## 1 Introduction
Similarly, ontologies have been developed to support manufacturability analysis [19], interoperability for data management [20], lifecycle data management [21], and data provenance in metal-based AM [22]. Furthermore, efforts to advance and couple data modeling in AM with digitalization approaches in the AEC industry, such as building information modeling (BIM), have started to gain attention. In [23], a framework based on fabrication information modeling is developed to integrate AM planning into BIM workflows, considering material, machine, and process parameters. In [24], a methodology for knowledge-driven decision support systems based on BIM concepts is developed and represented as an application ontology. Integrating lean production principles into the concrete printing process has also been discussed as an approach to address needs regarding bidirectional information flows and databases for design-to-construction workflows in the AM of concrete structures [25]. Current AM data models, however, assume materials that can be controlled by assuring constant process settings (e.g., print speed), well-defined material properties (e.g., grain size), and controlled environmental conditions (e.g., temperature), which differs from the reality of extrusion-based concrete printing [26]. When considering cementitious materials, which present time-dependent rheological behavior, material-process interactions are crucial when defining process settings and toolpaths, as discussed in [15]. Furthermore, the material properties of concrete change over time, having a detrimental effect on the quality of the printed components when material property variations are unaccounted for during manufacturing. Material-process interactions affecting the overall structural properties of printed components are still to be seamlessly integrated into the data modeling approaches used in the digital workflow in the AM of concrete structures to ensure the success of the concrete printing project. Current data modeling approaches limit the AM of concrete structures to a process with a long trial-and-error learning curve to determine the ideal process and material parameters. Therefore, semantic descriptions are necessary to advance data modeling approaches for the AM of concrete structures, formalizing material-process interactions. Also, new data models are required, enabling smoother digital workflows, describing AM input parameters, and allowing the monitoring of material-process interactions in real time. This study aims at formally describing the digital workflow, the input parameters, as well as interactions between the parameters in concrete printing, following a semantic modeling approach referred to as \"printing information modeling\" (PIM). The PIM approach represents a step necessary to standardize process information, material information, and geometry information into a unified data model. In this regard, a \"printing information model\", i.e., a semantic model, for extrusion-based AM of concrete structures is proposed, building on previous studies from the authors [27, 28]. A software tool that deploys building information modeling (BIM) concepts is developed to test the printing information model as a formal basis for BIM-based concrete printing. This paper is structured as follows. Section 2 gives an overview of the system and process analysis and requirements, integrating the results of a survey conducted in this study among practitioners and researchers. In Section 3, the proposed printing information model is presented, describing parameters and fundamental material-process interactions for AM of concrete structures. The semantic model is validated via a software tool and an example case. In Section 4, the results are discussed in the context of BIM-based concrete printing. The paper concludes with a summary of this study and an outlook on potential future work.