English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As an essential way to ensure success of construction projects, on-site inspection involves intensive paperwork, while generating large amounts of textual data. Lack of understanding of information hidden in text-based inspection records always leads to overlooking of important issues and deferred decisions. Therefore, a novel text mining approach based on keyword extraction and topic modeling is introduced to identify key concerns and their dynamics of on-site issues for better decision-making process. Then, the proposed approach was demonstrated in a real world project and tested with 7250 issue records. Results showed that the proposed method could successfully extract key concerns hidden in texts and identify their changes with time, thereby enabling a more efficient on-site inspection and data-centric decision-making process. This research contributes: (1) to the body of knowledge a new framework for discovering key concerns and their changes with time in texts, and (2) to the state of practice by providing insights on hot topics and their changes with time to reduce on-site issues and make decisions efficiently.

Understanding On-Site Inspection of Construction Projects Based on Keyword Extraction and Topic Modeling