Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature

• Published in: Materials Vol. 14; no. 8; p. 1983
• Main Authors: Ahmad, Mahmood, Hu, Ji-Lei, Ahmad, Feezan, Tang, Xiao-Wei, Amjad, Maaz, Iqbal, Muhammad Junaid, Asim, Muhammad, Farooq, Asim
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15-04-2021; MDPI
• Subjects: Adaptive systems; Algorithms; Artificial neural networks; Cement; Compressive strength; Concrete; Construction; data mining; Datasets; Decision trees; Failure analysis; Fly ash; Fuzzy logic; Gene expression; High temperature; Machine learning; Mechanical properties; Modelling; Numerical analysis; Parameter sensitivity; prediction; Root-mean-square errors; Sensitivity analysis; Silica fume; Silicon dioxide; Supervised learning; Temperature effects; compressive strength; concrete; prediction; sensitivity analysis; data mining; high temperature
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma14081983

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 (R
), 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 R
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.