Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Development of Estimation Model of Construction Activity Duration Using Neural Network Theory

건설공사 공정별 작업기간 산정을 위한 신경망 기반 모형 구축

  • Cho, Bit-Na (Department of Civil Engineering, Gyeongsang National University) ;
  • Kim, Hyeon-Seung (Department of Civil Engineering, Gyeongsang National University) ;
  • Kang, Leen-Seok (Department of Civil Engineering, Gyeongsang National University)
  • 조빛나 (경상대학교 토목공학과, 공학연구원) ;
  • 김현승 (경상대학교 토목공학과, 공학연구원) ;
  • 강인석 (경상대학교 토목공학과, 공학연구원)
  • Received : 2014.11.26
  • Accepted : 2015.05.07
  • Published : 2015.05.31
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Abstract

A reasonable process for the activity duration estimation is required for the successful construction management because it directly affects the entire construction duration and budget. However, the activity duration is being generally estimated by the experience of the construction manager. This study suggests an estimation model of construction activity duration using neural network theory. This model estimates the activity duration by considering both the quantitative and qualitative elements, and the model is verified by a case study. Because the suggested model estimates the activity duration by a reasonable schedule plan, it is expected to reduce the error between planning duration and actual duration in a construction project.

공정계획 수립 시 각 공정별 작업기간 산정은 프로젝트 전체 공사기간 및 사업비용 결정과 직결되기 때문에 합리적인 산정계획이 요구된다. 그러나 일반적으로 작업기간 산정은 공사 담당자의 경험과 직관을 통해 이루어지고 있고, 다양한 영향요인에 의한 불확실성으로 인해 예측에 어려움이 있다. 이에 본 연구에서는 작업기간 산정에 영향을 미치는 다양한 요인을 고려할 수 있도록 신경망 기반 건설공사 공정별 작업기간 산정 모형을 제시하고자 한다. 본 연구에서는 정량적 및 정성적 요인을 모두 고려하여 작업기간 산정 모형을 구축하고, 사례적용을 통해 모형의 적용가능성을 검토하였다. 또한 영향요인 상관성분석을 실시하여 구축된 신경망 구조의 적합성을 판단하였다. 연구에서는 작업기간 산정 모형을 통해 합리적인 일정계획을 제공함으로써 계획공사기간과 실제공사기간의 오차율을 줄이는데 도움을 줄 수 있을 것으로 기대된다.

Keywords

References

  1. S. J. Kim, J. S. Lee, "An Optimal Scheduling Method Using Probability on the Estimation of Construction Duration", Korean Journal of Construction Engineering and Management, Vol.5, No.6, pp.72-79, 2004.
  2. D. C. Kwon, C. S. Lee, "The Estimation of Construction Duration for High School Buildings Based on the Actual Data", Korean Journal of Construction Engineering and Management, Vol.5, No.22, pp.138-145, 2004.
  3. A. H. Boussabaine, A. P. Kaka, "A Neural Networks Approach for Cost Flow Forecasting", Construction Management & Economics, Vol.16, Issue 4, pp.471-479, 1998. DOI: http://dx.doi.org/10.1080/014461998372240
  4. J. P. Woo, H. S, Cha, K. R. Kim, D. W. Shin, "A Study on Duration Calculation Method for Eco-Friendly Remodeling Demolition Work Using Productivity Analysis", Korean Journal of Construction Engineering and Management, Vol.14, No.1, pp.124-132, 2013. DOI: http://dx.doi.org/10.6106/KJCEM.2013.14.1.124
  5. J. W. Shin, H. G. Ryu, H. S. Lee, M. S. Park, "Probabilistic Model to Forecast the Duration of Structural Work in High-rise Building Construction Considering Weather Elements", Journal of Architectural Institute of Korea, Vol.23, No.6, pp.123-132, 2007.
  6. H. S. Lee, J. W. Shin, M. S. Park, H. G. Ryu, "Probabilistic Duration Estimation Model for High-Rise Structural Work", Journal of Construction Engineering & Management, Vol.135, Issue 12, pp.1289-1298, 2009. DOI: http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000105
  7. H. D. Han, J. H. Kim, J. H. Yoon, J. W. Seo, "Road Construction Cost Estimation Model in the Planning Phase Using Artificial Neural Network", Journal of the Korean Society of Civil Engineers, Vol.31, No.6, pp.829-837, 2011.
  8. W. Pewdum, T. Rujirayanyong, V. Sooksatra, "Forecasting Final Budget and Duratio of Highway Construction Projects", Engineering, Construction and Architectural Management, 16(6), pp.544-557, 2009. DOI: http://dx.doi.org/10.1108/09699980911002566
  9. S. C. Ok, S. K. Sinha, "Construction Equipment Productivity Estimation using Artificial Neural Network Model", Construction Management and Economics, Vol.24, Issue 10, pp.1029-1044, 2006. DOI: http://dx.doi.org/10.1080/01446190600851033
  10. Hag-young Han. Introduction to Pattern Recognition. p.1-572, HANBIT Media, 2009.
  11. Hee-Sun Jung. Rainfall Adjust and Forecasting in Seoul Using a Artificial Neural Network Technique Including a Correlation Coefficient. Master Thesis, University of Yonsei, Seoul, Korea, 2007.