| description abstract | The construction industry is one of the most significant contributors to the growth of the US economy as well as the global market. The Purdue Index for Construction (Pi-C) was developed in the form of a composite index consisting of five dimensions (Economy, Stability, Social, Development, and Quality) to monitor the health status of the construction industry and facilitate data-driven decision making. Despite its great potential, metrics under the Development and Quality dimensions are still missing, which limits our understanding of the health status of the construction industry. A promising approach to identify the missing metrics is to apply the latent Dirichlet allocation (LDA), which supports the discovery of latent topics from a large set of textual data. In this regard, this work introduces an LDA-based method to identify new metrics for the Development and Quality dimensions of the Pi-C. A total of 10,466 abstracts of research papers relevant to Development and Quality were collected from academic search engines using a web crawler. The LDA analysis was conducted to identify metrics and corresponding variables. As a result, two new metrics—Technology and Education—in the Development dimension and one new metric—Sustainability—in the Quality dimension were identified for Pi-C. Results revealed that the updated Pi-C improves our understanding of the construction industry in terms of technology, education, and sustainability. The updated Pi-C is expected to assist decision makers in data-driven decision-making and strategy development in the construction industry. | |
