Automated code-compliance checking based on Building Information Modeling (BIM) has received increasing attention in recent years. However, many existing approaches rely on manually formalized rules derived from non-machine-readable standards and building codes, which limits scalability and hampers adaptability to regulatory changes. This paper presents a framework for the automated extraction and formalization of building code information requirements by leveraging Level 4 Smart Standards encoded in the National Information Standards Organization (NISO) Standards Tag Suite (STS) in combination with the ISOProps ontology. The proposed procedure generates machine-interpretable validation rules using the Shapes Constraint Language (SHACL), which can be directly applied to BIM models to produce structured compliance validation reports. The framework is demonstrated through a proof-of-concept implementation that uses real-world building regulations to verify the compliance of calcium silicate masonry units based on IFC models. The results indicate a substantial reduction in manual rule-modeling effort while enabling consistent compliance assessment across multiple regulatory revisions. Overall, the proposed approach supports more maintainable, transparent, and scalable compliance checking workflows, contributing to the digitalization and automation of regulatory approval processes.

Building permitting is essential for ensuring the safety, sustainability, and societal alignment of construction projects. Despite interest from both practitioners and researchers, the process remains largely manual and fragmented. Ontologies offer a promising solution by managing complexity and enabling automation through semantic information, though current ontologies in the building permit domain are limited to specific aspects like building code checking. On the process level, the OntoBPR framework integrates multiple domain-specific ontologies for a seamless digital permitting process and provides a workflow to automate the lifecycle of the permit review. Therefore, it suggests integrating the submitted building application using standardized information containers. The paper explores how digital applications can be submitted, reviewed, verified for completeness, and forwarded to authorities, and how permit review results can be gathered to support decision-making and automate notification issuance, and it provides a demonstration in a case study. In conclusion, OntoBPR formalizes a multi-layered ontology that advances and aligns the partitioned building permit process and provides an adaptable framework to harmonize diverse legal, informatics, and procedural aspects.

The construction industry is increasingly adopting Digital Twin (DT) technology to support the design, construction, and operation of buildings and structures. In this context, a key challenge for DTs is integrating heterogeneous data sources to address requirements that evolve across different life cycle phases and use cases. A modular approach for deploying DTs offers a flexible and scalable solution that can adapt to these changing requirements. However, a clear definition and structure of DT modules for the built environment are still missing. This research presents a modular System Reference Architecture (SRA) for implementing Semantic DTs in the construction industry. As its central component, the SRA leverages the inherently modular Asset Administration Shell (AAS) reference model for asset DTs in Industry 4.0. Built on submodels, each addressing a specific use case or aspect, the AAS serves as a high-level framework for DTs. The SRA extends the AAS with standardized Information Containers for Linked Document Delivery (ICDD), integrated through a Linked Data approach employing a semantic layer of ontologies. The feasibility of the proposed SRA is demonstrated through a case-specific implementation for the precast concrete production. Two submodels are developed within the SRA: one for accessing dynamic sensor data via time series databases and another for integrating BIM-derived semantic data using ICDD. The architecture is evaluated through a simulated curing process, where SPARQL and REST-based queries enable real-time monitoring and feedback control. The results confirm the SRA’s ability to integrate heterogeneous data sources, support semantic interoperability, and facilitate lifecycle-oriented feedback mechanisms.

The growing need for digital representations of existing buildings in the Architecture, Engineering, Construction & Operations (AECO) domain necessitates efficient methods to retrospectively create Building Information Modeling (BIM) models. One prominent approach to obtain the necessary information is Plan-to-BIM, i.e., analyzing building documentation such as floor plans. However, the storage of this information is not standardized which leads to compatibility issues in collaborative scenarios. To address this, the paper presents the Drawing Analysis Ontology (DAnO), which is designed to standardize the representation of technical drawing data extracted through computer vision techniques. Focusing on floor plans, DAnO enables the aggregation, integration, and validation of extracted elements by defining key concepts, such as DrawingElement, DisplayElement, and DescriptionElement, and their relationships. By means of real floor plans, a case study demonstrates the ontology’s effectiveness in facilitating the generation of building models from legacy drawings, highlighting its potential to streamline BIM reconstruction workflows and to enhance interoperability in the AECO industry.

Efficient maintenance planning and streamlined inspection for bridges are essential to prevent catastrophic structural failures. Digital Bridge Management Systems (BMS) have the potential to streamline these tasks. However, their effectiveness relies heavily on the availability of accurate digital bridge models, which are currently challenging and costly to create, limiting the widespread adoption of BMS. This study addresses this issue by proposing a computer vision-based process for generating bridge superstructure models from pixel-based construction drawings. We introduce an automatic pipeline that utilizes a deep learning-based symbol pose estimation approach based on Keypoint R-CNN to organize drawing views spatially, implementing parts of the proposed process. By extending the keypoint-based detection approach to simultaneously process multiple object classes with a variable number of keypoints, a single instance of Keypoint R-CNN can be trained for all identified symbols. We conducted an empirical analysis to determine evaluation parameters for the symbol pose estimation approach to evaluate the method’s performance and improve the trained model’s comparability . Our findings demonstrate promising steps towards efficient bridge modeling, ultimately facilitating maintenance planning and management.

Precast concrete construction enhances project efficiency, sustainability, and durability by leveraging a controlled production environment that ensures high-quality outputs. Still, the sequential nature of off-site production, encompassing casting, curing, and storage of the precast elements, is subject to significant uncertainties, including supply chain variability and environmental factors affecting curing times. Dynamic adjustments to the production schedule are essential for aligning with real-time changes, necessitating a robust framework for real-time data acquisition and analysis. Dynamic Scheduling (DS) is a responsive and adaptive approach that accommodates real-time changes, optimizes the production flow, and minimizes downtime or delays. However, the DS approach demands real-time data to quickly and efficiently respond to unforeseen challenges, which requires acquiring and analyzing production-relevant data throughout the production process. The Digital Twin (DT) emerges in Industry 4.0 (I4.0) as a bridge between physical operations and digital capabilities, enabling a seamless flow of information, for which the Asset Administration Shell (AAS) is a reference implementation. This study introduces a DS framework to optimize precast element production, utilizing a DT for real-time data aggregation across the production system. The framework implements a Semantic DT based on the AAS and the Linked Data approach. It employs Resource Description Framework (RDF) serialization of the AAS and an ontological representation of the production system for data integration. The framework leverages a simulation-based scheduler, which exchanges data with the DT in a Service-oriented Architecture (SoA) using SPARQL, a language for querying and updating graph databases. The approach is evaluated through a proof of concept, demonstrating effective uncertainty management in a dynamic production environment.

In mechanized tunneling projects, finding a low-risk and cost-effective alignment is an important task. Several alignment variants are usually created and each one is intensely scrutinized. Variants often have individual advantages and disadvantages and can lead to different constructive designs of a tunnel. In order to find the best alignment possible the variants have to be analyzed and evaluated based on requirements and evaluation criteria, such as safety, cost, built environment and operational requirements. To perform this evaluation and to enable comprehensive decision making, a holistic planning environment is examined that includes documents and models of different domains. In general, these domain specific data differ schematically and semantically, which consequently makes it challenging to combine and compare such diverse data. For this purpose, information from different sources must be linked and evaluated in a structured way. In particular, spatial relationships have to be investigated. Therefore, in this paper, ontology databases are utilized to merge BIM and GIS at data level to create an integrated model of the entire tunneling project. Relevant information for decision-making can then be derived, such as the location of private and public buildings that are in a certain vicinity of the planned alignment. On the one hand, the implementation of queries is a popular and frequently used approach to check for semantic properties. On the other hand, using a query language to derive information from geometric data can be challenging, due to the necessity of processing geometric data prior to and during query execution. Additionally, geometric definitions can differ in the considered coordinate reference system, the dimension or the structure. To handle geometry information by employing query languages, representations are methodically transformed to well-known text literals. A simplified and uniform geometric representation can be utilized for spatial reasoning, for example by adopting GeoSPARQL methods.