Construction projects usually involve signing various contracts with specific billing procedures. In practice, dealing with complex contract structures causes significant problems, especially with regard to timely payment and guaranteed cash flow. Furthermore, a lack of transparency leads to a loss of trust. As a result, late or non-payment is a common problem in the construction industry. This paper presents the concept of implementing smart contracts for automated, transparent, and traceable payment processing for construction projects. Automated billing is achieved by combining Building Information Modeling (BIM) approaches with blockchain-based smart contracts. Thereby, parts of traditional construction contracts are transferred to a smart contract. The smart contract is set up using digital BIM-based tender documents and contains all of the relevant data for financial transactions. Once the contracted construction work has been accepted by the client, payments can be made automatically via authorized financial institutions. This paper describes the framework, referred to as BIMcontracts, the container-based data exchange, and the digital contract management workflow. It discusses the industry-specific requirements for blockchain and data storage and explains which technical and software architectural decisions were made. A case study is used to demonstrate the current implementation of the concept.

The paper proposes a solution for a Building Information Modeling (BIM)-enabled Infrastructure Asset Management System (AMS) for road owners. The approach provides asset managers with a strategy for the dynamic use of Information Containers for Linked Document Delivery (ICDDs), considering the requirements of stakeholders across domains in the operational phase. The state of the art shows how information management can be carried out utilizing information containers employing Semantic Web technologies Resource Description Framework (RDF), SPARQL, and R2RML. The key output is developing a web-based platform that implements ICDD containers for asset management. Existing AMS are integrated by using SQL data mapped to RDF-based ontology data in the container. The use of existing domain-specific ontologies for infrastructure in combination with the linkage of domain knowledge to a three-dimensional BIM model is a step beyond the state of the art and practice in the construction industry. Linking inside the container allows for querying data across several information models and ontology-based data to create stakeholder-specific data views. The approach was demonstrated in two use cases. The first was related to the visual inspection of a concrete bridge. The detection of damage and the process of communicating the damage to a contractor charged with the repair were described. The second use case was related to a road pavement and demonstrated how decision-making about maintenance activities can be supported using cross-domain information containers.

Relational databases containing construction-related data are widely used in the Architecture, Engineering, and Construction (AEC) industry to manage diverse datasets, including project management and building-specific information. This study explores the use of large language models (LLMs) to convert construction data from relational databases into formal semantic representations, such as the resource description framework (RDF). Transforming this data into RDF-encoded knowledge graphs enhances interoperability and enables advanced querying capabilities. However, existing methods like R2RML and Direct Mapping face significant challenges, including the need for domain expertise and scalability issues. LLMs, with their advanced natural language processing capabilities, offer a promising solution by automating the conversion process, reducing the reliance on expert knowledge, and semantically enriching data through appropriate ontologies. This paper evaluates the potential of four LLMs (two versions of GPT and Llama) to enhance data enrichment workflows in the construction industry and examines the limitations of applying these models to large-scale datasets.