Performance Modeling of 3D Printed Structural Beams Using Low-Carbon Cementitious Composites

The integration of low-carbon cementitious composites with 3D printing technology offers a promising pathway toward reducing the environmental impact of construction while maintaining structural integrity and performance. This study presents a comprehensive review of performance modeling strategies for 3D printed structural beams fabricated using low-carbon cementitious materials, focusing on simulation-based optimization and artificial intelligence (AI) applications. With the construction industry accounting for nearly 38% of global CO₂ emissions, there is a critical need to develop and assess sustainable materials and digital fabrication methods that support global carbon-reduction targets. The paper evaluates a range of low-carbon cementitious composites, including fly ash-based geopolymer blends, recycled aggregate concretes, and limestone calcined clay cement (LC³), analyzing their mechanical properties, printability, and environmental profiles. Emphasis is placed on how rheology, buildability, and interlayer bonding influence structural beam performance under flexural, compressive, and dynamic loads. Advanced numerical simulations including finite element analysis (FEA), topology optimization, and parametric design tools are reviewed for their role in predicting deformation, stress distribution, and failure modes in 3D printed beam geometries. Furthermore, the review explores emerging AI and machine learning frameworks used to predict mechanical behavior and optimize mix designs based on large datasets of material compositions and structural parameters. AI models such as artificial neural networks (ANN), support vector machines (SVM), and random forest regression are assessed for their accuracy and generalizability in structural prediction and lifecycle performance modeling. Case studies from recent experimental and computational research are synthesized to highlight best practices, practical challenges, and gaps in current modeling approaches. The review concludes by identifying opportunities for hybrid digital-physical testing environments, real-time monitoring, and sustainable design automation. By bridging material science, structural engineering, and digital fabrication, this work advances a high-performance, low-carbon framework for the next generation of sustainable construction components.