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Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution
Date palm fiber (DPF) is normally used as fiber material in concrete. Though its addition to concrete leads to decline in durability and mechanical strengths performance. Additionally, due to its high ligno-cellulose content and organic nature, when used in concrete for high temperature application,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10616177/ https://www.ncbi.nlm.nih.gov/pubmed/37903786 http://dx.doi.org/10.1038/s41598-023-45462-z |
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author | Adamu, Musa Rehman, Khalil Ur Ibrahim, Yasser E. Shatanawi, Wasfi |
author_facet | Adamu, Musa Rehman, Khalil Ur Ibrahim, Yasser E. Shatanawi, Wasfi |
author_sort | Adamu, Musa |
collection | PubMed |
description | Date palm fiber (DPF) is normally used as fiber material in concrete. Though its addition to concrete leads to decline in durability and mechanical strengths performance. Additionally, due to its high ligno-cellulose content and organic nature, when used in concrete for high temperature application, the DPF can easily degrade causing reduction in strength and increase in weight loss. To reduce these effects, the DPF is treated using alkaline solutions. Furthermore, pozzolanic materials are normally added to the DPF composites to reduce the effects of the ligno-cellulose content. Therefore, in this study silica fume was used as supplementary cementitious material in DPF reinforced concrete (DPFRC) to reduce the negative effects of elevated temperature. Hence this study aimed at predicting the residual strengths of DPFRC enhanced/improved with silica fume subjected to elevated temperature using different models such as artificial neural network (ANN), multi-variable regression analysis (MRA) and Weibull distribution. The DPFRC is produced by adding DPF in proportions of 0%, 1%, 2% and 3% by mass. Silica fume was used as partial substitute to cement in dosages of 0%, 5%, 10% and 15% by volume. The DPFRC was then subjected to elevated temperatures between 200 and 800 °C. The weight loss, residual compressive strength and relative strengths were measured. The residual compressive strength and relative strength of the DPFRC declined with addition of DPF at any temperature. Silica fume enhanced the residual and relative strengths of the DPFRC when heated to a temperature up to 400 °C. To forecast residual compressive strength (RCS) and relative strength (RS), we provide two distinct ANN models. The first layer's inputs include DPF (%), silica fume (%), temperature (°C), and weight loss (%). The hidden layer is thought to have ten neurons. M-I is the scenario in which we use RCS as an output, whereas M-II is the scenario in which we use RS as an output. The ANN models were trained using the Levenberg–Marquardt backpropagation algorithm (LMBA). Both neural networking models exhibit a significant correlation between the predicted and actual values, as seen by their respective R = 0.99462 and R = 0.98917. The constructed neural models M-I and M-II are highly accurate at predicting RCS and RS values. MRA and Weibull distribution were used for prediction of the strengths of the DPFRC under high temperature. The developed MRA was found to have a good prediction accuracy. The residual compressive strength and relative strength followed the two-parameter Weibull distribution. |
format | Online Article Text |
id | pubmed-10616177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106161772023-11-01 Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution Adamu, Musa Rehman, Khalil Ur Ibrahim, Yasser E. Shatanawi, Wasfi Sci Rep Article Date palm fiber (DPF) is normally used as fiber material in concrete. Though its addition to concrete leads to decline in durability and mechanical strengths performance. Additionally, due to its high ligno-cellulose content and organic nature, when used in concrete for high temperature application, the DPF can easily degrade causing reduction in strength and increase in weight loss. To reduce these effects, the DPF is treated using alkaline solutions. Furthermore, pozzolanic materials are normally added to the DPF composites to reduce the effects of the ligno-cellulose content. Therefore, in this study silica fume was used as supplementary cementitious material in DPF reinforced concrete (DPFRC) to reduce the negative effects of elevated temperature. Hence this study aimed at predicting the residual strengths of DPFRC enhanced/improved with silica fume subjected to elevated temperature using different models such as artificial neural network (ANN), multi-variable regression analysis (MRA) and Weibull distribution. The DPFRC is produced by adding DPF in proportions of 0%, 1%, 2% and 3% by mass. Silica fume was used as partial substitute to cement in dosages of 0%, 5%, 10% and 15% by volume. The DPFRC was then subjected to elevated temperatures between 200 and 800 °C. The weight loss, residual compressive strength and relative strengths were measured. The residual compressive strength and relative strength of the DPFRC declined with addition of DPF at any temperature. Silica fume enhanced the residual and relative strengths of the DPFRC when heated to a temperature up to 400 °C. To forecast residual compressive strength (RCS) and relative strength (RS), we provide two distinct ANN models. The first layer's inputs include DPF (%), silica fume (%), temperature (°C), and weight loss (%). The hidden layer is thought to have ten neurons. M-I is the scenario in which we use RCS as an output, whereas M-II is the scenario in which we use RS as an output. The ANN models were trained using the Levenberg–Marquardt backpropagation algorithm (LMBA). Both neural networking models exhibit a significant correlation between the predicted and actual values, as seen by their respective R = 0.99462 and R = 0.98917. The constructed neural models M-I and M-II are highly accurate at predicting RCS and RS values. MRA and Weibull distribution were used for prediction of the strengths of the DPFRC under high temperature. The developed MRA was found to have a good prediction accuracy. The residual compressive strength and relative strength followed the two-parameter Weibull distribution. Nature Publishing Group UK 2023-10-30 /pmc/articles/PMC10616177/ /pubmed/37903786 http://dx.doi.org/10.1038/s41598-023-45462-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Adamu, Musa Rehman, Khalil Ur Ibrahim, Yasser E. Shatanawi, Wasfi Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution |
title | Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution |
title_full | Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution |
title_fullStr | Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution |
title_full_unstemmed | Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution |
title_short | Predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and Weibull distribution |
title_sort | predicting the strengths of date fiber reinforced concrete subjected to elevated temperature using artificial neural network, and weibull distribution |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10616177/ https://www.ncbi.nlm.nih.gov/pubmed/37903786 http://dx.doi.org/10.1038/s41598-023-45462-z |
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