A Novel AI-Based Thermal Conductivity Predictor in the Insulation Performance Analysis of Signal-Transmissive Wall

Abstract

It is well known that thermal conductivity measurement is a challenging task, due to the weaknesses of the traditional methods, such as the high cost, complex data analysis, and limitations of sample size. Nowadays, the requirement of quality of life and tightening energy efficiency regulations of buildings promote the demand for new construction materials. However, limited by the size and inhomogeneous structure, the thermal conductivity measurement of wall samples becomes a demanding topic. Additionally, we find the thermal parameter values of the samples measured in the laboratory are different from those obtained by theoretical computation. In this paper, a novel signal-transmissive wall is designed to provide the problem solving of signal connectivity in 5G. We further propose a new thermal conductivity predictor based on the Harmony Search (HS) algorithm to estimate the thermal properties of laboratory-made wall samples. The advantages of our approach over the conventional methods are simplicity and robustness, which can be generalized to a wide range of solid samples in the laboratory measurement.

Keywords

• thermal conductivity
• specific heat
• artificial intelligence
• harmony search
• optimization methods
• large sample measurement