Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature.

Ayaz Ahmad,Afnan Nafees,Furqan Farooq,Mariusz Maślak,Krzysztof Adam Ostrowski,Imran Mehmood

doi:10.3390/ma14154222

Abstract

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.

Highlights

Due to the fact that concrete has a relatively low cost when compared to other materials, as well as the fact that it is commonly used in engineering structures all over the world, its technology is subjected to constant innovations and improvements [1]
Hao et al [43] used the support vector machine (SVM) and k-fold crossvalidation to predict the compressive strength of concrete in a marine environment, stating that the SVM performs better when compared to the artificial neural network (ANN) and decision tree (DT)
The decision tree (DT) and ANN algorithms were selected from the individual techniques, while the bagging and gradient boosting (GB) regressors were used as ensemble algorithms for forecasting the strength of concrete at high temperatures

Summary

Introduction

Due to the fact that concrete has a relatively low cost when compared to other materials, as well as the fact that it is commonly used in engineering structures all over the world, its technology is subjected to constant innovations and improvements [1]. The fast and advanced development of urbanization requires a high demand for concrete [2], which possesses many desired properties including compressive strength, the ability to adopt any shape, and the capacity to resist environmental conditions [3]. Porosity, impact resistance, fire resistance, durability, and acoustic insulation are cited as being the advantages of concrete [4]. These various aspects enable it to be applied in the construction of infrastructures, dams, tunnels, bridges, and reservoirs [5]. The casting and curing of concrete in hot areas are considered a challenging task to perform, and what is more, concrete loses its mechanical properties (compressive and flexural strength) at high temperature, which results in the loss of its durability [12]

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