Laser-induced breakdown spectroscopy (LIBS) is a technique which enables the analysis of material components with precision and spatial resolution. Furthermore, the investigation method is comparatively fast which enables illustrating the distribution of elements within the examined material. This opens new possibilities for the investigation of very heterogeneous materials, such as concrete. Concrete consists of cement, water, and aggregates. As most of the transport processes take place exclusively in the hardened cement paste, relevant limit values linked to harmful element contents are specified in relation to the cement mass. When a concrete sample from an existing structure is examined, information on the concrete composition is usually not available. Therefore, assumptions have to be made to convert the element content analyzed in the sample based on the cement content in the sample. This inevitably leads to inaccuracies. Therefore, a method for distinction between cement paste and aggregates is required. Cement and aggregate components are chemically very close to each other and therefore, complex for classification. This is why the consideration of a single distinguishing feature is not sufficient. In this paper, a machine learning method is described and has been used to automate the distinction of the cement paste and aggregates of the LIBS data to receive reliable information of this technique. The presented approach could potentially be employed for many heterogeneous materials with the same complexity to quantify the arbitrary substances.

The sustainable asset management of district heating networks (DHNs) presents a complex challenge, integrating ecological, economic, and social sustainability dimensions. To address this, we developed a structured methodology for an algorithmic framework that supports sustainability assessments in DHNs. The proposed framework follows nine systematic phases, including defining objectives and weights, data collection and mining, establishing a data pipeline, aligning with key performance indicators (KPIs), conducting multi-criteria decision analysis (MCDA), and performing scenario-based sensitivity analysis. These phases enable the algorithm to assess both operational and strategic aspects of asset management. By incorporating six distinct sustainability scenarios – ranging from stricter environmental regulations and economic constraints to climate resilience and circular economy transitions – the framework evaluates potential outcomes and optimal strategies. Each scenario provides insights into the trade-offs and synergies between different sustainability objectives, guiding decision-makers in balancing efficiency, cost-effectiveness, and environmental impact. The results from scenario analyses inform tailored strategies, such as infrastructure reinvestment plans, predictive maintenance schedules, or adaptive regulatory compliance measures, ensuring resilient and future-proof DHN operations. This research establishes a foundation for data-driven, scenario-based sustainability management in DHNs, offering practical guidance for decision-making based on defined criteria and KPIs. The structured approach enhances flexibility and adaptability in asset management, paving the way for empirical validation and real-world implementation.

Pre-insulated bonded pipes used in district heating systems are engineered for a service life of at least 30 years. The longevity of these systems is primarily influenced by the properties of the polyurethane (PUR) foam insulation. While operational and installation parameters contribute to overall durability, the characterization of the foam layer is critical for service life prediction. According to EN 253, pipe manufacturers are required to maintain a minimum foam density of 55 kg/m ³ . However, the foaming process leads to a non-uniform density distribution across the pipe cross-section—from the steel carrier pipe to the high-density polyethylene (HDPE) casing. Determining the foam density distribution function enables normalization of measurement data, which is essential for subsequent analysis using spectroscopic techniques such as Laser Induced Breakdown Spectroscopy (LIBS) and Fourier Transform Infrared (FT-IR) Spectroscopy. Traditional methods for density determination, including liquid displacement, and the cut-and-weigh method, are often labor-intensive and prone to inaccuracies. To address these limitations, a methodology has been developed that utilizes nanoscale X-ray microscopy and computer vision techniques to analyze foam density distribution.

The aim of the asset management of district heating pipes is to create a financial framework for a reliable, affordable and ecological heat supply in a district heating system. Today, estimates based on available statistical data suggest that over 19,000 heating networks with a total pipeline route length of over 199,000 kilometres supply heat to over 80 million inhabitants in the European Union (EU). With the ‘European Green Deal’ and the EU ‘Fit-for-55’ legislative package, the EU has described its climate goals and established the political framework for achieving them through the ‘Renewable Energy Directive’ (RED III), the ‘Energy Performance of Buildings Directive’ (EPBD) and ‘Energy Efficiency Directive’ (EED). Existing district heating systems, their transformation and expansion as well as building new district heating systems are essential for the achievement of the European climate goals. To this end, network operators require reliable asset management tools for implementing the EU climate goals. This paper presents the factors influencing the service life of different pipe systems and approaches for improving asset management simulations based on ongoing national and international research.