Effective Prediction of Thermal Conductivity of Concrete Using Neural Network Method

Jong-Han Lee,Jong-Jae Lee,Baik-Soon Cho

doi:10.1007/s40069-012-0016-x

Abstract

The temperature distributions of concrete structures strongly depend on the value of thermal conductivity of concrete. However, the thermal conductivity of concrete varies according to the composition of the constituents and the temperature and moisture conditions of concrete, which cause difficulty in accurately predicting the thermal conductivity value in concrete. For this reason, in this study, back-propagation neural network models on the basis of experimental values carried out by previous researchers have been utilized to effectively account for the influence of these variables. The neural networks were trained by 124 data sets with eleven parameters: nine concrete composition parameters (the ratio of water–cement, the percentage of fine and coarse aggregate, and the unit weight of water, cement, fine aggregate, coarse aggregate, fly ash and silica fume) and two concrete state parameters (the temperature and water content of concrete). Finally, the trained neural network models were evaluated by applying to other 28 measured values not included in the training of the neural networks. The result indicated that the proposed method using a back-propagation neural algorithm was effective at predicting the thermal conductivity of concrete.

Highlights

Many accidental and environmental factors continue to produce and change heat flow within concrete structures.The magnitude of the temperature variance and resulting thermal behaviors primarily depend on the accuracy of thermal conductivity of concrete (TCC)
Since concrete is a composite material composed of water, cement, fine aggregate, coarse aggregate, and other admixtures, the thermal conductivity value changes according to the combination of the concrete compositions including the volume faction and the unit weight of the constituents, and the ratio of water to cement
Based on their data sets, the developed neural network model was trained with regard to eleven parameters: nine parameters representing the composition of concrete constituents, which were the water–cement ratio, the fine aggregate percentage, the coarse aggregate percentage, the unit water weight, the unit cement weight, the unit fine aggregate weight, the unit coarse aggregate weight, the unit fly ash weight, and the unit silica fume weight, and two parameters representing the state of concrete, which were the temperature of the concrete and the water content in the concrete

Summary

Introduction

Many accidental and environmental factors continue to produce and change heat flow within concrete structures. The neural network, a prediction method for the estimation of the TCC, was constructed and trained using 124 experimental data obtained by previous studies (Kim et al 2003; Morabito 1989; Harmathy 1983; Yamazaki et al 1995; Lie and Kodur 1996; Van Geem et al 1997; Khan et al 1998; Khan 2002; Kodur and Sultan 2003) Based on their data sets, the developed neural network model was trained with regard to eleven parameters: nine parameters representing the composition of concrete constituents, which were the water–cement ratio, the fine aggregate percentage, the coarse aggregate percentage, the unit water weight, the unit cement weight, the unit fine aggregate weight, the unit coarse aggregate weight, the unit fly ash weight, and the unit silica fume weight, and two parameters representing the state of concrete, which were the temperature of the concrete and the water content in the concrete. This study demonstrated that the proposed prediction method based on a neural network algorithm could be used as a reliable and effective technique for determining thermal conductivity in the thermal design and analysis of concrete structures

Principles of Neural Network

Structure of Neural Network

Training of Neural Network

Comparison of Estimated and Measured

Findings

Conclusions