Shrestha, K K (2016) Causes of change orders and its impact on road maintenance contracts. Unpublished PhD thesis, University of Nevada, Las Vegas, USA.
Abstract
Change orders (CO) commonly generate cost-growth, schedule-growth or both, in construction as well as in maintenance contracts. Literature reviews revealed that the causes and impact of CO on new construction contracts had been comprehensively studied, but the causes and impact of CO in maintenance contracts remained neglected. This study collected CO data on road maintenance contracts to determine the amount of CO and the most frequent and high-risk road maintenance activities that had CO. A Delphi study was conducted with 33 maintenance engineers from the state Department of Transportations (DOTs) to identify causes of CO and its impact on cost and schedule of road maintenance contracts. The results showed that the three important reasons of CO on the maintenance contracts were: changes in work scope, errors in the estimate, and failure to verify work site conditions before signing a contract. To reduce these CO, three most important preventive measures agreed by participants were: reviewing specifications, preparing accurate estimates, and reviewing the design drawing before bid solicitation. In this study, the CO contingency estimation tool was prepared using an artificial neural network (ANN) and a linear regression method. Historical CO data was used to predict the contingency cost for maintenance contracts. In order to reduce the negative impact on the schedule-growth, a schedule-crashing optimization tool was also developed. Hence, the primary contributions of this research to the body of knowledge are the quantification of the CO, the identification of the causes and preventive measures of CO, and the development of the tools to manage cost and schedule growth in road maintenance contracts.
Item Type: | Thesis (Doctoral) |
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Thesis advisor: | Shrestha, P P |
Uncontrolled Keywords: | failure; optimization; contingency; specifications; artificial neural network; maintenance contract; neural network; quantification |
Date Deposited: | 16 Apr 2025 19:33 |
Last Modified: | 16 Apr 2025 19:33 |