The use of artificial neural network to evaluate insulation thickness and life cycle costs: Pipe insulation application

• Artificial neural network (ANN) usage for predicting optimum insulation thickness (OIT) of pipe.
• The data are collected from each insulation markets and life cycle cost (LCC) analysis results.
• The collected and calculated values are used at the design of ANN in Matlab.
• ANN is to quickly predict the OIT and LCCs of pipe insulation with good accuracy.
• The OIT is determined by knowing input values of ANN only in any location on World.

This paper reports on the use of artificial neural networks (ANNs) to predict insulation thickness and life cycle costs (LCCs) for pipe insulation applications. Data were collected from insulation markets and some data calculated by using LCC analysis. Using the collected data set and LCC analysis results for training, a three-layer feedforward ANN model based on a backpropagation algorithm was developed. This model was used for predicting optimum insulation thickness, total cost, cost saving and payback period. The effects on the predicted parameter of heating degree-days are discussed in detail. The results show that the network yields a maximum correlation coefficient with minimum mean absolute relative error and root mean square error. The developed ANN model has a very practical use of determining the optimum thickness of insulation for any location in the world when just the input parameters of the ANN model are known.