Journal of the Korea Institute of Building Construction (한국건축시공학회지)

The Korean Institute of Building Construction (한국건축시공학회)
권호 표지
DOI QR코드

DOI QR Code

Crafting a Quality Performance Evaluation Model Leveraging Unstructured Data

비정형데이터를 활용한 건축현장 품질성과 평가 모델 개발

  • Lee, Kiseok (Department of Construction Economic & Finance Research, Construction & Economy Research Institute of Korea) ;
  • Song, Taegeun (Department of Construction Economic & Finance Research, Construction & Economy Research Institute of Korea) ;
  • Yoo, Wi Sung (Department of Construction Economic & Finance Research, Construction & Economy Research Institute of Korea)
  • Received : 2023.12.28
  • Accepted : 2024.02.05
  • Published : 2024.02.20
Download PDF
⟨ Previous Next ⟩

Abstract

The frequent occurrence of structural failures at building construction sites in Korea has underscored the critical role of rigorous oversight in the inspection and management of construction projects. As mandated by prevailing regulations and standards, onsite supervision by designated supervisors encompasses thorough documentation of construction quality, material standards, and the history of any reconstructions, among other factors. These reports, predominantly consisting of unstructured data, constitute approximately 80% of the data amassed at construction sites and serve as a comprehensive repository of quality-related information. This research introduces the SL-QPA model, which employs text mining techniques to preprocess supervision reports and establish a sentiment dictionary, thereby enabling the quantification of quality performance. The study's findings, demonstrating a statistically significant Pearson correlation between the quality performance scores derived from the SL-QPA model and various legally defined indicators, were substantiated through a one-way analysis of variance of the correlation coefficients. The SL-QPA model, as developed in this study, offers a supplementary approach to evaluating the quality performance of building construction projects. It holds the promise of enhancing quality inspection and management practices by harnessing the wealth of unstructured data generated throughout the lifecycle of construction projects.

최근 국내 건축현장에서 붕괴사고가 계속해서 발생하고 있어 시공 및 자재 품질 점검과 관리에 대한 공사감리의 중요성이 증가하고 있다. 현행 제도 및 기준에 의하면, 공사감리 업무는 주요 책임이 있는 감리자가 건축현장에서 진행되고 있는 시공 품질, 자재 품질, 재시공 이력 등이 상세하게 기술하여 공사감리보고서를 작성한다. 이러한 문서는 대표적인 비정형데이터로 건축현장에서 생성되고 있는 데이터의 80%의 비중을 차지하고 있으며, 건축현장의 품질정보가 상세하게 기록되어있다. 본 연구에 건축현장에서 발생하고 있는 공사감리보고서를 텍스트마이닝으로 전처리 후 감성사전을 구축하여 품질성과 수준을 평가하고 계량화할 수 있는 SL-QPA 모델을 제안하였다. 모델에서 산정된 성과 점수와 법적 기준에 의한 지표와의 피어슨 상관관계 분석하고, 상관계수에 대한 일원분산분석 결과는 통계적으로 유의미하였다. 제안된 SL-QPA 모델은 현행 건축현장 품질성과 진단에 상호 보완적으로 활용될 수 있고, 공사단계에서 연속적으로 생성되는 비정형데이터를 활용하여 점검 및 관리 활동의 적시성을 향상시킬 것으로 기대된다.

Keywords

Acknowledgement

This research was supported by a grant(RS-2022-00143493, project number:1615012983) from Digital-Based Building Construction and Safety Supervision Technology Research Program funded by Ministry of Land, Infrastructure and Transport of Korean Government.

References

  1. Announcing the results of the investigation into the collapse of the underground parking lot of an apartment building in Incheon [Internet]. Sejong (Korea): Ministry of Land, Infrastructure and Transport; 2023 Jul 05. Available from: http://www.molit.go.kr/USR/NEWS/m_71/dtl.jsp?lcmspage=1&id=95088540
  2. Kim DG. The seoul metropolitan government's ordering construction will directly supervise...a trial application of seongsan bridge floor repair work [Internet]. Seoul (Korea): Seoul Digital Foundation; 2022 Jul 13. Available from: https://www.seoul.go.kr/news/news_report.do#view/367460?tr_code=snewss
  3. Building act [Internet]. Sejong (Korea): Ministry of Land, Infrastructure and Transport; 2023 Jun 11. Available from: https://www.law.go.kr/%EB%B2%95%EB%A0%B9/%EA%B1%B4%EC%B6%95%EB%B2%95/%EC%A0%9C25%EC%A1%B0
  4. Song SH, Lee HS, Park MS. Quality performance management system for construction projects using quality performance indicators. Korean Journal of Construction Engineering and Management. 2006 Jan;7(3):76-85.
  5. Kwak C, Kim YS. An analysis for the causes of design quality declining from the perspective of a contractor in the apartment construction projects. Journal of The Architectural Institute of Korea Structure & Construction. 2010 Dec;26(12):193-200.
  6. Construction technology promotion act [Internet]. Sejong (Korea): Ministry of Land, Infrastructure and Transport; 2022 Jan 18. Available from: https://www.law.go.kr/%ED%96%89%EC%A0%95%EA%B7%9C%EC%B9%99/%EA%B1%B4%EC%84%A4%EA%B3%B5%EC%82%AC%ED%92%88%EC%A7%88%EA%B4%80%EB%A6%AC%EC%97%85%EB%AC%B4%EC%A7%80%EC%B9%A8
  7. Kim KJ. Measures to improve current building defect liability system through relevant legislation analysis and status analysis. Journal of Real Estate Analysis. 2021 Jul;7(2):117-39. https://doi.org/10.30902/jrea.2021.7.2.117
  8. Kim SH, Yoo WS, Choi SI. A review of the state-of-the-art in construction public data implementation - espacially 4 selected construction information systems. Journal of the Korea Academia-Industrial Cooperation Society. 2023 Apr;24(4):49-60. https://doi.org/10.5762/KAIS.2023.24.4.49
  9. Sung YK, Hur YK, Lee SW, Yoo WS. Development of performance indicators on private building construction sites using supervisory report. Korean Journal of Construction Engineering and Management. 2022 Nov;23(6):65-75. https://doi.org/10.6106/KJCEM.2022.23.6.065
  10. Park HM, Kim CH, Kim JH. Generating a korean sentiment lexicon through sentiment score propapation. KIPS Transactions on Software and Data Engineering. 2020 Feb;9(2):53-60. https://doi.org/10.3745/KTSDE.2020.9.2.53
  11. Minister won hee-ryong orders enhanced role of public supervision in safety [Internet]. Sejong (Korea): Ministry of Land, Infrastructure and Transport; 2023 Aug 09. Available from: http://www.molit.go.kr/USR/NEWS/m_71/dtl.jsp?lcmspage=1&id=95088682
  12. Lee CH, Kim BS. Improvement of personnel cost and placement scale of quality manager for construction. KSCE Journal of Civil and Environmental Engineering Research. 2018 Apr;38(2):327-36. https://doi.org/10.12652/Ksce.2018.38.2.0327
  13. Cho JH. An empirical study on top management's leadership in construction quality management activities and construction quality management performance. Journal of the Korean Society for Quality Management. 2017 Sep;45(3):403-26. https://doi.org/10.7469/JKSQM.2017.45.3.403
  14. Lee CH, Kim BS. The establishment of roles and titles of quality control personnel for construction project quality assurance. KSCE Journal of the Korean Society of Civil Engineers. 2017 Oct;37(5):871-8. https://doi.org/10.12652/Ksce.2017.37.5.0871
  15. Moradi S, Ansari R, Taherkhani R. A systematic analysis of construction performance management: key performance indicators from 2000 to 2020. Iranian Journal of Science and Technology, Transactions of Civil Engineering. 2022 Feb;46:15-31. https://doi.org/10.1007/s40996-021-00626-7
  16. Keenan M, Rostami A. The impact of quality management systems on construction performance in the north west of england. International Journal of Construction Management. 2019 Apr;21(9):871-83. https://doi.org/10.1080/15623599.2019.1590974
  17. Demirkesen S, Ozorhon B. Measuring project management performance case of construction industry. Engineering Management Journal. 2017 Dec;29(4):258-77. https://doi.org/10.1080/10429247.2017.1380579
  18. Kim HY, Jang YE, Kang HB, Son JW, Yi JS. A suggestion of the direction of construction disaster document management through text data classification model based on deep learning. Korean Journal of Construction Engineering and Management. 2021 Sep;22(5):73-85. https://doi.org/10.6106/KJCEM.2021.22.5.073
  19. Lee JH. Construction engineering and management. Seoul (Korea): Korean Journal of Construction Engineering and Management; 2019. 62 p.
  20. Youn DH. A study on the unit-space lexicon use of architects in house design. Journal of the Regional Association of Architectural Institute of Korea. 2013 Apr;15(2):139-48.
  21. Liu B. Sentiment analysis and opinion mining. CA: Morgan & Claypool Publishers; 2012. 168 p.
  22. Kim DY, Park JW, Choi JH. A comparative study between stock price prediction models using sentiment analysis and machine learning based on sns and news articles. Journal of Information Technology Services. 2014 Sep;13(3):221-33. https://doi.org/10.9716/KITS.2014.13.3.221
  23. Jun JJ, Ahn SH, Lee MH, Hwang HJ. Research on how to build a sentiment dictionary for economic terms [Internet]. Seoul (Korea): The Bank of Korea; 2020 May 23. Available from: file:///C:/Users/User/Downloads/%EA%B2%BD%EC%A0%9C%EC%9A%A9%EC%96%B4%20%EA%B0%90%EC%84%B1%EC%82%AC%EC%A0%84%20%EA%B5%AC%EC%B6%95%EB%B0%A9%EC%95%88%20%EC%97%B0%EA%B5%AC.pdf
  24. Han SH. A study on development of 'lexicon division-emotion'. The Journal of the Humanities for Unification. 2018 Sep;75:33-70. https://doi.org/10.21185/jhu.2018.9.75.33
  25. An JK, Kim HW. Building a korean sentiment lexicon using collective intelligence. Journal of Intelligence and Information Systems. 2015 Jun;21(2):49-67. https://doi.org/10.13088/jiis.2015.21.2.49