Development of an Optimized Computer-Aided Design Model With Ecological Sustainability

Environmental design faces challenges like high resource consumption and environmental impact, requiring new technologies to guide sustainable design. This study explored the use of neural network (NN) technology to optimize computer-aided design models for ecological sustainability. A deep neural network prediction model was constructed and trained using data on building energy consumption, material properties, and ecological factors. The trained model was applied to environmental design projects to optimize energy efficiency, material selection, and ecological integration. The results showed that NN-assisted computer-aided design optimization significantly improved ecological indicators, such as energy efficiency and material recycling, while maintaining aesthetics, functionality, and cost-effectiveness. This research demonstrates the potential for NNs in environmental design and offers a valuable tool for sustainable design practices.