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Logistic Regression Modeling to Determine Projects impacted by Schedule Compression

  • Chul-Ki Chang
  • Awad S. Hanna
  • Sungkwon WooEmail author
  • Chung-Suk Cho
Construction Management
  • 3 Downloads

Abstract

The competitive market realities in industrial environments demand timely completion of construction projects making time conservation a major concern for both owners and contractors. And the unpredictability of a construction project often leads to disputes followed by litigations between owners and contractors. Schedule compression is a common practice to achieve this timely completion of projects, however, can have detrimental consequences in terms of labor productivity and subsequent cost increase. Loss of productivity, however, is difficult to quantify especially when stemming from compressed schedule. Numerous researchers and trade associations have developed productivity factors to quantify the impact of schedule compression on labor productivity, but there has not been a method to quantitatively determine whether the project was impacted by schedule compression or not. This paper introduces a logistic regression impact model by analyzing the quantitative definition of schedule compression. The model will enable the user to determine if the schedule compression resulted in productivity loss or not. Based on the analysis of eight different factors, the logistic model will allow contractors and owners to determine the probability of a project being impacted by schedule compression.

Keywords

schedule compression labor productivity logistic regression model developing construction project management 

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Copyright information

© Korean Society of Civil Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chul-Ki Chang
    • 1
  • Awad S. Hanna
    • 2
  • Sungkwon Woo
    • 3
    Email author
  • Chung-Suk Cho
    • 4
  1. 1.Dept. of Architectural and Civil EngineeringHannam UniversityDaejeonKorea
  2. 2.Dept. of Civil and Environmental EngineeringUniversity of Wisconsin – MadisonMadisonUSA
  3. 3.Dept. of Civil EngineeringInha UniversityIncheonKorea
  4. 4.Dept. of Civil, Infrastructure and Environmental EngineeringKhalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates

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