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A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Development of data-driven prediction model for CFRP-steel bond strength by implementing ensemble learning algorithms
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Predicting the shear strength of reinforced concrete beams using artificial neural networks
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Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part I: beams without stirrups
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Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part II: beams with stirrups
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Effect of shear span-to-depth ratio on shear strength components of RC beams
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Prediction of Ultimate Shear Strength of Reinforced-Concrete Deep Beams Using Neural Networks
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Data Augmentation for Improving Deep Learning Models in Building Inspections or Postdisaster Evaluation
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Ensemble Transfer Learning Algorithm
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Defect Detection in Reinforced Concrete Using Random Neural Architectures
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Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
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Deep Transfer Learning for Image‐Based Structural Damage Recognition
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Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network
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Multifidelity approach for data‐driven prediction models of structural behaviors with limited data
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Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength
inferredcites →https://doi.org/10.12989/cac.2014.14.1.019openalex-snapshot
New strut-and-tie-models for shear strength prediction and design of RC deep beams
inferredcites →https://doi.org/10.14359/10416openalex-snapshot
The Modified Compression-Field Theory for Reinforced Concrete Elements Subjected to Shear
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Empirical Modeling of Reinforced Concrete Shear Strength Size Effect for Members without Stirrups
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Size Effect in Diagonal Shear Failure: Influence of Aggregate Size and Stirrups
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Shear Strength of Members without Transverse Reinforcement as Function of Critical Shear Crack Width
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Where is Shear Reinforcement Required? Review of Research Results and Design Procedures
inferredcites →https://doi.org/10.14359/51685839openalex-snapshot
ACI-DAfStb Database of Shear Tests on Slender Reinforced Concrete Beams without Stirrups
inferredcites →https://doi.org/10.14359/51686819openalex-snapshot
ACI-DAfStb Databases for Shear Tests on Slender Reinforced Concrete Beams with Stirrups
inferredcites →https://doi.org/10.14359/51686820openalex-snapshot
Database of Shear Tests for Non-slender Reinforced Concrete Beams without Stirrups
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Neural Network, Machine Learning, and Evolutionary Approaches for Concrete Material Characterization
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Novel Approach for Concrete Mixture Design Using Neural Dynamics Model and Virtual Lab Concept
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Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete
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Toward a Consistent Design of Structural Concrete
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Experimental Study on Shear Capacity of High Strength Reinforcement Concrete Deep Beams with Small Shear Span–Depth Ratio
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Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups
inferredcites →https://doi.org/10.4334/ijcsm.2007.1.1.063openalex-snapshot
Shear Capacity of Reinforced Concrete Beams Using Neural Network
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Adam: A Method for Stochastic Optimization
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Proposing an inherently interpretable machine learning model for shear strength prediction of reinforced concrete beams with stirrups
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Discussion on Pak, Hongrak, Leach, Samuel, Yoon, Seung Hyun, and Paal, Stephanie German (2023) “A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups.” Computer‐Aided Civil and Infrastructure Engineering, 38:11, 1520–1535
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Response to discussion on “A Knowledge Transfer Enhanced Ensemble Approach to Predict the Shear Capacity of Reinforced Concrete Deep Beams without Stirrups,” Computer‐Aided Civil and Infrastructure Engineering, 38:11, 1520–1535
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Machine learning-based prediction of shear and flexural capacity of reinforced concrete beams: a parallel modeling framework with SHAP interpretability and design code benchmarking
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