Information Transformation and Automated Reasoning for Automated Compliance Checking in Construction

This paper presents a new approach for automated compliance checking in the construction domain. The approach utilizes semantic modeling, semantic Natural Language Processing (NLP) techniques (including text classification and information extraction), and logic reasoning to facilitate automated textual regulatory document analysis and processing for extracting requirements from these documents and formalizing these requirements in a computer-processable format. The approach involves developing a set of algorithms and combining them into one computational platform: 1) semantic machine-learning-based algorithms for text classification (TC), 2) hybrid syntactic-semantic rule-based algorithms for information extraction (IE), 3) semantic rule-based algorithms for information transformation (ITr), and 4) logic-based algorithms for compliance reasoning (CR). This paper focuses on presenting our algorithms for ITr. A semantic logic-based representation for construction regulatory requirements is described. Semantic mapping rules and conflict resolution rules for transforming the extracted information into the representation are discussed. Our combined TC, IE and ITr algorithms were tested in extracting and formalizing quantitative requirements in the 2006 International Building Code, achieving 96% and 92% precision and recall, respectively.