AI-Driven Generative Design for Next-Generation 3D Concrete Printing in Architecture: State of the Art

Abstract

The convergence of artificial intelligence (AI) and 3D concrete printing (3DCP) heralds a paradigm shift in architectural design and construction, blending computational innovation with sustainable practices. This study synthesizes the state of the art in AI-driven generative design for 3DCP, examining cutting-edge methodologies such as generative adversarial networks (GANs), topology optimization, reinforcement learning (RL), and digital twins. These technologies collectively address longstanding challenges in material efficiency, structural robustness, and ecological impact by automating design exploration, real-time process control, and lifecycle management. The integration of AI with 3DCP enables unprecedented geometric complexity, adaptive fabrication, and smart city alignment, yet barriers in scalability, regulatory frameworks, and material standardization persist. By critically evaluating advancements in ChatGPT-aided ideation, physics-informed simulations, and IoT-enabled digital twins, this research maps a holistic framework for AI-augmented 3DCP. The paper underscores the transformative potential of AI in redefining architectural workflows, advocating for interdisciplinary collaboration to bridge computational creativity, ethical governance, and sustainable urban development.