ArticleMaterials (Basel, Switzerland)2021
A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP.Scientific reports · 2024Article
- Article
- Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.Scientific reports · 2024Article
- Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete.Gels (Basel, Switzerland) · 2024Article
- Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.PeerJ. Computer science · 2024Article
- Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques.Scientific reports · 2023Article
- Article
- An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset.Scientific reports · 2023Article
- Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis.Materials (Basel, Switzerland) · 2023Article
- Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches.Materials (Basel, Switzerland) · 2022Article
- Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques.Materials (Basel, Switzerland) · 2022Article
- Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash.Materials (Basel, Switzerland) · 2022Article
- A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete.Materials (Basel, Switzerland) · 2022Article
- Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming.Materials (Basel, Switzerland) · 2022Article
- Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques.Materials (Basel, Switzerland) · 2022Article
- Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches.Materials (Basel, Switzerland) · 2022Article
- Development of High-Tech Self-Compacting Concrete Mixtures Based on Nano-Modifiers of Various Types.Materials (Basel, Switzerland) · 2022Article
- Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches.Materials (Basel, Switzerland) · 2021Article
- Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP.Materials (Basel, Switzerland) · 2021Article
- Development of Flotation Device for Removing Unburnt Carbon in Fly Ash for Use in Hardened Cementitious Materials.Materials (Basel, Switzerland) · 2021Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water-binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (
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