Feasibility of Thermal Analysis of Constructive Systems Using the Ahp-Gaussian Method

Abstract

The low energy efficiency of buildings highlights the need for materials that bring advances in this area. Therefore, this work aims to analyze the thermal viability of walls in EPS core panels in Brazilian bioclimatic zones. For this, the software EnergyPlus 9.4 was used to analyze and compare this material with that of ceramic blocks, and the AHP-Gaussian method for decision-making on the most viable systems. Several scenarios were simulated, and the results indicated that in hot zones, even for low solar absorptance, the masonry presented better performance, with the EPS presenting 39.95% more thermal load consumption. On the other hand, in cold areas, EPS core panels were among the best solutions, depending on their characteristics. Finally, the study identifies the feasibility and impacts of the analyzed systems on energy efficiency, performance, and thermal comfort of affordable housing and the benefits of the AHP-Gaussian method in decision-making in studies in this area.

Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

“…The method starts with the basic principle that normal events occur following the normal curve; thus, the probability density function of occurrence of a given performance, in an alternative criterion, follows Gaussian logic, aiming to create a new approach to the original AHP method, which is based on a sensitivity analysis derived from the Gaussian factor [45].…”

“…The research of [44][45][46] clearly describes how it is possible to carry out a decisionmaking process through the AHP-Gaussian model as well as other aspects related to MCDMs. Still, the MCDM approach and the work of [34] use the AHP and MOORA decision-making models for a hybrid approach to a decision support problem involving smart sensors, in line with the objective intended by the current work.…”

“…The AHP-Gaussian approach can be used in a variety of industries and applications, such as engineering, finance, and healthcare. It can help decision makers to analyze complex problems, evaluate different alternatives, and make informed decisions based on data and the relative importance of different criteria [45].…”