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 light of the climate protection goals of the Paris Agreement, optimizing the sustainability of planning processes is becoming increasingly important. Building Information Modeling (BIM) centralizes planning information for interdisciplinary evaluation, enabling sustainable decision-making. This paper presents a systematic review of BIM-based decision support approaches for building sustainability. Following vom Brocke’s five-phase model and the PRISMA 2020 standard, 70 studies were analyzed to identify current methods, their respective strengths and limitations, and future research needs. The findings reveal a highly dynamic but fragmented field of research. Assessment-Based Optimization and multi-criteria decision-making (MCDM) methods dominate. However, the holistic integration of ecological, economic and social indicators remains rare, with social sustainability receiving the least attention. Most approaches rely on proprietary BIM environments, while open BIM applications and interoperable data standards remain underdeveloped. Standardized data sources, such as Environmental Product Declarations (EPDs), are well established for ecological assessments, but are largely lacking for the economic and social dimensions. The review highlights the urgent need for interoperable data formats, standardized evaluation methods, and accessible databases to enable scalable and comparable BIM-based sustainability optimizations. Advancing these foundations will be essential for achieving consistent, holistic sustainability optimization in the construction industry.

Public participation is crucial for designing livable cities and accelerating planning procedures. A key challenge is the complexity of planning documents, which are often difficult for non-experts to understand. Digital participation and techniques like 3D visualizations and Building Information Modeling (BIM) offer promising solutions to enhance the comprehensibility and acceptance of construction projects. This study aims to develop an assessment matrix for participation procedures that enables the quality and effectiveness of such processes to be comprehensively evaluated. A systematic literature analysis is first used to identify success factors and challenges of public participation as well as specific success factors and challenges for digital forms of participation (e-participation). These success factors are divided into the categories of resources, communication and process-related aspects, while challenges are categorized into stakeholder-specific and overarching aspects. Based on these findings, an assessment matrix is created to evaluate public participation in the context of planning and approval procedures. The assessment matrix includes parameters such as background information, the participation process, planning documents, and responses and their respective impacts. This matrix will be applied in the BIM4People research project, funded by the Ministry of Digital and Transport (BMDV), to identify improvements in current participation processes and explore how BIM models can optimize them.

Public participation is a central component of democratic decision-making processes, particularly in planning and approval procedures. However, increasing data complexity and the growing number of submitted objections significantly raise the effort required for their review and processing. Against this background, this study developed an AI-supported objection management system that uses a large language model (LLM) to automatically pre-sort objections by topic and generate response suggestions based on historical objection texts from previous infrastructure projects. The aim is to increase efficiency in the processing workflow while maintaining consistent response quality without replacing human decision-making. The prototype development is preceded by a literature review to identify key user requirements and derive relevant use cases. Subsequently, four expert workshops with representatives from German road and rail infrastructure administrations at the state and federal level were conducted to evaluate the prototype. The results indicate significant efficiency potential, particularly through automated thematic pre-sorting of objections. However, topic structures must be adapted to the specific procedure. AI currently mainly serves as supportive pre-processing and requires human review (“human-in-the-loop”). Transparent labeling of AI use is also necessary to ensure traceability and acceptance. The findings will be incorporated into the ongoing development of the prototype within the BIM4People research project funded by the German Federal Ministry of Transport (BMV), with the aim of further improving the system’s functionality and exploring additional applications.

Public participation is critical to the success of construction projects as it enables citizens to influence and engage in planning processes. Traditional participation methods often rely on two-dimensional technical plans, which are frequently perceived as difficult to understand, particularly by non-experts. In recent years, digital visualization techniques, especially 3D models, have emerged as promising alternatives. These approaches promise to enhance communication among stakeholders and deepen engagement by offering clearer and more comprehensible representations of construction projects. This paper explores how public participation based on 3D BIM model data can be implemented and examines the user experiences associated with such an approach. The concept is built on a 3D BIM modelbased participation platform integrated with a Common Data Environment (CDE), which serves as the source for three different model variants. A prototype of the participation platform was tested in the context of public involvement for the redesign of a mobility space in a German city. Citizens were therefore invited to interact with the various model variants and actively participate in the process. Findings from this initiative demonstrate that guided textual information with 3D visuals enhances citizens' comprehension of the project. Users particularly valued the 3D models for enhancing their understanding, while expressing a preference for rather realistic models, even if they are more complex. In summary, the results suggest that BIM models can significantly enhance public participation by improving comprehension and engagement, ultimately contributing to social sustainability.