Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest
Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programmin...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6618407 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550081550614528 |
|---|---|
| author | Mohsin Ali Khan Shazim Ali Memon Furqan Farooq Muhammad Faisal Javed Fahid Aslam Rayed Alyousef |
| author_facet | Mohsin Ali Khan Shazim Ali Memon Furqan Farooq Muhammad Faisal Javed Fahid Aslam Rayed Alyousef |
| author_sort | Mohsin Ali Khan |
| collection | DOAJ |
| description | Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature T, curing time t, age of the specimen A, the molarity of NaOH solution M, percent SiO2 solids to water ratio % S/W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate ( % AG), fine aggregate to the total aggregate ratio F/AG, sodium oxide (Na2O) to water ratio N/W in Na2SiO3 solution, alkali or activator to the FA ratio AL/FA, Na2SiO3 to NaOH ratio Ns/No, percent plasticizer (% P), and extra water added as percent FA EW%. RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions. |
| format | Article |
| id | doaj-art-78ee2c5d1ec946e49c9a9c3ee1e98ded |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-78ee2c5d1ec946e49c9a9c3ee1e98ded2025-02-03T06:07:42ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/66184076618407Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random ForestMohsin Ali Khan0Shazim Ali Memon1Furqan Farooq2Muhammad Faisal Javed3Fahid Aslam4Rayed Alyousef5Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, PakistanDepartment of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, KazakhstanDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaFly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature T, curing time t, age of the specimen A, the molarity of NaOH solution M, percent SiO2 solids to water ratio % S/W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate ( % AG), fine aggregate to the total aggregate ratio F/AG, sodium oxide (Na2O) to water ratio N/W in Na2SiO3 solution, alkali or activator to the FA ratio AL/FA, Na2SiO3 to NaOH ratio Ns/No, percent plasticizer (% P), and extra water added as percent FA EW%. RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.http://dx.doi.org/10.1155/2021/6618407 |
| spellingShingle | Mohsin Ali Khan Shazim Ali Memon Furqan Farooq Muhammad Faisal Javed Fahid Aslam Rayed Alyousef Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest Advances in Civil Engineering |
| title | Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest |
| title_full | Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest |
| title_fullStr | Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest |
| title_full_unstemmed | Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest |
| title_short | Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest |
| title_sort | compressive strength of fly ash based geopolymer concrete by gene expression programming and random forest |
| url | http://dx.doi.org/10.1155/2021/6618407 |
| work_keys_str_mv | AT mohsinalikhan compressivestrengthofflyashbasedgeopolymerconcretebygeneexpressionprogrammingandrandomforest AT shazimalimemon compressivestrengthofflyashbasedgeopolymerconcretebygeneexpressionprogrammingandrandomforest AT furqanfarooq compressivestrengthofflyashbasedgeopolymerconcretebygeneexpressionprogrammingandrandomforest AT muhammadfaisaljaved compressivestrengthofflyashbasedgeopolymerconcretebygeneexpressionprogrammingandrandomforest AT fahidaslam compressivestrengthofflyashbasedgeopolymerconcretebygeneexpressionprogrammingandrandomforest AT rayedalyousef compressivestrengthofflyashbasedgeopolymerconcretebygeneexpressionprogrammingandrandomforest |