ArticleMaterials (Basel, Switzerland)2022
Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Ensemble machine learning models for predicting strength of concrete with foundry sand and coal bottom ash as fine aggregate replacements.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
- A Comprehensive Review of Numerical and Machine Learning Approaches for Predicting Concrete Properties: From Fresh to Long-Term.Materials (Basel, Switzerland) · 2025Review
- AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete.Scientific reports · 2025Article
- Predicting workability and mechanical properties of bentonite plastic concrete using hybrid ensemble learning.Scientific reports · 2025Article
- Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques.Scientific reports · 2023Article
- An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset.Scientific reports · 2023Article
- Machine Learning Estimation of Plateau Stress of Aluminum Foam Using X-ray Computed Tomography Images.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
- Recyclable Materials for Ecofriendly Technology.Materials (Basel, Switzerland) · 2022Article
- A Systematic Review of the Research Development on the Application of Machine Learning for Concrete.Materials (Basel, Switzerland) · 2022Review
- Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques.Materials (Basel, Switzerland) · 2022Article
- Comparative Numerical Studies on the Structural Behavior of Buried Pipes Subjected to Extreme Environmental Actions.Materials (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The entraining and distribution of air voids in the concrete matrix is a complex process that makes the mechanical properties of lightweight foamed concrete (LFC) highly unpredictable. To study the complex nature of aerated concrete, a reliable and robust prediction model is required, employing different machine learning (ML) techniques. This study aims to predict the compressive strength of LFC by using a support vector machine (SVM) as an individual learner along with bagging, boosting, and random forest (RF) as a modified ensemble learner. For that purpose, a database of 191 data points was collected from published literature, where the mix design ingredients, i.e., cement content, sand content, water to cement ratio, and foam volume, were chosen to predict the compressive strength of LFC. The 10-K fold cross-validation method and different statistical error and regression tools, i.e., mean absolute error (MAE), root means square error (RMSE), and coefficient of determinant (R
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