DOI QR코드

DOI QR Code

Structural Analysis of Earthwork Productivity Influence Factors Using Fuzzy DEMATEL Method

Fuzzy DEMATEL 방법을 활용한 토공사 생산성 영향요인 구조분석

  • Lee, Chanwoo (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Kim, Hyeonmin (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Kim, Hyungjun (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Cho, Hunhee (School of Civil, Environmental and Architectural Engineering, Korea University)
  • Received : 2023.09.25
  • Accepted : 2023.11.08
  • Published : 2023.12.20

Abstract

Enhancing productivity in earthwork projects is crucial, significantly affecting both time and cost efficiencies. However, existing research in this domain predominantly relies on qualitative data and methodologies, which may not suffice given its critical significance. This study employed the fuzzy DEMATEL method to conduct a structural analysis of variables affecting productivity in construction projects. The findings reveal that plan changes possess the most substantial overall influence on earthwork productivity, with a comprehensive strength rating of 4.58. Additionally, it was observed that precipitation data exerted the most pronounced positive impact, with a rating of 0.48. These insights are anticipated to aid in identifying and prioritizing areas for productivity enhancement in construction projects.

토공사 프로젝트에서 생산성 향상은 공기와 비용에 직접적인 영향을 미치는 중요한 키워드이다. 그러나 토공사의 생산성에 대한 기존 연구는 정성적인 데이터와 방법에 의존하는 경우가 많아 중요성에 비해 미흡한 실정이다. 본 연구에서는 fuzzy DEMATEL 방법을 적용하여 토공사의 생산성에 영향을 미치는 요인에 대한 구조분석을 수행하였다. 본 연구의 결과는 토공사 생산성 향상을 위한 우선 개선사항 도출에 기여할 것으로 판단된다.

Keywords

Acknowledgement

This research was conducted with the support of the "National R&D Project for Smart Construction Technology (No.23SMIP-A158708-04)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

References

  1. Korea Specialty Contractors Association [Internet]. Seoul (Korea): 2021 Contract performance of industrial classification. 2022 [updated 2022 Nov 14; cited 2023 Sep 1]. Available from: https://www.kosca.or.kr/I0/I040601.asp?GBN=C&m_y=2021&area=00
  2. Won SK, Han CH, Kim SK. A combination model of earthwork equipment using system dynamics. Korean Journal of Construction Engineering and Management. 2007 Aug 31;8(4):194-202.
  3. Kim JH, Seo JW. BIM based intelligent excavation system. Journal of KIBIM. 2011 Mar;1(1):1-5. https://doi.org/10.13161/kibim.2011.1.1.001
  4. Lim JI, Kim YS, Kim YS, Kim SB. A process of selecting productivity influencing factors for forecasting construction productivity. Korean Journal of Construction Engineering and Management. 2008 Aug;9(4):92-100.
  5. Yu JH, Lee HS. Productivity management system for construction projects. Journal of the Architectural Institute of Korea Structure & Construction. 2002 Jul;18(7):103-13.
  6. Amede E. A relationship between productivity and significant controlling factors of highway construction earthwork. Cogent Engineering. 2022 Aug;9(1):2114203. https://doi.org/10.1080/23311916.2022.2114203
  7. Papa I, Picchio R, Lovrincevic M, Janes D, Pentek T, Validzic D, Venanzi R, Duka A. Factors affecting earthwork volume in forest road construction on steep terrain. Land. 2023 Feb;12(2):400. https://doi.org/10.3390/land12020400
  8. Zakeri M, Olomolaiye P, Holt GD, Harris FC. Factors affecting the motivation of Iranian construction operatives. Building and Environment. 1997 Mar;32(2):161-6. https://doi.org/10.1016/S0360-1323(96)00044-3
  9. Mengistu M, Quezon ET, Kebede G. Assessment of factors affecting labor productivity on road construction projects in Oromia region, bale zone. International Journal of Scientific & Engineering Research. 2016 Nov;7(11):899-910.
  10. Lee SB, Pyo YM. A study on the analysis of factors decreasing construction labor-productivity using AHP method. Journal of the Regional Association of Architectural Institute of Korea. 2007 Feb;9(1):179-87.
  11. Jeong JH, Lee SW, Ahn BJ, Jee NY, Kim JJ. A comparative analysis of hindrance factors to labor productivity in each construction site using the IPA. Korean Journal of Construction Engineering and Management. 2014 Nov;15(6):71-82. https://doi.org/10.6106/KJCEM.2014.15.6.071
  12. Bellman RE, Zadeh LA. Decision-making in a fuzzy environment. Management Science. 1970 Dec;17(4):B141. https://doi.org/10.1287/mnsc.17.4.B141
  13. Zadeh LA. Fuzzy sets. Information and Control. 1965 Jun;8(3):338-53. https://doi.org/10.1016/S0019-9958(65)90241-X
  14. Al-Najjar B, Alsyouf I. Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. International Journal of Production Economics. 2003 Apr;84(1):85-100. https://doi.org/10.1016/S0925-5273(02)00380-8
  15. Opricovic S, Tzeng GH. Defuzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2003 Oct;11(5):635-52. https://doi.org/10.1142/S0218488503002387
  16. Yager RR, Filev DP. Generation of fuzzy rules by mountain clustering. Journal of Intelligent and Fuzzy Systems. 1994 Jan;2(3):209-19. https://doi.org/10.3233/IFS-1994-2301
  17. Shieh JI, Wu HH, Huang KK. A DEMATEL method in identifying key success factors of hospital service quality. Knowlege-Based Systems. 2010 Apr;23(3):277-83. https://doi.org/10.1016/j.knosys.2010.01.013
  18. Li RJ. Fuzzy method in group decision making. Computers & Mathematics with Applications. 1999 Jul;38(1):91-101. https://doi.org/10.1016/S0898-1221(99)00172-8