Table of Contents
Advances in Artificial Neural Systems
Volume 2015, Article ID 421215, 9 pages
Research Article

Artificial Neural Network Estimation of Thermal Insulation Value of Children’s School Wear in Kuwait Classroom

1Technical Affairs Section, Civil Defense General Administration, 47760 Al Zahra, Kuwait
2Department of Chemical Engineering, Public Authority of Applied Education and Training, College of Technological Studies, 70654 Shuwaikh, Kuwait

Received 8 May 2015; Accepted 14 September 2015

Academic Editor: Ozgur Kisi

Copyright © 2015 Khaled Al-Rashidi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Artificial neural network (ANN) was utilized to predict the thermal insulation values of children’s school wear in Kuwait. The input thermal insulation data of the different children’s school wear used in Kuwait classrooms were obtained from study using thermal manikins. The lowest mean squared error (MSE) value for the validation data was 1.5 × 10−5 using one hidden layer of six neurons and one output layer. The R2 values for the training, validation, and testing data were almost equal to 1. The values from ANN prediction were compared with McCullough’s equation and the standard tables’ methods. Results suggested that the ANN is able to give more accurate prediction of the clothing thermal insulation values than the regression equation and the standard tables methods. The effect of the different input variables on the thermal insulation value was examined using Garson algorithm and sensitivity analysis and it was found that the cloths weight, the body surface area nude (BSA0), and body surface area covered by one layer of clothing (BSAC1) have the highest effect on the thermal insulation value with about 29%, 27%, and 23%, respectively.