Keywords: Model repository, Dataset, UML, Open models, Curation, Community, Education, Machine learning
Astract: Datasets of Unified Modeling Language (UML) models are becoming increasingly valuable for education, empirical research, and tool development in model-driven engineering (MDE) and conceptual modeling. In recent years, several datasets have emerged - mostly compiled through automated crawling of open platforms such as GitHub and GenMyModel. While these efforts have improved access to real-world modeling artifacts, the resulting collections often suffer from serious quality issues: they include syntactically invalid models, semantically incorrect structures, and placeholder or dummy content. Moreover, most models are not accompanied by textual domain descriptions, which are essential for understanding the intent behind the model and assessing its semantic soundness. Therefore these model datasets are far from ideal as a source for modeling exercises or empirical MDE research. This paper presents an initial step toward a community-curated golden dataset of UML models, designed to address these limitations. Our contribution includes i) a curated set of UML models, each paired with a natural language description of the modeled domain requirements, ii) a publicly accessible web platform for exploring and querying the dataset, and iii) a structured process for community-based contribution and evaluation to support sustainable growth and quality assurance of the dataset. By fostering community involvement and providing high-quality, semantically grounded models, this work lays the foundation for a widely accepted benchmark dataset in UML-based research and education.

Keywords: Construction Management, Tunnelling, Process mining, Documentation, Data analysis
Astract: Conventional tunnel construction often relies on manual methods of construction process analysis, using tools such as paper-based cycle diagrams or spreadsheets, which lack immediate updates and capabilities, limiting performance evaluation, communication, and decision-making. As a result, moving to a fully digital process incorporating business intelligence capabilities can deliver benefits by improving data-driven decision-making, operational efficiency and resource allocation. This paper presents a case study using construction documentation to evaluate the applicability of data and process analytics in conventional tunnelling. We also present a novel approach to visualising and analysing construction sequence deviations. The study demonstrates how data and process analysis can be utilised to evaluate the activity sequences, the duration of single activities, advance rates, and general project performance. By adhering to established industry standards, this research examines the practical implementation of data analysis methods in operational tunnelling environments, contributing to the development of integrated digital workflows.

Keywords: Conventional tunneling, Drill and blast, BIM, IFC, Construction phase, Digitalization
Astract: Building Information Modeling (BIM) has become the key technology for the digital transformation of the architecture, engineering, and construction (AEC) sector. The Industry Foundation Classes (IFC), as an open and vendor-neutral data model, play an essential role in this transformation. Despite all the recent advances of BIM, tunneling needs to catch up with other AEC sectors adopting digital technologies. Currently, IFC is mainly used to capture information directly connected to the structural elements of the final tunnel but rarely as a means to document the tunnel excavation and support. This article proposes an IFC-driven process for the execution phase of conventional tunneling projects by extending the usage of IFC to information about the process, labor, equipment, and employed material. The proposed process is evaluated by a case study demonstrating how to represent data from a conventional tunneling project by IFC. The results show that IFC provides the necessary concepts to express the data of the execution phase of conventional tunneling projects. Implementing an IFC-driven process in this phase significantly contributes to the digital transformation of tunneling projects.

Keywords: BIM (Building Information Modeling), IFC (Industry Foundation Classes)

Keywords: BIM, Construction phase, Digitalization, Equipment, IFC, Labor, Material, Process, Tunneling
Astract: The documentation process of conventional tunneling projects is time-consuming and costly. Building Information Modeling (BIM) has enabled substantial productivity gains in the Architecture, Engineering, & Construction (AEC) sector. However, BIM has only been marginally adopted in the execution phase of conventional tunneling projects. For this purpose, we propose a BIM model that facilitates fully digital and automated data exchange between project stakeholders. We use the Industry Foundation Classes (IFC) as a basis and identify concepts potentially useful to represent data from the execution phase of construction projects. We demonstrate how IFC concepts are utilized to represent a shift report of a conventional tunneling project. Thereby, we deliver a reference model as an implementation guide for software developers in this domain. This may serve as a blueprint for handling construction management data in a machine-readable format, laying the foundations for Big Open BIM in the execution phase of construction projects.