Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete
The experimental design of high-strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python-based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modellin...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8850535 |
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| author | Fahid Aslam Furqan Farooq Muhammad Nasir Amin Kaffayatullah Khan Abdul Waheed Arslan Akbar Muhammad Faisal Javed Rayed Alyousef Hisham Alabdulijabbar |
| author_facet | Fahid Aslam Furqan Farooq Muhammad Nasir Amin Kaffayatullah Khan Abdul Waheed Arslan Akbar Muhammad Faisal Javed Rayed Alyousef Hisham Alabdulijabbar |
| author_sort | Fahid Aslam |
| collection | DOAJ |
| description | The experimental design of high-strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python-based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect. |
| format | Article |
| id | doaj-art-6c6465ceda4e447fbdba082349c0b64a |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-6c6465ceda4e447fbdba082349c0b64a2025-02-03T01:24:57ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88505358850535Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength ConcreteFahid Aslam0Furqan Farooq1Muhammad Nasir Amin2Kaffayatullah Khan3Abdul Waheed4Arslan Akbar5Muhammad Faisal Javed6Rayed Alyousef7Hisham Alabdulijabbar8Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P.O. Box 380, Al-Hofuf, Al Ahsa 31982, Saudi ArabiaDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University (KFU), P.O. Box 380, Al-Hofuf, Al Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Architecture and Civil Engineering, City University of Hong Kong, Kowloon 999077, Hong KongDepartment of Civil Engineering, COMSATS University Islamabad, 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 ArabiaThe experimental design of high-strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python-based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.http://dx.doi.org/10.1155/2020/8850535 |
| spellingShingle | Fahid Aslam Furqan Farooq Muhammad Nasir Amin Kaffayatullah Khan Abdul Waheed Arslan Akbar Muhammad Faisal Javed Rayed Alyousef Hisham Alabdulijabbar Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete Advances in Civil Engineering |
| title | Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete |
| title_full | Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete |
| title_fullStr | Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete |
| title_full_unstemmed | Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete |
| title_short | Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete |
| title_sort | applications of gene expression programming for estimating compressive strength of high strength concrete |
| url | http://dx.doi.org/10.1155/2020/8850535 |
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