On the public citation graph

A Heterogeneous Transfer Learning-Based Genetic Expression Programming Approach to Estimate Reinforced Concrete Beam Shear Strength

0 signed · 33 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-1-4471-5013-8_11openalex-extracted
Introduction to Evolutionary Algorithms
inferredcites →https://doi.org/10.1007/bf00175355openalex-extracted
Genetic programming as a means for programming computers by natural selection
inferredcites →https://doi.org/10.1007/bfb0055923openalex-extracted
Genetic Programming
inferredcites →https://doi.org/10.1007/s10462-020-09894-7openalex-extracted
Genetic programming in civil engineering: advent, applications and future trends
inferredcites →https://doi.org/10.1016/j.autcon.2014.02.007openalex-extracted
Formulation of shear strength of slender RC beams using gene expression programming, part I: Without shear reinforcement
inferredcites →https://doi.org/10.1016/j.compstruc.2015.09.005openalex-extracted
Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming
inferredcites →https://doi.org/10.1016/j.dibe.2021.100045openalex-extracted
Machine learning in construction: From shallow to deep learning
inferredcites →https://doi.org/10.1016/j.jobe.2020.102118openalex-extracted
A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams
inferredcites →https://doi.org/10.1016/j.jobe.2021.102300openalex-extracted
Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement
inferredcites →https://doi.org/10.1016/j.jobe.2022.104145openalex-extracted
Prediction of failure modes, strength, and deformation capacity of RC shear walls through machine learning
inferredcites →https://doi.org/10.1016/j.jobe.2022.105046openalex-extracted
Predicting nominal shear capacity of reinforced concrete wall in building by metaheuristics-optimized machine learning
inferredcites →https://doi.org/10.1016/j.jobe.2023.106257openalex-extracted
Symbolic machine learning improved MCFT model for punching shear resistance of FRP-reinforced concrete slabs
inferredcites →https://doi.org/10.1016/j.measurement.2016.10.024openalex-extracted
Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement
inferredcites →https://doi.org/10.1016/s0045-7949(02)00437-6openalex-extracted
Empirical modelling of shear strength of RC deep beams by genetic programming
inferredcites →https://doi.org/10.1061/(asce)cp.1943-5487.0000787openalex-extracted
Machine Learning–Based Backbone Curve Model of Reinforced Concrete Columns Subjected to Cyclic Loading Reversals
inferredcites →https://doi.org/10.1109/cec.2002.1006211openalex-extracted
Prevention of early convergence in genetic programming by replacement of similar programs
inferredcites →https://doi.org/10.1109/cec.2015.7257018openalex-extracted
Transfer learning in genetic programming
inferredcites →https://doi.org/10.1109/cec.2016.7744245openalex-extracted
Further investigation on genetic programming with transfer learning for symbolic regression
inferredcites →https://doi.org/10.1109/cec.2017.7969453openalex-extracted
Common subtrees in related problems: A novel transfer learning approach for genetic programming
inferredcites →https://doi.org/10.1109/jproc.2020.3004555openalex-extracted
A Comprehensive Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/tkde.2009.191openalex-extracted
A Survey on Transfer Learning
inferredcites →https://doi.org/10.1111/mice.12965openalex-extracted
A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups
inferredcites →https://doi.org/10.1155/2020/8850535openalex-extracted
Applications of Gene Expression Programming for Estimating Compressive Strength of High‐Strength Concrete
inferredcites →https://doi.org/10.1155/2021/6618407openalex-extracted
Compressive Strength of Fly‐Ash‐Based Geopolymer Concrete by Gene Expression Programming and Random Forest
inferredcites →https://doi.org/10.1186/s40537-016-0043-6openalex-extracted
A survey of transfer learning
inferredcites →https://doi.org/10.1186/s40537-017-0089-0openalex-extracted
A survey on heterogeneous transfer learning
inferredcites →https://doi.org/10.14359/10416openalex-extracted
The Modified Compression-Field Theory for Reinforced Concrete Elements Subjected to Shear
inferredcites →https://doi.org/10.14359/14274openalex-extracted
Empirical Modeling of Reinforced Concrete Shear Strength Size Effect for Members without Stirrups
inferredcites →https://doi.org/10.14359/19731openalex-extracted
Shear Strength of Members without Transverse Reinforcement as Function of Critical Shear Crack Width
inferredcites →https://doi.org/10.14359/19942openalex-extracted
Where is Shear Reinforcement Required? Review of Research Results and Design Procedures
inferredcites →https://doi.org/10.15554/pcij.05011987.74.150openalex-extracted
Toward a Consistent Design of Structural Concrete
inferredcites →https://doi.org/10.3390/ma13051218openalex-extracted
Experimental Study on Shear Capacity of High Strength Reinforcement Concrete Deep Beams with Small Shear Span–Depth Ratio
inferredcites →https://doi.org/10.48550/arxiv.cs/0102027openalex-extracted
Gene Expression Programming: a New Adaptive Algorithm for Solving Problems
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.