcites →https://doi.org/10.1038/323533a0openalex-extracted
Learning representations by back-propagating errors
inferredcites →https://doi.org/10.1109/21.97458openalex-extracted
A survey of decision tree classifier methodology
inferredcites →https://doi.org/10.1109/5254.708428openalex-extracted
Support vector machines
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-extracted
XGBoost
inferredcites →https://doi.org/10.3390/app9194069openalex-extracted
Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms
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
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.