A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC)
Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters...
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| Published in: | Applied sciences Vol. 10; no. 20; p. 7330 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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MDPI AG
01.10.2020
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R2 = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model. |
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| AbstractList | Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R2 = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model. Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient [R.sup.2] = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model. Keywords: strength concrete; prediction; genetic engineering programming |
| Audience | Academic |
| Author | Alyousef, Rayed Nasir Amin, Muhammad Rehan Sadiq, Muhammad Faisal Javed, Muhammad Faisal Khan, Kaffayatullah Farooq, Furqan Aslam, Fahid |
| Author_xml | – sequence: 1 givenname: Furqan surname: Farooq fullname: Farooq, Furqan – sequence: 2 givenname: Muhammad orcidid: 0000-0001-6524-4389 surname: Nasir Amin fullname: Nasir Amin, Muhammad – sequence: 3 givenname: Kaffayatullah orcidid: 0000-0001-7994-4642 surname: Khan fullname: Khan, Kaffayatullah – sequence: 4 givenname: Muhammad surname: Rehan Sadiq fullname: Rehan Sadiq, Muhammad – sequence: 5 givenname: Muhammad Faisal orcidid: 0000-0001-5478-9324 surname: Faisal Javed fullname: Faisal Javed, Muhammad Faisal – sequence: 6 givenname: Fahid orcidid: 0000-0003-2863-3283 surname: Aslam fullname: Aslam, Fahid – sequence: 7 givenname: Rayed orcidid: 0000-0002-3821-5491 surname: Alyousef fullname: Alyousef, Rayed |
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| SubjectTerms | Algorithms Civil engineering Concrete Construction industry Datasets Gene expression Genetic engineering genetic engineering programming High strength concrete Machine learning Mechanical properties prediction Prediction theory Research methodology strength concrete Structure |
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