Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete
Compressive strength is one of the important property of concrete and depends on many factors. Most of the concrete compressive strength predictive models mainly rely on available literature data, which are too simple to consider all the contributing factors. This study adopted a new approach to pre...
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MDPI AG
2020-08-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/10/9/737 |
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| author | Muhammad Faisal Javed Muhammad Nasir Amin Muhammad Izhar Shah Kaffayatullah Khan Bawar Iftikhar Furqan Farooq Fahid Aslam Rayed Alyousef Hisham Alabduljabbar |
| author_facet | Muhammad Faisal Javed Muhammad Nasir Amin Muhammad Izhar Shah Kaffayatullah Khan Bawar Iftikhar Furqan Farooq Fahid Aslam Rayed Alyousef Hisham Alabduljabbar |
| author_sort | Muhammad Faisal Javed |
| collection | DOAJ |
| description | Compressive strength is one of the important property of concrete and depends on many factors. Most of the concrete compressive strength predictive models mainly rely on available literature data, which are too simple to consider all the contributing factors. This study adopted a new approach to predict the compressive strength of sugarcane bagasse ash concrete (SCBAC). A vast amount of data from the literature study and fifteen laboratory tested concrete samples with different dosage of bagasse ash, were respectively used to calibrate and validate the models. The novel Gene Expression Programming, Multiple Linear Regression and Multiple Non-Linear Regression were used to model SCBAC compressive strength. The water cement ratio, bagasse ash percent replacement, quantity of fine and coarse aggregate and cement content were used as an input for models development. Various statistical indicators, i.e., NSE, R<sup>2</sup> and RMSE were used to assess the performance of the models. The results indicated a strong correlation between observed and predicted values with NSE and R<sup>2</sup> both above 0.8 during calibration and validation for the Gene Expression Programming (GEP). The outcomes from GEP outclassed all the models to predict SCBAC compressive strength. The validity of the model is further verified using data of fifteen tests conducted in the laboratory. Moreover, the cement content in the mix was revealed as the most sensitive parameter followed by water cement ratio form sensitivity analysis. The GEP fulfilled all the criteria for external validity. The simple formulae derived in this study could be used reliably for the prediction of SCBAC compressive strength. |
| first_indexed | 2024-03-10T17:02:53Z |
| format | Article |
| id | doaj.art-7abe107a81df416197e92a3ea899cb2a |
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| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-03-10T17:02:53Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-7abe107a81df416197e92a3ea899cb2a2023-11-20T10:54:14ZengMDPI AGCrystals2073-43522020-08-0110973710.3390/cryst10090737Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based ConcreteMuhammad Faisal Javed0Muhammad Nasir Amin1Muhammad Izhar Shah2Kaffayatullah Khan3Bawar Iftikhar4Furqan Farooq5Fahid Aslam6Rayed Alyousef7Hisham Alabduljabbar8Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, KP PakistanDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P.O. 380, Al-Hofuf, Al Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, KP PakistanDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P.O. 380, Al-Hofuf, Al Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, KP PakistanDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, KP 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 ArabiaDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaCompressive strength is one of the important property of concrete and depends on many factors. Most of the concrete compressive strength predictive models mainly rely on available literature data, which are too simple to consider all the contributing factors. This study adopted a new approach to predict the compressive strength of sugarcane bagasse ash concrete (SCBAC). A vast amount of data from the literature study and fifteen laboratory tested concrete samples with different dosage of bagasse ash, were respectively used to calibrate and validate the models. The novel Gene Expression Programming, Multiple Linear Regression and Multiple Non-Linear Regression were used to model SCBAC compressive strength. The water cement ratio, bagasse ash percent replacement, quantity of fine and coarse aggregate and cement content were used as an input for models development. Various statistical indicators, i.e., NSE, R<sup>2</sup> and RMSE were used to assess the performance of the models. The results indicated a strong correlation between observed and predicted values with NSE and R<sup>2</sup> both above 0.8 during calibration and validation for the Gene Expression Programming (GEP). The outcomes from GEP outclassed all the models to predict SCBAC compressive strength. The validity of the model is further verified using data of fifteen tests conducted in the laboratory. Moreover, the cement content in the mix was revealed as the most sensitive parameter followed by water cement ratio form sensitivity analysis. The GEP fulfilled all the criteria for external validity. The simple formulae derived in this study could be used reliably for the prediction of SCBAC compressive strength.https://www.mdpi.com/2073-4352/10/9/737sugarcane bagasse ashsensitivity analysiscompressive strengthgene expression programmingmultiple linear and non-linear regressiongreen concrete |
| spellingShingle | Muhammad Faisal Javed Muhammad Nasir Amin Muhammad Izhar Shah Kaffayatullah Khan Bawar Iftikhar Furqan Farooq Fahid Aslam Rayed Alyousef Hisham Alabduljabbar Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete Crystals sugarcane bagasse ash sensitivity analysis compressive strength gene expression programming multiple linear and non-linear regression green concrete |
| title | Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete |
| title_full | Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete |
| title_fullStr | Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete |
| title_full_unstemmed | Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete |
| title_short | Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete |
| title_sort | applications of gene expression programming and regression techniques for estimating compressive strength of bagasse ash based concrete |
| topic | sugarcane bagasse ash sensitivity analysis compressive strength gene expression programming multiple linear and non-linear regression green concrete |
| url | https://www.mdpi.com/2073-4352/10/9/737 |
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