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.

In hospital construction, additional challenges must be considered, such as an increased number of stakeholders and building trades, such as medical and laboratory technology. Due to the increasing requirements and challenges, associated construction processes are becoming more intricate. Especially for complex building types, the effects of this development are clearly noticeable and cause considerable disruptions to the construction process. A main difficulty constitutes the missing definition of the interfaces of building trades and participants. In the present study, interfaces in hospital construction were identified and analyzed by guided interviews with experts from the health sector. The qualitative content analysis, according to Mayring, was used for the evaluation to derive appropriate solution approaches. This paper presents the interfaces using the example of hospital construction in Germany and general approaches of optimization. Hereby, the digital method Building Information Modeling (BIM) plays a decisive role in the optimization of interfaces, especially in complex buildings. Furthermore, a task and building trade control matrix is required to better coordinate the interfaces. The identified approach intends to alleviate potential disputes and misunderstandings among stakeholders, as well as to improve time and financial predictability, which are particularly valuable during inflationary periods.

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.