Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches

The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) t...
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Autor*in:	Syyed Adnan Raheel Shah [verfasserIn] Marc Azab [verfasserIn] Hany M. Seif ElDin [verfasserIn] Osama Barakat [verfasserIn] Muhammad Kashif Anwar [verfasserIn] Yasir Bashir [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	sustainability reinforced concrete industrial wastes compressive strength green and sustainable concrete high performance concrete

Übergeordnetes Werk:	In: Buildings - MDPI AG, 2012, 12(2022), 7, p 914
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:7, p 914

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/buildings12070914

Katalog-ID:	DOAJ030918251

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authorswithroles_txt_mv	Syyed Adnan Raheel Shah @@aut@@ Marc Azab @@aut@@ Hany M. Seif ElDin @@aut@@ Osama Barakat @@aut@@ Muhammad Kashif Anwar @@aut@@ Yasir Bashir @@aut@@
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callnumber-first	T - Technology
author	Syyed Adnan Raheel Shah
spellingShingle	Syyed Adnan Raheel Shah misc TH1-9745 misc sustainability misc reinforced concrete misc industrial wastes misc compressive strength misc green and sustainable concrete misc high performance concrete misc Building construction Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches
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topic_title	TH1-9745 Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches sustainability reinforced concrete industrial wastes compressive strength green and sustainable concrete high performance concrete
topic	misc TH1-9745 misc sustainability misc reinforced concrete misc industrial wastes misc compressive strength misc green and sustainable concrete misc high performance concrete misc Building construction
topic_unstemmed	misc TH1-9745 misc sustainability misc reinforced concrete misc industrial wastes misc compressive strength misc green and sustainable concrete misc high performance concrete misc Building construction
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title	Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches
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title_full	Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches
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title_sort	predicting compressive strength of blast furnace slag and fly ash based sustainable concrete using machine learning techniques: an application of advanced decision-making approaches
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title_auth	Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches
abstract	The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R<sup<2</sup<, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R<sup<2</sup< values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.
abstractGer	The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R<sup<2</sup<, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R<sup<2</sup< values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.
abstract_unstemmed	The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R<sup<2</sup<, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R<sup<2</sup< values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.
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title_short	Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches
url	https://doi.org/10.3390/buildings12070914 https://doaj.org/article/649d505dd1634046a56334d34f84b274 https://www.mdpi.com/2075-5309/12/7/914 https://doaj.org/toc/2075-5309
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author2	Marc Azab Hany M. Seif ElDin Osama Barakat Muhammad Kashif Anwar Yasir Bashir
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Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches

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