Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cem...

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Published in	Materials Vol. 15; no. 15; p. 5242
Main Authors	Khan, Kaffayatullah, Gudainiyan, Jitendra, Iqbal, Mudassir, Jamal, Arshad, Amin, Muhammad Nasir, Mohammed, Ibrahim, Al-Faiad, Majdi Adel, Abu-Arab, Abdullah M.
Format	Journal Article
Language	English
Published	Basel MDPI AG 29.07.2022 MDPI
Subjects	Additives Aggregates Algorithms By products Cement Civil engineering Compressive strength Concrete Construction industry Fly ash Gene expression Grout Industrial wastes Investigations Laboratories Landfill Mathematical models Natural resources Neural networks Parameter sensitivity Particle swarm optimization Performance indices Polyethylene terephthalate Portland cements Radiation Reinforced concrete Sensitivity analysis Silica fume Support vector machines Swarm intelligence
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ISSN	1996-1944 1996-1944
DOI	10.3390/ma15155242

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Abstract	Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.
AbstractList	Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects. Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.
Author	Khan, Kaffayatullah Abu-Arab, Abdullah M. Al-Faiad, Majdi Adel Iqbal, Mudassir Jamal, Arshad Mohammed, Ibrahim Gudainiyan, Jitendra Amin, Muhammad Nasir
AuthorAffiliation	3 Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan; mudassiriqbal@uetpeshawar.edu.pk 5 Applied Research Center for Metrology Standards and Testing, Research and Innovation, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; ibrahim@kfupm.edu.sa 1 Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf 31982, Saudi Arabia; mgadir@kfu.edu.sa (M.N.A.); malfaiad@kfu.edu.sa (M.A.A.-F.); 219041496@student.kfu.edu.sa (A.M.A.-A.) 4 Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman BinFaisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia; ajjamal@iau.edu.sa 2 Department of Civil Engineering, GLA University, Mathura 281 406, India; jitendra.gudainiyan@gla.ac.in
AuthorAffiliation_xml	– name: 2 Department of Civil Engineering, GLA University, Mathura 281 406, India; jitendra.gudainiyan@gla.ac.in – name: 3 Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan; mudassiriqbal@uetpeshawar.edu.pk – name: 4 Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman BinFaisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia; ajjamal@iau.edu.sa – name: 5 Applied Research Center for Metrology Standards and Testing, Research and Innovation, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; ibrahim@kfupm.edu.sa – name: 1 Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf 31982, Saudi Arabia; mgadir@kfu.edu.sa (M.N.A.); malfaiad@kfu.edu.sa (M.A.A.-F.); 219041496@student.kfu.edu.sa (A.M.A.-A.)
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Snippet	Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The...
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SubjectTerms	Additives Aggregates Algorithms By products Cement Civil engineering Compressive strength Concrete Construction industry Fly ash Gene expression Grout Industrial wastes Investigations Laboratories Landfill Mathematical models Natural resources Neural networks Parameter sensitivity Particle swarm optimization Performance indices Polyethylene terephthalate Portland cements Radiation Reinforced concrete Sensitivity analysis Silica fume Support vector machines Swarm intelligence
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Title	Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models
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