ArticleScientific reports2025
Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses.
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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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
7 citing papers in PubMed.
- An interpretable surrogate modeling framework for rice husk ash concrete using copula-based virtual sampling.Scientific reports · 2026Article
- Compressive strength of mortar incorporating recycled nonmetallic printed circuit board waste: experimental and interpretable machine learning analysis.Scientific reports · 2026Article
- Evaluate the compressive strength of fiber-reinforced geopolymer concrete incorporating ground granulated blast furnace slag with machine learning approach.Scientific reports · 2026Article
- Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete.Scientific reports · 2026Article
- Evaluating the predictive accuracy of supervised machine learning models to explore the mechanical strength of blast furnace slag incorporated concrete.Scientific reports · 2026Article
- CFG-DWC: a hybrid correlation-driven feature engineering framework for optimized machine learning performance in carbonation depth analysis of concrete subjected to natural environments.Scientific reports · 2025Article
- Hybrid Mortar Composites Incorporating Oyster Shell Filler and Recycled Fibers from Disposable Masks.Materials (Basel, Switzerland) · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The construction sector is proactively working to minimize the environmental impact of cement manufacturing by adopting alternative cementitious substances and cutting carbon emissions tied to concrete. This study investigates the viability of using waste industrial materials as a replacement of cement in concrete mixes. The primary goal is to predict the compressive strength of waste-incorporated concrete by evaluating the effects of materials such as cement, fly ash (FA), silica fume (SF), ground granulated blast furnace slag (GGBFS), metakaolin (MK), water usage, aggregate levels, and superplasticizer dosages. A total of 441 data entries were sourced from various publications. Multiple machine learning techniques, such as light gradient boosting (LGB), extreme gradient boosting (XGB), and decision trees (DT), along with hybrid approaches like XGB-LGB and XGB-DT, were utilized to study how these variables influence compressive strength. The dataset was partitioned into training and testing, and statistical tools were employed to assess the correlation between input variables and strength. Model accuracy was gauged using metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), and the coefficient of determination (R
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Registered trials
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