Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar

The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laborat...

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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 19414 - 30
Hlavní autori:	Khan, Naseer Muhammad, Ma, Liqiang, Inqiad, Waleed Bin, Khan, Muhammad Saud, Iqbal, Imtiaz, Emad, Muhammad Zaka, Alarifi, Saad S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 03.06.2025 Nature Publishing Group Nature Portfolio
Predmet:	639/166 704/172/4081 Algorithms Cement Cement mortar Civil engineering Compressive strength Construction industry Gene expression Gene expression programming Green development Humanities and Social Sciences Interpretable machine learning Learning algorithms Machine learning Metakaolin multidisciplinary Natural resources Prediction models Resource depletion Science Science (multidisciplinary) Metakaolin Interpretable machine learning Cement mortar Compressive strength Gene expression programming
ISSN:	2045-2322, 2045-2322
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Abstract	The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing of 0.998 followed by BR having values equal to 0.946 while MEP had the lowest testing of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn’t produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry.
AbstractList	Abstract The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing $$\:{\text{R}}^{2}$$ of 0.998 followed by BR having $$\:{\text{R}}^{2}$$ values equal to 0.946 while MEP had the lowest testing $$\:{\text{R}}^{2}$$ of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn’t produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry. The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing of 0.998 followed by BR having values equal to 0.946 while MEP had the lowest testing of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn’t produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry. The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing $$\:{\text{R}}^{2}$$ of 0.998 followed by BR having $$\:{\text{R}}^{2}$$ values equal to 0.946 while MEP had the lowest testing $$\:{\text{R}}^{2}$$ of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn’t produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry. The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing of 0.998 followed by BR having values equal to 0.946 while MEP had the lowest testing of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn’t produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry. The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing [Formula: see text] of 0.998 followed by BR having [Formula: see text] values equal to 0.946 while MEP had the lowest testing [Formula: see text] of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn't produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry. The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing [Formula: see text] of 0.998 followed by BR having [Formula: see text] values equal to 0.946 while MEP had the lowest testing [Formula: see text] of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn't produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry.The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the construction sector. This would reduce the stress on depleting natural resources and foster a sustainable construction industry. However, the laboratory determination of 28 day compressive strength (C-S) of MK-based mortar is associated with several time and resource constraints. Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. A comprehensive dataset compiled from published literature having five input parameters including water-to-binder ratio, mortar age, and maximum aggregate diameter etc. was used for this purpose. The developed models were validated by means of error metrics, residual assessment, and external validation checks which revealed that XGB is the most accurate algorithm having testing [Formula: see text] of 0.998 followed by BR having [Formula: see text] values equal to 0.946 while MEP had the lowest testing [Formula: see text] of 0.893. However, MEP and GEP algorithms expressed their output in the form of empirical equations which other black-box algorithms couldn't produce. Moreover, interpretable machine learning approaches including shapely additive explanatory analysis (SHAP), individual conditional expectation (ICE), and partial dependence plots (PDP) were conducted on the XGB model which highlighted that water-to-binder ratio and sample age are some of the most significant variables to predict the C-S of MK-based cement mortars. Finally, a graphical user interface (GUI) was made for implementation of findings of this study in the civil engineering industry.
ArticleNumber	19414
Author	Iqbal, Imtiaz Emad, Muhammad Zaka Khan, Muhammad Saud Alarifi, Saad S. Khan, Naseer Muhammad Ma, Liqiang Inqiad, Waleed Bin
Author_xml	– sequence: 1 givenname: Naseer Muhammad surname: Khan fullname: Khan, Naseer Muhammad email: nmkhan@mce.nust.edu.pk organization: Xinjiang Key Laboratory of Coal-bearing Resources Exploration and Exploitation, Xinjiang Institute of Engineering, Key Laboratory of Xinjiang Coal Resources Green Mining (Xinjiang Institute of Engineering), Ministry of Education, Xinjiang Engineering Research Center of Green Intelligent Coal Mining, Xinjiang Institute of Engineering, Sustainable Advanced Geomechanical Engineering, Military College of Engineering, National University of Sciences and Technology, School of Mines, China University of Mining and Technology – sequence: 2 givenname: Liqiang surname: Ma fullname: Ma, Liqiang email: ckma@cumt.edu.cn organization: Xinjiang Key Laboratory of Coal-bearing Resources Exploration and Exploitation, Xinjiang Institute of Engineering, Key Laboratory of Xinjiang Coal Resources Green Mining (Xinjiang Institute of Engineering), Ministry of Education, Xinjiang Engineering Research Center of Green Intelligent Coal Mining, Xinjiang Institute of Engineering, School of Mines, China University of Mining and Technology – sequence: 3 givenname: Waleed Bin surname: Inqiad fullname: Inqiad, Waleed Bin organization: Department of Civil Engineering, College of Engineering & Physical Sciences, Aston University – sequence: 4 givenname: Muhammad Saud surname: Khan fullname: Khan, Muhammad Saud organization: Department of Civil Engineering, Price Faculty of Engineering, University of Manitoba – sequence: 5 givenname: Imtiaz surname: Iqbal fullname: Iqbal, Imtiaz organization: Department of Civil Engineering, College of Engineering & Physical Sciences, Aston University – sequence: 6 givenname: Muhammad Zaka surname: Emad fullname: Emad, Muhammad Zaka organization: Department of Petroleum Engineering , King Fahd University of Petroleum and Minerals – sequence: 7 givenname: Saad S. surname: Alarifi fullname: Alarifi, Saad S. organization: Department of Geology and Geophysics, College of Science, King Saud University
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Keywords	Metakaolin Interpretable machine learning Cement mortar Compressive strength Gene expression programming
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Snippet	The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the... Abstract The use of naturally available materials such as metakaolin (MK) can greatly reduce the utilization of emission intensive materials like cement in the...
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SubjectTerms	639/166 704/172/4081 Algorithms Cement Cement mortar Civil engineering Compressive strength Construction industry Gene expression Gene expression programming Green development Humanities and Social Sciences Interpretable machine learning Learning algorithms Machine learning Metakaolin multidisciplinary Natural resources Prediction models Resource depletion Science Science (multidisciplinary)
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Title	Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar
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