3D concrete printing (3DCP) aims to transform construction by increasing automation, reducing material usage, and enabling customized designs. However, the lack of regulations and reliance on trial-and-error methods lead to significant waste and inefficiencies. Reliable models are essential to predict and control the complex printing process, which is influenced by various influencing factors from material, process, and environment. This study proposes a structural modelling framework to predict print stability and buildability in extrusion-based 3DCP, integrating environmental influences – particularly temperature – and process-related effects on the layer geometry. Ambient temperature affects the development of material stiffness and strength, while print process parameters alter layer geometry; both factors impact the stability in the fresh state but are often neglected or simplified in numerical approaches. The proposed structural modelling framework builds on [1] and employs an elastoplastic material law with nonlinear hardening for a better approximation of the fresh concrete behavior observed experimentally. Layers are activated sequentially using a pseudo-density approach, and a co-rotational formulation captures geometrical nonlinear effects for buckling failure. Material parameters evolve linearly in time to reflect structural build-up and hydration. First, temperature effects are incorporated via the Maturity approach, resulting in time- and temperature-dependent material properties. Second, realistic layer geometry is introduced through a fast machine learning model [2] that predicts the filament cross-sectional shape based on material and process parameters, enabling accurate mesh generation for structural simulation. The method is demonstrated through examples of printed walls and cylinders with varying dimensions. A sensitivity study highlights the influence of ambient temperature and realistic layer geometry on structural stability, providing insights into improving predictive accuracy for 3DCP. REFERENCES [1] R.J. Wolfs, F.P. Bos and T.A. Salet. Early age mechanical behaviour of 3d printed concrete: numerical modelling and experimental testing. Cement and Concrete Research 106, 103–116. https://doi.org/10.1016/j.cemconres.2018.02.001 [2] G. Rizzieri, F. Lanteri, L. Ferrara and M. Cremonesi. ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing. arXiv, 2025. https://arxiv.org/abs/2510.0200