Development of an approximate construction duration prediction model during the project planning phase for general office buildings

Dong Jun Yeom, Hae Mi Seo, Yoo Jun Kim, Chung Suk Cho, Youngsuk Kim

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Accurate prediction of the construction duration is imperative to the reliable cash flow analysis during the project planning phase when feasibility analysis is carried out. However, lack of information and frequent changes that occur as a result of a negotiation process between the owner and the designer in defining the project scope make it difficult to compute real-time construction duration. Domestic and foreign models for calculating the construction durations cannot be readily applied to computation of construction duration for general office buildings in Korea specifically during the project planning phase as there is a limit in its applicability due to numerous restrictions. Moreover, there are no preceding studies suggesting different computational approaches to predict the entire construction duration for office buildings with the approximate construction duration concept during planning phase. Therefore, based on the collected performance data, this study proposes a multiple linear regression model that facilitates reliable prediction of approximate construction duration for office buildings in the project planning phase. The model will allow the owner and other stakeholders to predict the real-time construction duration using the basic information on office buildings and to assess the construction durations incorporating frequent changes during the project planning phase.

Original languageBritish English
Pages (from-to)238-253
Number of pages16
JournalJournal of Civil Engineering and Management
Volume24
Issue number3
DOIs
StatePublished - 2018

Keywords

  • Approximate duration
  • Construction schedule
  • General office building
  • Multiple linear regression analysis
  • Prediction model
  • Project planning phase

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