ArticleScientific reports2025
Machine learning predictions of high-strength RCA concrete utilizing chemically activated fly ash and nano-silica.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Modeling the mechanical properties of lightweight high-strength concrete incorporating supplementary cementitious materials using multi-expression programming and random forest.Scientific reports · 2026Article
- Prediction of the Unconfined Compressive Strength of One-Part Geopolymer-Stabilized Soil Under Acidic Erosion: Comparison of Multiple Machine Learning Models.Materials (Basel, Switzerland) · 2026Article
- Multi-scale theoretical modeling with molecular simulation framework for fly ash-based high-performance concrete.Scientific reports · 2025Article
- Hexaallylaminocyclotriphosphazene-Modified Dental Compositions for 3D Printing of Dental Crowns.Polymers · 2025Article
- Performance optimization of rigid pavement concrete using metakaolin treated RCA and silica fume with an experimental and machine learning based approach.Scientific reports · 2025Article
- Synergistic Influence of Multi-Walled Carbon Nanotubes and Nanosilica Powder on Mechanical Performance of Mortar with Demolished Concrete Waste Aggregate and Polypropylene Fibers Addition Using Taguchi Design of Experiment.Materials (Basel, Switzerland) · 2025Article
- Influence of colloidal nano silica on solidification mechanisms and hydration process of nano modified cement.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explores the potential of RCA combined with nano silica and chemically activated fly ash to produce sustainable and high strength concrete. The research addresses the challenges posed by RCA's inferior mechanical and durability properties by incorporating SCM. A comprehensive experimental program includes 420 and 240 samples for compressive strength and acid resistance. Machine learning algorithms such as Decision Trees, Random Forest, XG-Boost, and Ada Boost are used to predict RCA concrete performance metrics, with XG-Boost achieve the highest predictive accuracy (R
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Registered trials
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