## 5 Conclusions and Outlook  
The update of a simulation model with real-time data during the construction process enables continuous improvement in the performance prognosis and enables the real-time steering of production processes. This paper presents a concept that facilitates the updating of a simulation model focusing on logistics with quasi-real data during construction, incorporating tunnel-specific knowledge. The first application example demonstrates the improvement in the performance prognosis by integrating the current construction progress and updating the stochastic input parameters for the duration of the core processes of excavation and ringbuild. A significant improvement in the prognosis of the project duration could already be achieved after a project progress of 10%, comparable to other results from the literature. However, the results are a first indication that cannot be generalized, as they are project-specific. Considering the processes individually, an improvement in the predicted to the actual project duration with a deviation of less than 10% cannot be seen before 25% of the project progress. This indicates that there is still a high degree of variability and uncertainty within the investigated processes, especially for the advance process where geological layers have a strong influence on the process behavior. As our logistic simulations do not simulate the physical excavation process of the ground, the average advance speed per soil type is of interest. The coupling of logistic simulations with physical-mechanical excavation simulations should be investigated to further improve the performance predictions. For the integration of actual data into simulations, a database that is available in real time during tunneling is essential. The heterogeneity of data sources in tunneling (machine data, shift reports, etc.) still poses a challenge here. Due to the progressive digitalization of construction sites, digital, open solutions for the organization and storage of heterogeneous data are increasingly being developed. Linked data approaches combined with process simulation during construction should be explored. Figure 15: Deviation in the predicted process durations for the updated ringbuild and advance parameters as average values of the MCS compared to the actual data over the update process [34]. The subsequent research agenda entails the updating of the additional parameters listed in Section 3.2.1, and the investigation of the effects of these on the overall system represents the next research goal. It is imperative to consider the simulation's calculation time as a critical factor. The investigation of data processing methods with the help of artificial intelligence is another worthy research goal. The first approaches to combining artificial intelligence and process simulation approaches already exist. With the increasing development of hard- and software as well as increasing computing capacity, the application of the methods will also become steadily more interesting for real-time simulations. Conceptualization, A.J., M.T. and M.K. ; methodology, A.J. ; software, A.J. ; validation, J.B., M.T. and M.K. ; formal analysis, A.J. ; investigation, A.J. and J.B.; resources, M.T. and M.K. ; data curation, A.J. ; writing--original draft preparation, A.J. ; writing--review and editing, J.B. and M.T. ; visualization, A.J. ; supervision, M.T. and M.K. ; project administration, M.T. and M.K. ; funding acquisition, M.T. and M.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the German Research Foundation (DFG) within subproject C3 of the Collaborative Research Center SFB 837 (Interaction Modeling in mechanized Tunneling). Not applicable. The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.