Construction safety risk assessments are labor-intensive and disconnected from dynamic project conditions and digital systems. This research investigates how domain-specific multi-agent generative artificial intelligence systems can automate construction safety risk assessments and subsequently share them with knowledge graphs (KGs) to enable downstream reuse of the extracted knowledge and overcome information isolation. The research employs a Design Science Research methodology to develop a novel multi-agent system leveraging Large Language Models (LLMs) and semantic-web techniques. Individual agents within the system perform specific tasks, including hazard identification, risk mitigation, and ontology-aligned SPARQL query production. An evaluation with eight construction safety experts validates that the system successfully automates the production of complete and relevant risk assessment documents. A semantic, structural, and syntactical correctness assessment through SHACL shapes, and manual completeness evaluation, furthermore validates that the system reliably converts the risk information into accurate SPARQL insertions compatible with existing ontology-based KGs. Unlike prior research, this agentic pipeline automates task-based assessment generation and semantic integration, enabling downstream reasoning and interoperability within digital twin environments. Safety professionals benefit from faster and machine-readable availability of safety information that supports downstream reasoning processes (e.g., hazard detection or simulation), which are not evaluated in this study. Future research should identify whether the provided knowledge is sufficient to streamline such downstream processes and outline clear information requirements to enhance the system further. Future research should investigate automated and scalable semantic validation methods, reasoning over concurrent tasks, and integration with digital twin systems.

• Automated safety tools may exceed manual methods but still need industry adoption. • Decision-making in construction is hindered by limited safety insight extraction. • This study utilizes knowledge graph for enhanced safety and planning insights. • The proposed framework and ontology create effective safety knowledge representation. • A case study demonstrates the cross-domain impact of safety and planning decisions. Construction projects and their dynamic and yet hazardous work environments face significant challenges. Despite advancements, many proposed solutions for information extraction and utilization remain impractical due to complexity and lack of interoperability. Information is often siloed in proprietary formats, making it difficult to integrate. This issue is evident in the construction safety domain, where advanced risk analysis tools provide detailed insights to hazards but can be overwhelming. Similar challenges exist in cost estimation, schedule evaluation, progress monitoring, and quality compliance checking. Decision-making in construction scheduling struggles to assess how changes impact site safety due to insufficient information and knowledge extraction capabilities, especially when it comes to cross-domain knowledge extraction. This study aims to make safety information accessible to safety and planning professionals. By leveraging Digital Twins, automated safety analysis, and knowledge representation, we enable decision-makers to gain deeper insights into their domain and understand the interplay between project planning and safety. We propose a framework for knowledge extraction, an ontology for capturing knowledge, and query building blocks to transform natural language questions into actionable queries. These methods are tested in a case study, revealing valuable insights into the cross-domain impact of decisions.

Specific occupational construction safety, health, and well-being related knowledge and information are scattered and fragmented. Despite technological advancements of information and knowledge management, a link between safety management and information models is still missing. In this paper we present first steps towards a unifying formal (logic-based) domain model of construction safety, called SafeConDM, that consists of: (1) a semantically rich ontology of hazard, safety concepts, and concept relationships that builds on, and integrates with, existing construction safety ontologies and building information models; (2) a set of first-order if-then rules linking construction site states with the potential for specific hazards to occur that we define in a novel way using spatial artefacts. We present a prototype software tool, based on our ASP4BIM tool that implements SafeConDM for construction hazard analysis and safe construction planning decision support, and empirically evaluate our tool on three real-world construction building models.