Artificial intelligence-driven logic reconstruction and generative design framework for architectural conceptual design

Traditional architectural conceptual design is composed of the designer's subjective thinking and linear deductive mechanism, which cannot cope with new requirements such as complexity, multi-objective, immediacy, and continuity. It lacks high efficiency and limited ability to optimize design schemes. This article is based on AI technology design methods such as Generative Adversarial Networks (GANs), Deep Reinforcement Learning (DRL), and Diffusion Models, and establishes a design logic reconstruction framework that includes data-driven, multi-dimensional collaboration, intelligent optimization, and human-machine interaction fusion. Construct corresponding mathematical models using structured design input parameters to complete the complete design path of "requirement semantics → feature extraction → AI generation → performance evaluation → feedback iteration." Experiments have shown that the working time of AI systems has been reduced from 4 days to 6 hours, the number of solutions generated by AI has increased threefold, energy savings have increased by 23.5%, and ventilation and natural lighting have been improved by 18% and 32%, respectively. This method significantly improves the efficiency, scientificity, and environmental performance of architectural design, achieving a shift from conceptual design based conceptual design experience mode to algorithm based logical mode.