ArticleScientific reports2025
Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis.
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 4 papers.
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4 citing papers in PubMed.
- Machine Learning-Enabled Real-Time Prediction of Drying Shrinkage in Fly Ash-Modified Cementitious Materials.ACS omega · 2026Article
- Performance evaluation of fly-ash and silica-fume based geopolymer concrete at different alkaline molarities with machine learning-based strength prediction.Scientific reports · 2026Article
- Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties.Scientific reports · 2025Article
- AI-driven models for prediction of ceramic tiles' properties and detection of the influences: Behavior during shaping and drying.Science progressArticle
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7 authors.
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Abstract
Recent sustainable engineering trends show the re-use of wastes in the production of concrete materials. This was important in two ways. First, there is a great environmental necessity to eliminate these industrial wastes and their usage in a solid waste upcycling system to ensure structural sustainability creates an avenue for this process. Second, it has become important to reduce laboratory and equipment costs by establishing intelligent models through the application of these supplementary cements and optimized for optimal performance of concrete materials. For these reasons, the present research work has applied the intelligent learning abilities of eight (8) ensemble-based and one (1) symbolic regression machine learning methods to predict the strengths (compressive-Fc, flexural-Ff and splitting tensile-Ft) of SCGPC with the "Orange Data Mining" software version 3.36. In this research paper, the influence of the industrial wastes like ground granulated blast furnace slag (GGBS) and fly ash (FA) and alkali activators such as (NaOH and Na
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