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

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

doi:10.3390/ma14081983

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.

Highlights

Concrete is one of the most versatile materials used in the construction of buildings, subway systems, and many other civil engineering structures
Some examples of concrete structure that are vulnerable to high temperature include industrial structures, such as chimneys working at high temperature, as well as factories dealing with chemicals with high fire risk [1]
“−” represents that this performance statistic is not included in the reference

Summary

Introduction

Concrete is one of the most versatile materials used in the construction of buildings, subway systems, and many other civil engineering structures. With the rapid development of urbanization, the demand for structural concrete is increasing. As a core aspect of these structures, concrete may encounter aberrant results such as abrasion, freezing, and chemical erosion during the whole life of the structure. One of the aberrant results is high temperature and fire. Some examples of concrete structure that are vulnerable to high temperature include industrial structures, such as chimneys working at high temperature, as well as factories dealing with chemicals with high fire risk [1]. The fire causes the concrete temperature in the concrete structure to be extremely high. If the concrete surface reaches above 100 ◦ C, it can be observed that heat transfer can increase the internal temperature of concrete to 300–700 ◦ C [2]

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Results

Conclusion