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

Mahmood Ahmad; Ji-Lei Hu; Feezan Ahmad; Xiao-Wei Tang; Maaz Amjad; Muhammad Junaid Iqbal; Muhammad Asim; Asim Farooq

doi:10.3390/ma14081983

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

Materials (Basel). 2021 Apr 15;14(8):1983. doi: 10.3390/ma14081983.

Authors

Mahmood Ahmad¹, Ji-Lei Hu², Feezan Ahmad³, Xiao-Wei Tang³, Maaz Amjad⁴, Muhammad Junaid Iqbal¹, Muhammad Asim¹, Asim Farooq⁵

Affiliations

¹ Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan.
² College of Civil Engineering and Architecture, China Three Gogres University, Yichang 443002, China.
³ State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China.
⁴ Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan.
⁵ Department of Transportation Engineering, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur 22620, Pakistan.

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.

Keywords: compressive strength; concrete; data mining; high temperature; prediction; sensitivity analysis.

Abstract

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