ArticleMaterials (Basel, Switzerland)2022
Multi Expression Programming Model for Strength Prediction of Fly-Ash-Treated Alkali-Contaminated Soils.
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 5 papers.
What it found
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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
5 citing papers in PubMed.
- Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis.Scientific reports · 2026Article
- Machine learning prediction of the unconfined compressive strength of controlled low strength material using fly ash and pond ash.Scientific reports · 2024Article
- Article
- Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches.Materials (Basel, Switzerland) · 2022Article
- PCA-Based Hybrid Intelligence Models for Estimating the Ultimate Bearing Capacity of Axially Loaded Concrete-Filled Steel Tubes.Materials (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Rapid industrialization is leading to the pollution of underground natural soil by alkali concentration which may cause problems for the existing expansive soil in the form of producing expanding lattices. This research investigates the effect of stabilizing alkali-contaminated soil by using fly ash. The influence of alkali concentration (2 N and 4 N) and curing period (up to 28 days) on the unconfined compressive strength (UCS) of fly ash (FA)-treated (10%, 15%, and 20%) alkali-contaminated kaolin and black cotton (BC) soils was investigated. The effect of incorporating different dosages of FA (10%, 15%, and 20%) on the UCS
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