Artificial Intelligence in 3D Printed Concrete: Sustainability Assessment and Implementation Challenges

Abstract

The integration of Artificial Intelligence (AI) into 3D Printed Concrete (3DPC) is reshaping sustainable construction by enabling real-time optimization, automation, and environmental performance gains. Amid escalating climate change, urban growth, and resource constraints, AI-enhanced 3DPC offers a pathway to resilient and low-carbon built environments. We examine how machine learning, computer vision, and predictive analytics are transforming 3DPC across material design, process monitoring, and lifecycle assessment. Notably, AI-enabled optimization frameworks have demonstrated up to 60% reductions in material waste and 30% improvements in energy efficiency. We analyze AI-driven advancements in mix proportioning, robotic path planning, and sensor-based quality control, emphasizing how dynamic feedback loops support adaptive manufacturing. Socio-economic implications such as shifts in labor demand and emerging skill requirements are also addressed, underscoring the broader workforce transformation underway in AI-automated construction. Key challenges include limited datasets, opaque algorithmic decision-making, integration difficulties in complex site conditions, and high computational costs. To address these, we propose multi-objective optimization strategies, circular economy approaches, and transparent, human-AI collaborative systems. Our findings reveal that fully realizing the sustainability potential of AI in 3DPC depends on interdisciplinary collaboration, interpretable AI models, and forward-thinking policy support. By aligning technological innovation with regulatory frameworks and human expertise, AI-powered 3DPC can serve as a cornerstone for next-generation construction enabling greener, faster, and more inclusive infrastructure development worldwide.