发表机构
Politecnico di Milano; Bundesanstalt für Materialforschung und -prüfung (BAM)（米兰理工大学; 联邦材料研究与测试研究所（BAM））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出几何感知的深度学习-FEM框架，结合ShapeGen3DCP与层激活FEM，证实丝材几何对3D混凝土打印可建造性的关键影响，为选择高效准确的几何表示提供实用指导。
AI 中文摘要
沉积丝材的几何形态会显著影响3D混凝土打印（3DCP）结构的结构性能与稳定性。然而，大多数基于有限元（FEM）的可建造性评估方法将打印层表示为简化的矩形，这可能会限制预测精度。本研究提出一种几何感知建模框架，将基于深度学习的丝材形状预测工具ShapeGen3DCP与层激活FEM方法相结合，以研究真实丝材几何对可建造性的影响。该框架可直接从材料与工艺参数生成几何感知数值模型，无需进行实验丝材表征或计算密集型流体流动模拟。通过实验数据验证及对直线墙体的参数研究表明，挤出参数及由此产生的丝材几何会显著影响可建造性预测；真实丝材表示对自由流动沉积尤为重要，而压层策略对几何简化的敏感性较低。在所研究的表示形式中，椭圆近似在几何保真度与建模简洁性之间提供了有效平衡；若需采用矩形表示以生成规则计算网格实现更快模拟，基于体积守恒定义矩形尺寸，相比使用最大丝材宽度或层间接触宽度校准，可提升预测可靠性。总体而言，所提方法证明了将丝材几何纳入3DCP模拟的重要性，并为选择高效、准确的可建造性评估几何表示提供了实用指导。
英文摘要
The geometric morphology of deposited filaments can significantly influence the structural performance and stability of 3D concrete-printed (3DCP) structures. However, most finite element (FEM)-based approaches for buildability assessment represent printed layers as simplified rectangles, potentially limiting predictive accuracy. This study proposes a geometry-informed modelling framework that integrates the deep-learning-based filament shape prediction tool ShapeGen3DCP with a layer-activation FEM approach to investigate the effect of realistic filament geometries on buildability. The framework generates geometry-aware numerical models directly from material and process parameters, eliminating the need for experimental filament characterization or computationally intensive fluid-flow simulations. Validation against experimental data and a parametric study of rectilinear walls demonstrate that extrusion parameters and the resulting filament geometry can significantly influence buildability predictions. Realistic filament representations are particularly important for free-flow deposition, whereas layer-pressing strategies are less sensitive to geometric simplifications. Among the investigated representations, an elliptical approximation provides an effective balance between geometric fidelity and modelling simplicity. When rectangular representations are preferred to enable regular computational meshes for faster simulations, defining their dimensions based on volume conservation improves prediction reliability compared with calibrating them using either the maximum filament width or the interlayer contact width. Overall, the proposed methodology demonstrates the importance of incorporating filament geometry into 3DCP simulations and provides practical guidance for selecting efficient and accurate geometric representations for buildability assessment.
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