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

Abstract

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.