Content area
Abstract
The construction industry is known for being information-intensive. Building Information Modeling (BIM) has attained increasing popularity as a tool to provide comprehensive information support for construction practitioners in the Architecture, Engineering, Construction, and Operation (AECO) industry. However, as more data is aggregated in BIM, it is increasingly challenging to extract building information from BIM models to support construction activities. Existing BIM Information Extraction (IE) methods are unable to provide a human-machine interactive conversation system for BIM practitioners to use natural language speech. With the development of Artificial Intelligence (AI) technologies, more opportunities arose for using speech and natural language tools in the AECO industry. AI technologies, such as a spoken dialogue system (SDS), enable a machine to converse with a human via natural language speech. “AI BIM”, which involves integrating conversational AI techniques with BIM, has become one of the future trends of BIM development.
This dissertation aims to develop an intelligent Building Information Spoken Dialogue System (iBISDS) to facilitate the performance and efficiency of IE from building information models. The iBISDS enables construction practitioners to utilize natural language speech to extract building information from building information models and enables a machine to generate a spoken natural language response. Furthermore, the developed iBISDS can be used by users with limited BIM knowledge and experience. The developed iBISDS is based on Industry Foundation Classes (IFC) specification data format, a widely supported open standard in the AECO industry. This dissertation implements machine learning technologies for natural language processing (NLP) and ontologies to support IE from BIM models. The architecture and functionalities of the iBISDS are discussed in detail. Quantitative validation and three case studies are utilized to validate the developed system. The contribution of this dissertation will facilitate the adoption of conversational AI technologies in the AECO industry, and the architecture of iBISDS can be extended to other IE areas.