Automated Information Transformation for Automated Regulatory Compliance Checking in Construction

Abstract

To fully automate regulatory compliance checking of construction projects, regulatory requirements need to be automatically extracted from various construction regulatory documents and then transformed into a formalized format that enables automated reasoning. To address this need, the authors propose an approach for automatically extracting information from construction regulatory textual documents and transforming them into logic clauses that could be directly used for automated reasoning. This paper focuses on presenting the proposed information transformation (ITr) methodology and the corresponding algorithms. The proposed ITr methodology utilizes a rule-based, semantic natural language processing (NLP) approach. A set of semantic mapping (SeM) rules and conflict resolution (CoR) rules are used to enable the automation of the transformation process. Several syntactic text features (captured using NLP techniques) and semantic text features (captured using an ontology) are used in the SeM and CoR rules. A bottom-up method is leveraged to handle complex sentence components. A consume and generate mechanism is proposed to implement the bottom-up method and execute the SeM rules. The proposed ITr algorithms were tested in transforming information instances of quantitative requirements, which were automatically extracted from the International Building Code 2009, into logic clauses. The algorithms achieved 98.2 and 99.1% precision and recall, respectively, on the testing data.