Natural language generation and deep learning for intelligent building codes

Abstract

Many existing automated compliance checking (ACC) systems require the processes of extracting regulatory information from natural-language building-code requirements and transforming the extracted information into computer-processable semantic representations. These processes could, however, be jeopardized by the ambiguous nature of the natural language and the hierarchically complex structures of building-code requirements. To address this problem, this paper proposes the concept of intelligent building code for bypassing the error-prone information extraction and transformation processes. In the proposed intelligent code, the natural-language requirements in the code are connected with highly structured computer-understandable semantic information, which is represented in the form of semantic requirement hierarchies and can be readily used by computers for ACC. The paper also proposes a deep learning-based method to automatically generate such intelligent code. The method leverages the requirement hierarchy representation, a proposed deep learning unit-to-text model for generating requirement sentence segments, and a proposed semantic correspondence score for configuring the segments into requirement sentences. The method was implemented and tested on a dataset from multiple regulatory documents. The generated intelligent requirements were evaluated in terms of both natural-language requirement comprehensibility and correspondence between the natural language and the semantic representation, with the results indicating high performance for the proposed representation and method. The proposed intelligent code will help reduce ACC errors, improve requirement comprehensibility, and facilitate intelligent code analytics.