AI-DRIVEN GENERATIVE DESIGN IN ARCHITECTURE AND STRUCTURAL OPTIMIZATION

Аннотация

Artificial intelligence (AI) has rapidly emerged as a disruptive force reshaping architectural design and structural engineering practices. AI-driven generative design combines computational creativity with optimization methods to explore vast design spaces efficiently, enabling humans to evolve beyond intuition-bound paradigms. While conventional generative design tools focused on parametric manipulation and heuristic search, the integration of deep learning, probabilistic generative models, and optimization frameworks now empowers the generation of novel architectural forms that satisfy structural, environmental, and performance constraints concurrently. This review synthesizes the state-of-the-art in AI-enhanced generative design and structural optimization, contextualizing advances in algorithmic frameworks, applications in building systems, and the challenges ahead. Key contributions from recent literature are critically examined and organized into thematic segments including theoretical foundations, architectural applications, structural optimization techniques, integration challenges, and future research directions. The paper concludes by identifying emerging trends that will define next-generation AI-centric design ecosystems in architecture and structural engineering.