ArticleScientific reports2024
Enhancing the mechanical properties' performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Compressive strength prediction of coconut fiber reinforced concrete using PSO optimized explainable machine learning.Scientific reports · 2026Article
- Experimental and machine learning-based analysis of red mud influence on recycled aggregate concrete properties.Scientific reports · 2025Article
- Mechanical and thermal performance of bio-brick masonry with hydrated lime mortar at high temperature.Scientific reports · 2025Article
- Optimization of waste plastic fiber concrete with recycled coarse aggregate using RSM and ANN.Scientific reports · 2025Article
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Authors and funding
5 authors.
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Abstract
The present research incorporates five AI methods to enhance and forecast the characteristics of building envelopes. In this study, Response Surface Methodology (RSM), Support Vector Machine (SVM), Gradient Boosting (GB), Artificial Neural Networks (ANN), and Random Forest (RF) machine learning method for optimization and predicting the mechanical properties of natural fiber addition incorporated with construction and demolition waste (CDW) as replacement of Fine Aggregate in Paver blocks. In this study, factors considered were cement content, natural fine aggregate, CDW, and coconut fibre, while the resulting measure was the machinal properties of the paver blocks. Furthermore, machine learning techniques to precision the predicting machinal properties were extensively evaluated. The outcomes from both the training and testing phases demonstrated the strong predictive power of RSM, SVM, GB, ANN, and RF with a criterion used Root Mean square error (RMSE), Mean square error (MSE), Mean Absolute Error (MAE) and correlation coefficient (R). Moreover, the results demonstrated that GB and ANN provide enhanced performance in comparison to SVM and RF for determining testing factors.
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Registered trials
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