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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Vydáno v:Applied sciences Ročník 10; číslo 20; s. 7330
Hlavní autoři: Farooq, Furqan, Nasir Amin, Muhammad, Khan, Kaffayatullah, Rehan Sadiq, Muhammad, Faisal Javed, Muhammad Faisal, Aslam, Fahid, Alyousef, Rayed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2020
Témata:
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
Pakistan
ISSN:2076-3417, 2076-3417
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Shrnutí: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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ISSN:2076-3417
2076-3417
DOI:10.3390/app10207330