ArticleScientific reports2025
Analyzing the influence of manufactured sand and fly ash on concrete strength through experimental and machine learning methods.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Passive determination of anisotropic compressive strength of 3D printed concrete using multiple neural networks enhanced with explainable machine learning (XML).Scientific reports · 2025Article
- Synergistic effects of fly ash, ground granulated blast furnace slag, and graphene oxide in low-carbon concrete: modelling, optimization, and sustainability assessment.Scientific reports · 2025Article
- Machine learning models with SHAP for performance prediction of eco-friendly fiber reinforced mortars with glass waste.Scientific reports · 2025Article
- Forecasting compressive strength of concrete containing rice husk ash using various machine learning algorithms.Scientific reports · 2025Article
- Experimental and GEP-based evaluation of compressive strength in eco-friendly mortars with waste foundry sand and varying cement grades.Scientific reports · 2025Article
- Evaluation of high performance concrete hardness properties using fuzzy logic based modeling.Scientific reports · 2025Article
- Comparison of deep LSTM and machine learning models for predicting compressive strength of fly ash/slag-based geopolymer concrete.Scientific reports · 2025Article
- Microstructural Analysis of Sand Reinforced by EICP Combined with Glutinous Rice Slurry Based on CT Scanning.Materials (Basel, Switzerland) · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
River sand supplies are decreasing due to overexploitation and illicit sand mining. One ton of Portland cement production (the main binder in concrete) emits about one ton of carbon dioxide into the atmosphere. Thus, this study replaced conventional cement and river sand (R sand) with recycled waste materials (fly ash and manufactured sand (M sand)). The concrete mix proportions were designed using M40 grade, and the Ordinary Portland cement (OPC) and R sand were replaced with 0-85 wt% of fly ash and 0-100 wt% of M sand. The concrete samples were tested for compressive strength after 3-90 days of curing. Furthermore, machine learning (ML) techniques were engaged to predict the compressive strength of the concrete samples using Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). Besides, the concrete samples containing fly ash, M sand, and R sand were characterized for microstructures and elemental compositions using SEM-EDS. The results revealed improved concrete compressive strength by incorporating fly ash and M sand. After 28 days of curing, OPC and R sand were partially replaced with 25 and 50 wt% of fly ash and M sand attained the designed strength of M 40 grade concrete. XGBoost model yielded the most accurate performance metrics for forecasting the compressive strength in training and testing phases with R
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