ArticleMaterials (Basel, Switzerland)2022
Predicting the Compressive Strength of Concrete Containing Fly Ash and Rice Husk Ash Using ANN and GEP Models.
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 3 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
3 citing papers in PubMed.
- Optimizing the mechanical performance of sustainable industrial waste modified concrete using supervised machine learning modeling and feature importance analysis.Scientific reports · 2026Article
- ANN trained by BBO for modeling of fly ash cementitious systems with high range water reducing admixtures.Scientific reports · 2026Article
- Machine learning and interactive GUI for concrete compressive strength prediction.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Climate change has become trending news due to its serious impacts on Earth. Initiatives are being taken to lessen the impact of climate change and mitigate it. Among the different initiatives, researchers are aiming to find suitable alternatives for cement. This study is a humble effort to effectively utilize industrial- and agricultural-waste-based pozzolanic materials in concrete to make it economical and environmentally friendly. For this purpose, a ternary blend of binders (i.e., cement, fly ash, and rice husk ash) was employed in concrete. Different variables such as the quantity of different binders, fine and coarse aggregates, water, superplasticizer, and the age of the samples were considered to study their influence on the compressive strength of the ternary blended concrete using gene expression programming (GEP) and artificial neural networking (ANN). The performance of these two models was evaluated using R
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Registered trials
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