A Semantic Approach for Automated Rule Compliance Checking in Construction Industry

Automated Compliance Checking (ACC) of building/construction projects is one of the important applications in Architecture, Engineering and Construction (AEC) industry, because it provides the checking processes and results of whether a building design complies with relevant laws, policies and regulations. Currently, Automated Compliance Checking still involves lots of manual operations, and massive time and cost consumption. Additionally, some sub-tasks of ACC have been researched, while few studies can automatically implement the whole ACC process. To solve related issues, we proposed a semantic approach to implement the whole ACC process in an automated way. Natural Language Processing (NLP) is used to extract rule terms and logic relationships among these terms from text regulatory documents. Rule terms are mapped to keywords (concepts or properties) in BIM data through term matching and semantic similarity analysis. After that, according to the mapped keywords in BIM and logic relationships among keywords, a corresponding SPARQL query is automatically generated. The query results can be non-compliance or compliance with rules based on the generated SPARQL query and requirements of stakeholders. The cases study proves that the proposed approach can provide a flexible and effective rule checking for BIM data. In addition, based on the proposed approach, we also further develop a semantic framework to implement automated rule compliance checking in construction industry.

Automated summary

Automated Compliance Checking (ACC) is crucial in the Architecture, Engineering and Construction (AEC) industry, however, it still involves manual operations and high costs. A semantic approach is proposed to automate the whole ACC process, extracting rule terms and logic relationships from text regulatory documents using NLP, mapping them to BIM data keywords for generating SPARQL queries, providing flexible and effective rule checking for BIM data.