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.
Recommended citation: Lin, J.R.*, Hu, Z.Z., Li, J.L., and Chen, L.M. (2020). "Understanding On-Site Inspection of Construction Projects based on Keyword Extraction and Topic Modeling" IEEE Access. 8: 198503-198517. doi: 10.1109/ACCESS.2020.3035214
This research is supported by the Natural Science Foundation of China (No. 51908323), the Beijing Municipal Science and Technology Project (No. Z181100005918006) and the Tsinghua University Initiative Scientific Research Program (No. 2019Z02UOT).
Accession Number: WOS:000589748900001
IDS Number: OR8WZ