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.

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.