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Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature
- Source :
- Materials, Materials, Vol 14, Iss 1983, p 1983 (2021), Volume 14, Issue 8
- Publication Year :
- 2021
-
Abstract
- Supervised learning algorithms are a recent trend for the prediction of mechanical properties of concrete. This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, based on the experimental data of 207 tests. The cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature were used as inputs for the models’ development. The performance of the AdaBoost, RF, and DT models are assessed using statistical indices, including the coefficient of determination (R2), root mean squared error-observations standard deviation ratio (RSR), mean absolute percentage error, and relative root mean square error. The applications of the above-mentioned approach for predicting the compressive strength of concrete at high temperature are compared with each other, and also to the artificial neural network and adaptive neuro-fuzzy inference system models described in the literature, to demonstrate the suitability of using the supervised learning methods for modeling to predict the compressive strength at high temperature. The results indicated a strong correlation between experimental and predicted values, with R2 above 0.9 and RSR lower than 0.5 during the learning and testing phases for the AdaBoost model. Moreover, the cement content in the mix was revealed as the most sensitive parameter by sensitivity analysis.
- Subjects :
- Mean squared error
0211 other engineering and technologies
020101 civil engineering
02 engineering and technology
lcsh:Technology
Article
Standard deviation
0201 civil engineering
high temperature
sensitivity analysis
021105 building & construction
General Materials Science
AdaBoost
lcsh:Microscopy
lcsh:QC120-168.85
Mathematics
lcsh:QH201-278.5
Artificial neural network
lcsh:T
Supervised learning
prediction
data mining
compressive strength
Compressive strength
Mean absolute percentage error
Properties of concrete
lcsh:TA1-2040
concrete
lcsh:Descriptive and experimental mechanics
lcsh:Electrical engineering. Electronics. Nuclear engineering
lcsh:Engineering (General). Civil engineering (General)
Biological system
lcsh:TK1-9971
Subjects
Details
- ISSN :
- 19961944
- Volume :
- 14
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- Materials (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....273aaf3cb6dc09d76302a998226c9073