The durability of concrete structures is essential for reliable infrastructure. Although many deterioration models are available, they are rarely applied in situ. For existing structures in need of repair or durability assessment, this is also the case for Building Information Modeling (BIM). However, both BIM and durability modeling hold great potential to both minimize expended resources and maximize the reliability of structures. At the Institute for Building Materials Research (ibac) at RWTH Aachen University, a novel approach to the calibration of deterioration models using Bayesian inference iteratively in a BIM model enriched with machine-readable diagnosis data to achieve a predictive decision support tool is being developed. This paper demonstrates the digital workflow, validates the proposed approach, and expresses the added value for the planning of repair measures.

Digitisation in the construction industry continues to advance and, together with the increasing dissemination and further development of hardware and software, is steadily opening up further opportunities for innovative ways of working. Building Information Modelling (BIM) is currently becoming the standard for new construction but has not yet been optimised for use in existing buildings. Therefore, the Institute of Building Materials Research (ibac) is researching new methods and possibilities for BIM-based building preservation. In this paper, the automated creation and analysis of point clouds as well as the implementation of further information from in situ diagnosis and monitoring systems in BIM-Models are presented. On a practical example, the different steps of a subsequent digitisation of an existing building are demonstrated considering new possibilities as autonomous robots and the intelligent utilisation of sensors and diagnostics tools. The goal is a decision support tool, which is independent from proprietary software, adaptive to different types of buildings and open for various interfaces. Current results show that quantifying point clouds and making BIM-models usable beyond the planning and execution phase for new buildings are essential steps for the digitisation of building maintenance. The proposed digital workflow holds great potential for effective building diagnoses and efficient service life management.

Building Information Modeling (BIM) is increasingly establishing a model-based work process in the construction industry. Though it can be considered the standard for the planning of new buildings, the use cases for existing buildings are still limited. Nonetheless, BIM models provide promising possibilities which are increasingly being researched in different fields of application. At the Institute for Building Materials Research (ibac) at RWTH Aachen University, a novel approach for maintenance and repair of reinforced concrete is being developed, using BIM models enriched with machine-readable diagnosis data. This paper proposes a digital workflow and highlights the added value for planning repair measures. Using BIM in maintenance and repair can accelerate the planning process and decrease the required material consumption for the execution.

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.

Purpose: Fire safety requirements involve complex spatial and semantic conditions that go beyond simple attribute checks. This review consolidates approaches for automated code compliance checking (ACCC) for fire safety, classifies them by applied technologies, methods, complexity levels and data formats, and assesses their suitability for building permit processes. Design/methodology/approach: A PRISMA-compliant systematic literature review across major databases, augmented by backward and forward snowballing, yielded 16 papers. The included studies were mapped descriptively and analyzed thematically using concept matrices according to Webster and Watson, covering methods, technologies, rule complexity, data formats, and reported opportunities and challenges. Findings: Existing methods effectively support rule checks of lower complexity, but tasks involving spatial reasoning and interdependent safety conditions, such as multi-story compartmentation, remain difficult to automate. Compared to black box systems, white box methods are preferred for their transparency and auditability, with visual programming noted as particularly user-friendly. Despite the many advantages of ACCC, organizational, technical, and regulatory requirements constrain deployment in the permit process. Originality/value: The use of ACCC in the building permit process is a promising opportunity to achieve long-term efficiency gains, reduce errors and lighten the workload in building authorities, provided that the current challenges are addressed appropriately through a coherent implementation strategy. As research activities have accelerated significantly since 2023, the topic is becoming increasingly important, while several challenges remain unresolved. This research identifies further gaps, such as limited support for complex spatial–topological reasoning or insufficient IFC expressiveness for fire-safety properties.