Research on Sustainable Material Selection and Optimization Design for Eco-Art

Current material selection and combination design methods in eco-art are often limited by single evaluation indicators, weak integration of environmental performance with artistic expression, and insufficient systematic optimization. This paper proposes an integrated multidimensional assessment and intelligent optimization framework for sustainable material selection in eco-art. The framework evaluates environmental indicators such as carbon emission, resource consumption, and recyclability; functional indicators such as structural performance and processing performance; and artistic adaptability indicators such as color, texture, and visual harmony. A multi-objective optimization model is then established with environmental-load minimization and comprehensive-performance maximization as optimization objectives. A genetic algorithm is used to determine material combination ratios and important parameters, including density, porosity, and surface-treatment coefficient. Experimental results show that the optimized scheme C4 reduces carbon emissions to 165 kg CO2/m3, representing a 21.4% reduction compared with the highest-emission scheme, while the resource consumption index decreases to 0.55. The structural stability index reaches 0.76, and the overall color-texture score rises to 4.38. In addition to eco-art applications, the framework offers methodological support for sustainable fiber-based and multifunctional materials, including future electromagnetic material characterization where environmental performance, structure, and functional response must be jointly optimized.