Sewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing.

The deconstruction of a building marks the end of its life cycle. At the same time, this phase offers many opportunities for the acquisition and reuse of used building materials. During the deconstruction phase, planning and execution are carried out using a variety of different software applications. Despite the use of digital 3D models according to the Building Information Modeling (BIM) method, there is no interoperability along the value chain in the deconstruction of a building. To counteract the lack of interoperability in the deconstruction phase of buildings, the article develops an ontology for the formalization of knowledge within the deconstruction phase and the usability of heterogeneous data sets in connection with the 3D BIM model. After assessing the status quo on the topic of BIM in deconstruction and ontologies in building construction, the systematic development of the deconstruction ontology (DCO) and its validation on a demonstrator is conducted. The result is the presentation of the basic usability of ontologies in conjunction with graphical databases and digital 3D models in the deconstruction phase. This ensures interoperability along the value chain and counteracts data loss.

In recent decades, the condition of many bridge structures has deteriorated and the need for maintenance measures has increased. Until now, these maintenance measures have mainly been carried out manually and reactively. The use of digital 3D models based on Building Information Modelling (BIM) can remedy this situation and create the basis for predictive maintenance management. While the generation of 3D models of new bridge structures is simple, the digitization of existing structures can be a complex process. This article provides an overview of the state of the scientific practice with regard to the procedures, technologies and data used to generate 3D models of existing bridge structures using the BIM method. Using a systematic literature analysis, scientific databases are searched for suitable literature and analysed with predefined filtering parameters. The results provide a uniform understanding of the current status quo of the digitisation of existing bridge structures and show existing degrees of digitisation and automation.

For a detailed analysis of damage to bridge structures, it is necessary to apply diagnostic methods. The results of these procedures are often complex results represented in heterogeneous and incompatible data formats. These are sometimes difficult to integrate with 3D Building Information Modelling (BIM) models. This article examines two system- and data-level integration methods for linking heterogeneous building diagnostic data with 3D digital bridge models in Industry Foundation Classes (IFC) format. At the beginning of the article, an ontology for non-destructive diagnostic methods (SODIA ontology) is developed and linked to other bridge ontologies. The first approach consists of transforming the diagnostic data and the data of the IFC model into the data schema of the Resource Description Framework (RDF), linking them together and integrating them into a graphical database (data integration). The second approach investigates system integration using information containers according to the ISO linking of ISO 21597. By validating the two approaches, it can be demonstrated that both methods provide a way to link heterogeneous diagnostic data to a 3D bridge model and achieve interoperability in maintenance management. The results can improve the use of data by bridge asset managers in the operation and maintenance management of bridges. At the same time, they serve as a basis for possible data analyses that lead to predictions about damage progression and changes in condition.