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Strength models of near-surface mounted (NSM) fibre-reinforced polymer (FRP) shear-strengthened RC beams based on machine learning approaches

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

inferredcites →https://doi.org/10.1007/s00170-011-3675-xopenalex-snapshot
Bayesian regularization-based Levenberg–Marquardt neural model combined with BFOA for improving surface finish of FDM processed part
inferredcites →https://doi.org/10.1007/s00500-019-04103-2openalex-snapshot
Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models
inferredcites →https://doi.org/10.1007/s11042-020-10139-6openalex-snapshot
A review on genetic algorithm: past, present, and future
inferredcites →https://doi.org/10.1016/j.cemconcomp.2005.11.003openalex-snapshot
Near surface mounted CFRP laminates for shear strengthening of concrete beams
inferredcites →https://doi.org/10.1016/j.compositesb.2011.09.006openalex-snapshot
NSM shear strengthening technique with CFRP laminates applied in high-strength concrete beams with or without pre-cracking
inferredcites →https://doi.org/10.1016/j.compositesb.2013.09.001openalex-snapshot
Design formula to evaluate the NSM FRP strips shear strength contribution to a RC beam
inferredcites →https://doi.org/10.1016/j.compositesb.2014.05.001openalex-snapshot
Application of artificial intelligence techniques to predict the performance of RC beams shear strengthened with NSM FRP rods. Formulation of design equations
inferredcites →https://doi.org/10.1016/j.compositesb.2017.01.028openalex-snapshot
NSM shear strengthening technique with CFRP laminates applied in high T cross section RC beams
inferredcites →https://doi.org/10.1016/j.compositesb.2017.07.072openalex-snapshot
Reinforced concrete beams strengthened in flexure with near-surface mounted (NSM) CFRP strips: Current status and research needs
inferredcites →https://doi.org/10.1016/j.compstruct.2009.10.027openalex-snapshot
Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
inferredcites →https://doi.org/10.1016/j.compstruct.2012.09.026openalex-snapshot
Shear strengthening of RC beams with NSM CFRP laminates: Experimental research and analytical formulation
inferredcites →https://doi.org/10.1016/j.compstruct.2019.111698openalex-snapshot
Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques
inferredcites →https://doi.org/10.1016/j.compstruct.2021.113618openalex-snapshot
A predictive model for the bond strength of near-surface-mounted FRP bonded to concrete
inferredcites →https://doi.org/10.1016/j.conbuildmat.2008.03.009openalex-snapshot
Modeling of debonding failure for RC beams strengthened in shear with NSM FRP reinforcement
inferredcites →https://doi.org/10.1016/j.conbuildmat.2023.131361openalex-snapshot
Development of Bayesian regularized artificial neural network for airborne chlorides estimation
inferredcites →https://doi.org/10.1016/j.engstruct.2008.11.016openalex-snapshot
Effective methods of using CFRP bars in shear strengthening of concrete girders
inferredcites →https://doi.org/10.1016/j.engstruct.2010.09.017openalex-snapshot
Tests on reinforced concrete beams strengthened in shear using near surface mounted CFRP and steel bars
inferredcites →https://doi.org/10.1016/j.engstruct.2013.05.032openalex-snapshot
Bond–slip model for CFRP strips near-surface mounted to concrete
inferredcites →https://doi.org/10.1016/j.engstruct.2018.10.065openalex-snapshot
Recurrent neural network model with Bayesian training and mutual information for response prediction of large buildings
inferredcites →https://doi.org/10.1016/j.engstruct.2020.110412openalex-snapshot
Shear strengthening of reinforced concrete deep beams using near-surface mounted hybrid carbon/glass fibre reinforced polymer strips
inferredcites →https://doi.org/10.1016/j.eswa.2013.04.013openalex-snapshot
A Bayesian regularized artificial neural network for stock market forecasting
inferredcites →https://doi.org/10.1016/j.istruc.2022.07.025openalex-snapshot
Data-driven prediction of FRP strengthened reinforced concrete beam capacity based on interpretable ensemble learning algorithms
inferredcites →https://doi.org/10.1016/j.knosys.2013.11.015openalex-snapshot
Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting
inferredcites →https://doi.org/10.1061/(asce)cc.1943-5614.0000402openalex-snapshot
Bond Strength Model for CFRP Strips Near-Surface Mounted to Concrete
inferredcites →https://doi.org/10.1061/(asce)cc.1943-5614.0000612openalex-snapshot
Investigation of Near Surface–Mounted Method for Shear Rehabilitation of Reinforced Concrete Beams Using Fiber Reinforced–Polymer Composites
inferredcites →https://doi.org/10.1080/13287982.2019.1565617openalex-snapshot
Shear strengthening of RC beams using near-surface mounted carbon fibre-reinforced polymers
inferredcites →https://doi.org/10.1108/jedt-01-2020-0022openalex-snapshot
Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network
inferredcites →https://doi.org/10.1177/07316844211068116openalex-snapshot
Prediction of compressive strength of fiber-reinforced polymers-confined cylindrical concrete using artificial intelligence methods
inferredcites →https://doi.org/10.1177/13694332221125830openalex-snapshot
Shear strengthening of RC beams with NSM FRP. II: Assessment of strength models
inferredcites →https://doi.org/10.1177/13694332221125832openalex-snapshot
Shear strengthening of RC beams with NSM FRP. I: Review of strength models
inferredcites →https://doi.org/10.1260/1369433042863198openalex-snapshot
Strengthening of RC Members Using Near-Surface Mounted FRP Composites: Design Overview
inferredcites →https://doi.org/10.14359/51689029openalex-snapshot
New Shear Strengthening Configurations of Near-Surface-Mounted CFRP Laminates for RC Beams
inferredcites →https://doi.org/10.1504/ijsmss.2012.050453openalex-snapshot
Performance of NSM-FRP RC beams in flexure and shear using locally developed CFRP rebars
inferredcites →https://doi.org/10.3390/buildings11110563openalex-snapshot
Influence of Compressive Strength of Concrete on Shear Strengthening of Reinforced Concrete Beams with Near Surface Mounted Carbon Fiber-Reinforced Polymer
inferredcites →https://doi.org/10.3390/polym14091824openalex-snapshot
A Machine Learning Model for Torsion Strength of Externally Bonded FRP-Reinforced Concrete Beams
inferredcites →https://doi.org/10.56748/ejse.19238openalex-snapshot
Shear Strengthening of RC Beams Using NSM GFRP Bars or CFRP U-Wrap Sheets
inferredcites →https://doi.org/10.5860/choice.27-0936openalex-snapshot
Genetic algorithms in search, optimization, and machine learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4819234openalex-extracted
Genetic Evolutionary Deep Learning for Fire Resistance Analysis in Frp-Strengthened Rc Beams
inferred← citeshttps://doi.org/10.1007/s10570-026-07170-5openalex-snapshot
A review on environmental durability and service life prediction of natural fiber composites: mechanisms, strategies, and models
inferred← citeshttps://doi.org/10.1007/s42107-025-01490-wopenalex-snapshot
Performance evaluation and deep learning-based prediction of CFRP-strengthened RC beams with core-cut openings
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Recycling of copper tailing as filler material in asphalt paving mastic: A sustainable solution for mining waste recovery
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112804openalex-snapshot
A metaheuristic-driven categorical boosting framework with interpretability for high-precision prediction of mechanical properties in corroded reinforced concrete beams
inferred← citeshttps://doi.org/10.1016/j.engfailanal.2024.109149openalex-snapshot
Genetic evolutionary deep learning for fire resistance analysis in fibre-reinforced polymers strengthened reinforced concrete beams
inferred← citeshttps://doi.org/10.1016/j.engstruct.2024.119485openalex-snapshot
Data-driven shear strength prediction of RC beams strengthened with FRCM jackets using machine learning approach
inferred← citeshttps://doi.org/10.1016/j.engstruct.2025.120105openalex-snapshot
Effect of shear span-to-effective depth ratio and FRP material type on the behaviour of RC T-beams strengthened in shear with embedded FRP bars
inferred← citeshttps://doi.org/10.1016/j.istruc.2024.107779openalex-snapshot
Monopole communication towers strengthened by fiber-reinforced polymer: Experiment, theoretical analysis and implementation
inferred← citeshttps://doi.org/10.1016/j.istruc.2025.108223openalex-snapshot
Failure mode improvement and flexural performance of RC beams strengthened with prestressed NSM-CFRP bars anchored with additional aluminium alloy ribs
inferred← citeshttps://doi.org/10.1016/j.istruc.2025.109027openalex-snapshot
Evaluating neural network models for load-bearing capacity of RCFST columns
inferred← citeshttps://doi.org/10.1016/j.istruc.2025.110211openalex-snapshot
Experimental study on two-span continuous RC beams reinforced with novel ductile FRP-UHPC hybrid rebars
inferred← citeshttps://doi.org/10.1016/j.istruc.2025.110305openalex-snapshot
Enhancing load-displacement response predictions for concrete beams strengthened with prestressed near-surface mounted FRPs via transfer learning
inferred← citeshttps://doi.org/10.1016/j.istruc.2026.112444openalex-snapshot
Compressive behavior and stress-strain model of novel FRP-UHPC hybrid rebars
inferred← citeshttps://doi.org/10.1016/j.jobe.2025.112472openalex-snapshot
Interpretation and analysis of scattering in steel fiber alignment in concrete: The H alpha decomposition method using fully polarized B-scan ground penetrating radar (GPR) image data
inferred← citeshttps://doi.org/10.1016/j.jobe.2025.114482openalex-snapshot
Machine learning-based prediction of long-term durability of FRP and FRP-reinforced concrete: A state-of-the-art review
inferred← citeshttps://doi.org/10.1016/j.tws.2026.115490openalex-snapshot
Mechanics-Informed Machine Learning for FRP Composites: A Comprehensive Review and Outlook
inferred← citeshttps://doi.org/10.1016/j.ymssp.2026.114033openalex-snapshot
Hybrid generative-probabilistic machine learning approach for predicting residual tensile strength and elastic modulus of FRP coupons under environmental exposure
inferred← citeshttps://doi.org/10.1061/jccof2.cceng-5161openalex-snapshot
Unbiased Model of the Contribution of NSM CFRP Systems to Shear Resistance in RC Beams Utilizing Multiobjective Optimization
inferred← citeshttps://doi.org/10.1155/adce/8815587openalex-snapshot
Numerical and Finite Element Analysis of RC Beams Strengthened With a Novel Technique Using External Plates and Rebars
inferred← citeshttps://doi.org/10.1177/13694332261451892openalex-snapshot
Prediction of axial performance of FRP-confined concrete based on data-driven models: A novel transfer learning approach
inferred← citeshttps://doi.org/10.32792/utq/utjes.16.1.706openalex-snapshot
Shear Strengthening of RC Beams Using NSM Technique and Different Types of Bar Materials and Geopolymer Adhesive
inferred← citeshttps://doi.org/10.3390/buildings14113592openalex-snapshot
Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars
inferred← citeshttps://doi.org/10.3390/buildings15111908openalex-snapshot
Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning
inferred← citeshttps://doi.org/10.3390/ma19102019openalex-snapshot
Flexural Behavior of Reinforced Concrete Two-Way Slabs Strengthened with Basalt Fiber-Reinforced Polymer Grid and Engineered Cementitious Composite
inferred← citeshttps://doi.org/10.66632/eei-2026-26openalex-snapshot
The flexural behavior of RC beams strengthened by near-surface mounted steel or FRP bars is investigated experimentally and numerically
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