Neural Network Prediction and Enhanced Strength Properties of Natural Fibre-Reinforced Quaternary-Blended Composites

This research, with its potential to revolutionise the construction industry, aims to develop quaternary-blended composites (QBC) by replacing 80% of ordinary Portland cement (OPC) with metakaolin, rice husk ash, and wood ash combined with discrete hybrid natural fibres at a volume fraction of 0.5%. This study investigates the mechanical properties, including compressive strength, split tensile strength, and impact strength of the QBC at various curing ages of 7, 28, and 56 days. Scanning electron microscopy (SEM) analysis was performed to assess the microstructural characteristics. This research aimed to formulate a novel quaternary binder that may minimise our reliance on cement. The experimental results indicate that the mix labelled M4L2 exhibited superior compressive and split tensile strength performance, with percentage increases of approximately 51.03% and 29.19%, respectively. Meanwhile, the M5L1 mix demonstrated enhanced impact energy, with a percentage increase of about 36.40% in 56 days. SEM observations revealed that the MC4 mix contained unhydrated portions and larger cracks. In contrast, the presence of fibres in the M4L2 mix contributed to crack resistance, resulting in a denser matrix and improved microstructural properties. This study also employed an artificial neural network (ANN) model to predict the compressive, tensile, and impact strength characteristics of the QBC, with the predictions aligning closely with the experimental results. An investigation was conducted to determine the ideal number of hidden layers and neurons in each layer. The model’s effectiveness was evaluated using statistical metrics such as correlation coefficient (R), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MEA), and mean absolute percentage error (MAPE). The findings suggest that the developed QBCs can effectively reduce reliance on conventional cement while offering improved mechanical properties suitable for sustainable construction practices.

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Publication Details

Journal
CivilEng
Published
2024-09-26
DOI
https://doi.org/10.3390/civileng5040043
Citations
3
Primary Topic
Innovative concrete reinforcement materials
Type
article
Field-Weighted Citation Impact
0.78
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article

Neural Network Prediction and Enhanced Strength Properties of Natural Fibre-Reinforced Quaternary-Blended Composites

Pavithra Chandramouli, Veerappan Sathish Kumar, J. Revathy, Gajalakshmi Pandulu et al.
3 citations
CivilEng
Innovative concrete reinforcement materials
0.78
article

Neural Network Prediction and Enhanced Strength Properties of Natural Fibre-Reinforced Quaternary-Blended Composites

Pavithra Chandramouli, Veerappan Sathish Kumar, J. Revathy, Gajalakshmi Pandulu, Mohamed Riyaaz Nayum Akthar
article en
3 citations

Abstract

This research, with its potential to revolutionise the construction industry, aims to develop quaternary-blended composites (QBC) by replacing 80% of ordinary Portland cement (OPC) with metakaolin, rice husk ash, and wood ash combined with discrete hybrid natural fibres at a volume fraction of 0.5%. This study investigates the mechanical properties, including compressive strength, split tensile strength, and impact strength of the QBC at various curing ages of 7, 28, and 56 days. Scanning electron microscopy (SEM) analysis was performed to assess the microstructural characteristics. This research aimed to formulate a novel quaternary binder that may minimise our reliance on cement. The experimental results indicate that the mix labelled M4L2 exhibited superior compressive and split tensile strength performance, with percentage increases of approximately 51.03% and 29.19%, respectively. Meanwhile, the M5L1 mix demonstrated enhanced impact energy, with a percentage increase of about 36.40% in 56 days. SEM observations revealed that the MC4 mix contained unhydrated portions and larger cracks. In contrast, the presence of fibres in the M4L2 mix contributed to crack resistance, resulting in a denser matrix and improved microstructural properties. This study also employed an artificial neural network (ANN) model to predict the compressive, tensile, and impact strength characteristics of the QBC, with the predictions aligning closely with the experimental results. An investigation was conducted to determine the ideal number of hidden layers and neurons in each layer. The model’s effectiveness was evaluated using statistical metrics such as correlation coefficient (R), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MEA), and mean absolute percentage error (MAPE). The findings suggest that the developed QBCs can effectively reduce reliance on conventional cement while offering improved mechanical properties suitable for sustainable construction practices.

CivilEngVol. 5(4)
SRM Institute of Science and Technology (IN), B.S. Abdur Rahman Crescent Institute of Science & Technology (IN)
Openalex Percentile: Top 30%
Innovative concrete reinforcement materials
0.78
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