Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials
The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete contain...
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| Published in | Materials Vol. 14; no. 19; p. 5762 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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02.10.2021
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| Abstract | The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures. |
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| AbstractList | The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures. The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R 2 ), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R 2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures. The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures.The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures. |
| Author | Aslam, Fahid Joyklad, Panuwat Ostrowski, Krzysztof Adam Zajdel, Paulina Ahmad, Waqas Ahmad, Ayaz |
| AuthorAffiliation | 4 Department of Civil and Environmental Engineering, Faculty of Engineering, Srinakharinwirot University, 63 Mou 6, Rangsit-Nakhonnayok Rd., Khong 16, Ongkharak District, Nakhon Nayok 26120, Thailand; panuwatj@g.swu.ac.th 3 Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; f.aslam@psau.edu.sa 2 Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland; krzysztof.ostrowski.1@pk.edu.pl (K.A.O.); paulina.zajdel@doktorant.pk.edu.pl (P.Z.) 1 Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan |
| AuthorAffiliation_xml | – name: 2 Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland; krzysztof.ostrowski.1@pk.edu.pl (K.A.O.); paulina.zajdel@doktorant.pk.edu.pl (P.Z.) – name: 1 Department of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan – name: 3 Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; f.aslam@psau.edu.sa – name: 4 Department of Civil and Environmental Engineering, Faculty of Engineering, Srinakharinwirot University, 63 Mou 6, Rangsit-Nakhonnayok Rd., Khong 16, Ongkharak District, Nakhon Nayok 26120, Thailand; panuwatj@g.swu.ac.th |
| Author_xml | – sequence: 1 givenname: Waqas orcidid: 0000-0002-1668-7607 surname: Ahmad fullname: Ahmad, Waqas – sequence: 2 givenname: Ayaz orcidid: 0000-0002-0312-2965 surname: Ahmad fullname: Ahmad, Ayaz – sequence: 3 givenname: Krzysztof Adam orcidid: 0000-0001-5047-5862 surname: Ostrowski fullname: Ostrowski, Krzysztof Adam – sequence: 4 givenname: Fahid orcidid: 0000-0003-2863-3283 surname: Aslam fullname: Aslam, Fahid – sequence: 5 givenname: Panuwat surname: Joyklad fullname: Joyklad, Panuwat – sequence: 6 givenname: Paulina orcidid: 0000-0002-1940-0106 surname: Zajdel fullname: Zajdel, Paulina |
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| Cites_doi | 10.1016/j.conbuildmat.2020.118581 10.1007/s10853-016-9917-4 10.1016/j.conbuildmat.2020.121456 10.1002/fam.2968 10.1016/j.cemconres.2009.07.009 10.1016/j.conbuildmat.2019.116761 10.1016/j.jclepro.2021.126032 10.1002/suco.202000472 10.3390/ma12010039 10.1016/j.conbuildmat.2018.01.167 10.1016/j.jobe.2020.101272 10.3390/ma14040794 10.1061/(ASCE)0899-1561(2006)18:4(597) 10.3390/ma14154222 10.1111/ffe.13483 10.1155/2020/8850535 10.3390/ma14143829 10.1016/j.conbuildmat.2020.120102 10.3390/ma13051075 10.1016/j.jclepro.2014.03.005 10.3390/ma12203324 10.3390/ma14154264 10.1016/j.conbuildmat.2018.08.021 10.1016/S0008-8846(98)00165-3 10.3390/ma14092297 10.1016/j.conbuildmat.2018.06.150 10.1016/j.advengsoft.2015.05.007 10.1016/j.asoc.2019.105837 10.1007/s10346-019-01286-5 10.1016/j.jclepro.2019.118937 10.1016/j.conbuildmat.2016.08.111 10.3390/ma14081827 10.1680/jgrma.18.00010 10.1016/j.cemconres.2010.12.001 10.1016/j.jclepro.2015.05.145 10.3390/buildings11080324 10.1016/j.conbuildmat.2019.01.024 10.1016/j.cemconres.2019.05.008 10.1016/j.cemconcomp.2017.06.011 10.1016/j.cemconres.2006.01.005 |
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| References | Khan (ref_11) 2020; 30 Ying (ref_43) 2013; 39 Oliveira (ref_23) 2020; 262 Dou (ref_41) 2020; 17 Li (ref_20) 2019; 7 Ribeiro (ref_40) 2020; 86 Dananjayan (ref_25) 2016; 112 ref_14 Chu (ref_4) 2019; 228 ref_35 ref_34 Freund (ref_42) 1999; 14 Khan (ref_5) 2016; 125 ref_33 Afroughsabet (ref_8) 2016; 51 ref_32 Tosti (ref_21) 2020; 245 Khan (ref_13) 2021; 44 Khan (ref_12) 2018; 182 Lothenbach (ref_18) 2011; 41 ref_15 Yeh (ref_37) 2006; 18 Lee (ref_28) 2006; 36 Juenger (ref_22) 2019; 101 Su (ref_30) 2021; 270 Khan (ref_10) 2018; 166 Nguyen (ref_31) 2020; 247 Gandomi (ref_39) 2015; 88 ref_46 Juenger (ref_19) 2019; 122 Yeh (ref_38) 2003; 15 Shubbar (ref_24) 2018; 187 Aslam (ref_45) 2020; 2020 ref_1 Ahmad (ref_17) 2021; 15 Yeh (ref_36) 1998; 28 ref_3 ref_2 Ahmad (ref_16) 2021; 15 ref_29 Li (ref_27) 2009; 39 Shi (ref_9) 2019; 202 Farooq (ref_44) 2021; 292 Ukwattage (ref_26) 2015; 103 ref_7 ref_6 |
| References_xml | – volume: 247 start-page: 118581 year: 2020 ident: ref_31 article-title: Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2020.118581 – volume: 51 start-page: 6517 year: 2016 ident: ref_8 article-title: High-performance fiber-reinforced concrete: A review publication-title: J. Mater. Sci. doi: 10.1007/s10853-016-9917-4 – volume: 15 start-page: e00683 year: 2021 ident: ref_16 article-title: A scientometric review of waste material utilization in concrete for sustainable construction publication-title: Case Stud. Constr. Mater. – volume: 270 start-page: 121456 year: 2021 ident: ref_30 article-title: Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2020.121456 – ident: ref_14 doi: 10.1002/fam.2968 – volume: 39 start-page: 1033 year: 2009 ident: ref_27 article-title: Durability properties of micro-cracked ECC containing high volumes fly ash publication-title: Cem. Concr. Res. doi: 10.1016/j.cemconres.2009.07.009 – volume: 228 start-page: 116761 year: 2019 ident: ref_4 article-title: Mixture design of self-levelling ultra-high performance FRC publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2019.116761 – volume: 292 start-page: 126032 year: 2021 ident: ref_44 article-title: Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2021.126032 – ident: ref_6 doi: 10.1002/suco.202000472 – volume: 14 start-page: 1612 year: 1999 ident: ref_42 article-title: A short introduction to boosting publication-title: J. Jpn. Soc. Artif. Intell. – ident: ref_2 doi: 10.3390/ma12010039 – volume: 166 start-page: 581 year: 2018 ident: ref_10 article-title: Effectiveness of hair and wave polypropylene fibers for concrete roads publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2018.01.167 – volume: 30 start-page: 101272 year: 2020 ident: ref_11 article-title: Cracking behaviour and constitutive modelling of hybrid fibre reinforced concrete publication-title: J. Build. Eng. doi: 10.1016/j.jobe.2020.101272 – ident: ref_29 doi: 10.3390/ma14040794 – volume: 18 start-page: 597 year: 2006 ident: ref_37 article-title: Analysis of strength of concrete using design of experiments and neural networks publication-title: J. Mater. Civ. Eng. doi: 10.1061/(ASCE)0899-1561(2006)18:4(597) – ident: ref_35 – ident: ref_46 doi: 10.3390/ma14154222 – volume: 44 start-page: 2135 year: 2021 ident: ref_13 article-title: Efficiency of basalt fiber length and content on mechanical and microstructural properties of hybrid fiber concrete publication-title: Fatigue Fract. Eng. Mater. Struct. doi: 10.1111/ffe.13483 – volume: 2020 start-page: 1 year: 2020 ident: ref_45 article-title: Applications of gene expression programming for estimating compressive strength of high-strength concrete publication-title: Adv. Civ. Eng. doi: 10.1155/2020/8850535 – ident: ref_34 doi: 10.3390/ma14143829 – volume: 262 start-page: 120102 year: 2020 ident: ref_23 article-title: Use of air-cooled blast furnace slag as supplementary cementitious material for self-compacting concrete production publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2020.120102 – ident: ref_7 doi: 10.3390/ma13051075 – volume: 103 start-page: 665 year: 2015 ident: ref_26 article-title: A laboratory-scale study of the aqueous mineral carbonation of coal fly ash for CO2 sequestration publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2014.03.005 – ident: ref_3 doi: 10.3390/ma12203324 – volume: 39 start-page: 745 year: 2013 ident: ref_43 article-title: Advance and prospects of AdaBoost algorithm publication-title: Acta Autom. Sin. – volume: 15 start-page: 659 year: 2003 ident: ref_38 article-title: Prediction of strength of fly ash and slag concrete by the use of artificial neural networks publication-title: J. Chin. Inst. Civil Hydraul. Eng. – ident: ref_1 doi: 10.3390/ma14154264 – volume: 15 start-page: e00698 year: 2021 ident: ref_17 article-title: Sustainable approach of using sugarcane bagasse ash in cement-based composites: A systematic review publication-title: Case Stud. Constr. Mater. – volume: 187 start-page: 1051 year: 2018 ident: ref_24 article-title: The development of a low carbon binder produced from the ternary blending of cement, ground granulated blast furnace slag and high calcium fly ash: An experimental and statistical approach publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2018.08.021 – volume: 28 start-page: 1797 year: 1998 ident: ref_36 article-title: Modeling of strength of high-performance concrete using artificial neural networks publication-title: Cem. Concr. Res. doi: 10.1016/S0008-8846(98)00165-3 – ident: ref_32 doi: 10.3390/ma14092297 – volume: 182 start-page: 703 year: 2018 ident: ref_12 article-title: Effect of super plasticizer on the properties of medium strength concrete prepared with coconut fiber publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2018.06.150 – volume: 88 start-page: 63 year: 2015 ident: ref_39 article-title: Assessment of artificial neural network and genetic programming as predictive tools publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2015.05.007 – volume: 86 start-page: 105837 year: 2020 ident: ref_40 article-title: Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105837 – volume: 17 start-page: 641 year: 2020 ident: ref_41 article-title: Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan publication-title: Landslides doi: 10.1007/s10346-019-01286-5 – volume: 245 start-page: 118937 year: 2020 ident: ref_21 article-title: Life cycle assessment of the reuse of fly ash from biomass combustion as secondary cementitious material in cement products publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2019.118937 – volume: 125 start-page: 800 year: 2016 ident: ref_5 article-title: Use of glass and nylon fibers in concrete for controlling early age micro cracking in bridge decks publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2016.08.111 – ident: ref_15 doi: 10.3390/ma14081827 – volume: 7 start-page: 40 year: 2019 ident: ref_20 article-title: Durability properties of structural concretes containing secondary cementitious materials publication-title: Green Mater. doi: 10.1680/jgrma.18.00010 – volume: 41 start-page: 1244 year: 2011 ident: ref_18 article-title: Supplementary cementitious materials publication-title: Cem. Concr. Res. doi: 10.1016/j.cemconres.2010.12.001 – volume: 112 start-page: 4173 year: 2016 ident: ref_25 article-title: Direct mineral carbonation of coal fly ash for CO2 sequestration publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2015.05.145 – ident: ref_33 doi: 10.3390/buildings11080324 – volume: 202 start-page: 290 year: 2019 ident: ref_9 article-title: Research progress on CNTs/CNFs-modified cement-based composites—A review publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2019.01.024 – volume: 122 start-page: 257 year: 2019 ident: ref_19 article-title: Supplementary cementitious materials: New sources, characterization, and performance insights publication-title: Cem. Concr. Res. doi: 10.1016/j.cemconres.2019.05.008 – volume: 101 start-page: 44 year: 2019 ident: ref_22 article-title: Extending supplementary cementitious material resources: Reclaimed and remediated fly ash and natural pozzolans publication-title: Cem. Concr. Compos. doi: 10.1016/j.cemconcomp.2017.06.011 – volume: 36 start-page: 1279 year: 2006 ident: ref_28 article-title: Autogenous shrinkage of concrete containing granulated blast-furnace slag publication-title: Cem. Concr. Res. doi: 10.1016/j.cemconres.2006.01.005 |
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| SubjectTerms | Accuracy Algorithms Artificial intelligence Bagging Blast furnace practice Blast furnace slags Cement hydration Civil engineering Compressive strength Concrete Decision trees Errors Fly ash Gene expression Laboratories Machine learning Mean square errors Mechanical properties Neural networks Sensitivity analysis Software |
| Title | Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials |
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