Change Order

Only available on StudyMode
  • Download(s) : 54
  • Published : February 15, 2013
Open Document
Text Preview
Decision Tree Approach to Classify and Quantify Cumulative Impact of Change Orders on Productivity Min-Jae Lee, M.ASCE1; Awad S. Hanna, M.ASCE2; and Wei-Yin Loh3 Downloaded from ascelibrary.org by CONCORDIA UNIVERSITY LIBRARIES on 01/14/13. Copyright ASCE. For personal use only; all rights reserved.

Abstract: Multiple or unusual change orders often cause productivity losses through a ‘‘ripple effect’’ or ‘‘cumulative impact’’ of changes. Many courts and administrative boards recognize that there is cumulative impact above and beyond the change itself. However, determination of the impact and its cost is difficult due to the interconnected nature of construction work and the difficulty in isolating causal factors and their effects. As a result, it is very difficult for owners and contractors to agree on equitable adjustments for the cumulative impact. What is needed is a reliable method model to identify and quantify the loss of productivity caused by the cumulative impact of change orders. A number of studies have attempted to quantify the impact of change orders on the project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, traditional regression analysis has shortcomings in dealing with highly correlated multivariable data. Moreover, regression analysis has shown limited success when dealing with many qualitative or noisy input factors. Classification and regression tree methods have the ability to deal with these complex multifactor modeling problems. This study develops decision tree models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders for electrical and mechanical projects. The results show that decision tree models give significantly improved results for classification and quantification compared to traditional statistical methods in the field of construction productivity data analysis, which is characterized by noisiness and uncertainty. DOI: 10.1061/ ASCE 0887-3801 2004 18:2 132 CE Database subject headings: Change order; Productivity; Claims; Decision theory.

Introduction
Multiple or unusual change orders often cause productivity loss because construction is based upon sequential production. Therefore, any disruption to a task in the sequence will impact the remaining tasks even if the change order itself does not involve these tasks. This is commonly referred to as the ‘‘ripple effect’’ or ‘‘cumulative impact’’ of changes. Many courts and administrative boards recognize that there is cumulative impact above and beyond the change itself Hensel Phelps Construction Co. v. General Services Administration, GSBCA Lexis 10 2001 . However, current construction contracts do not typically include adequate language to enable fair and equitable compensation for the unforeseen cumulative impact of changes. Often, the contractor fails to foresee, and the owner fails to acknowledge, the ‘‘synergistic effect’’ of the changes on the work as a whole when pricing individual changes. Consequently, projects that exceed cost or schedule targets are likely to lead to claims. Determining the impact 1 Professor, Dept. of Civil Engineering, Chungnam National Univ., 220 Gung-dong Yuseong-gu, Daejeon, South Korea 305-764. E-mail: lmjcm@hanmail.net 2 Professor, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin—Madison, 2314 Engineering Hall, 1415 Engineering Dr., Madison, WI 53706. E-mail: hanna@engr.wisc.edu 3 Professor, Dept. of Statistics, Univ. of Wisconsin—Madison, 1210 West Dayton St., Madison, WI 53706. E-mail: loh@stat.wisc.edu Note. Discussion open until September 1, 2004. Separate discussions must be submitted for individual papers. To extend the closing date by one month, a written request must be filed with the ASCE Managing Editor. The manuscript for this paper was submitted for review and possible publication on February 24, 2003; approved on June 4, 2003. This paper is part of the Journal of...
tracking img