Optimization of thermal characteristics of industrial structures using Analytic Network Process (ANP)

The ever-increasing energy consumption rate in the construction industry has prompted structural engineers and designers to explore innovative ways to reduce energy consumption throughout the construction-to-demolition cycle. To achieve this, improving the thermal and mechanical characteristics of structural and non-structural elements, along with expanding the application of these materials, is paramount. This approach significantly reduces energy consumption and minimizes harmful effects, aligning with the goals of sustainable development. The serviceability characteristics of a structure depend on several parameters that evaluate the thermal behavior of materials. Dynamic heat-transfer analyses of structural components play a critical role in designing energy-efficient new buildings. Thermal conductivity is a key dynamic parameter worth mentioning. However, the thermal conductivity of materials is highly dependent on operating temperatures and moisture content, and little information is available on the performance of insulation materials under actual climatic conditions. Furthermore, temperature profiles in materials are a function of the inside and outside temperatures and thermo-physical properties of the materials. When a heat wave strikes the outer surface of a wall, it travels through the wall and deforms based on the material properties before reaching the inner surface. This phenomenon is referred to as "time lag" and is a critical factor in understanding the thermal behavior of building materials. This study employs an advanced network analysis to determine the most energy-efficient structural panel and steel for constructing the structural components. The selection of the optimal structure is based on key criteria such as thermal conductivity of the panels, time lag, weight of the structure, and cost. By utilizing the Analytic Network Process (ANP) method, the best energy-efficient structure can be chosen. To calculate these parameters, a three-span silo was simulated in the SAP2000V19.2 software.