ArticleMaterials (Basel, Switzerland)2016
Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash.
Article in Materials (Basel, Switzerland), 2016. 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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Who cites it
14 citing papers in PubMed.
- Machine learning-driven optimization of alkali-activated sustainable pavement concrete incorporating agro-industrial wastes.Scientific reports · 2026Article
- Hybrid machine learning approach for predicting compressive strength of sustainable concrete incorporating palm oil fuel ash.Scientific reports · 2026Article
- Innovative development of high-strength concrete with ultrafine metakaolin-heated POFA binders and fine coal bottom ash aggregate: Strength, durability and microstructural performance.Scientific reports · 2026Article
- Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis.Scientific reports · 2025Article
- Article
- Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis.Scientific reports · 2025Article
- Review
- Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.PeerJ. Computer science · 2024Article
- Article
- A Review on the Application of Lignocellulosic Biomass Ash in Cement-Based Composites.Materials (Basel, Switzerland) · 2023Review
- Design of a machine learning model for the precise manufacturing of green cementitious composites modified with waste granite powder.Scientific reports · 2022Article
- Palm Oil Fuel Ash-Based Eco-Efficient Concrete: A Critical Review of the Short-Term Properties.Materials (Basel, Switzerland) · 2021Review
- Effect of Shrinkage Reducing Admixture on Drying Shrinkage of Concrete with Different w/c Ratios.Materials (Basel, Switzerland) · 2020Article
- Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials.Sensors (Basel, Switzerland) · 2017Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (
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