Text Mining-Based Patent Analysis for Automated Rule Checking in AEC

TL;DR

This paper uses text mining techniques on patents to analyze automated rule checking in the AEC industry, revealing hotspots and trends to guide future innovation.

Contribution

It introduces an integrated approach combining SNA and LDA for comprehensive patent analysis in ARC, highlighting development trends and hotspots.

Findings

Different research hotspots in Chinese and English patents.

Identification of key ARC topics and their evolution.

Insights into technological development directions.

Abstract

Automated rule checking (ARC), which is expected to promote the efficiency of the compliance checking process in the architecture, engineering, and construction (AEC) industry, is gaining increasing attention. Throwing light on the ARC application hotspots and forecasting its trends are useful to the related research and drive innovations. Therefore, this study takes the patents from the database of the Derwent Innovations Index database (DII) and China national knowledge infrastructure (CNKI) as data sources and then carried out a three-step analysis including (1) quantitative characteristics (i.e., annual distribution analysis) of patents, (2) identification of ARC topics using a latent Dirichlet allocation (LDA) and, (3) SNA-based co-occurrence analysis of ARC topics. The results show that the research hotspots and trends of Chinese and English patents are different. The contributions of this study have three aspects: (1) an approach to a comprehensive analysis of patents by integrating multiple text mining methods (i.e., SNA and LDA) is introduced ; (2) the application hotspots and development trends of ARC are reviewed based on patent analysis; and (3) a signpost for technological development and innovation of ARC is provided.