ArticleMaterials (Basel, Switzerland)2022
Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 19 citations in OpenAlex.
- A Comprehensive Review of Numerical and Machine Learning Approaches for Predicting Concrete Properties: From Fresh to Long-Term.Materials (Basel, Switzerland) · 2025Review
- Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions.Scientific reports · 2025Article
- An ensemble learning-based prediction model for the compressive strength degradation of concrete containing superabsorbent polymers (SAP).Scientific reports · 2024Article
- Experimental Study on Mechanical Properties and Durability of Polymer Silica Fume Concrete with Vinyl Ester Resin.Materials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the n
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.