ArticleScientific reports2025
Machine learning models with SHAP for performance prediction of eco-friendly fiber reinforced mortars with glass waste.
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 5 papers.
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5 citing papers in PubMed.
- Mode I, Mode III and mixed-mode fracture behaviour of jute-sisal fiber reinforced ternary geopolymer concrete.Scientific reports · 2026Article
- Mechanical and tribological response of red brick dust filled hemp-epoxy composites using response surface methodology and machine learning approach.Scientific reports · 2026Article
- Performance of eco-friendly geopolymer loadbearing bricks for thermally comfortable structures.Scientific reports · 2026Article
- A hybrid machine learning approach for predicting the flexural strength of concrete reinforced with waste aluminium fibres.Scientific reports · 2026Article
- Comparative assessment of machine learning models for polymer solution viscosity prediction in enhanced oil recovery.Scientific reports · 2026Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence and machine learning are increasingly applied in civil engineering to predict material performance and optimize mix design. This study compares three ensemble learning approaches, Boosting, Bagging, and Stacking, for predicting the slump and compressive strength (CS) of eco-friendly mortars produced in the laboratory by partially replacing cement with glass powder (GP) and reinforcing with flax fibers (FF) and polypropylene fibers (PPF). Seven input variables were considered: the amounts of cement, GP, water, water-to-binder ratio (W/B), FF, PPF, and the superplasticizer dosage. A database of 580 experimental mixtures was created and used to train six predictive models: XGBoost and LightGBM (Boosting), Random Forest and Extra Trees (Bagging), and two hybrid Stacking models. Model performance was assessed using statistical metrics (R
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