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    Overruns in Transportation Infrastructure Projects

    Access Status
    Fulltext not available
    Authors
    Love, Peter
    Sing, Michael
    Wang, Xiangyu
    Irani, Z.
    Thwala, D.
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Love, P. and Sing, M. and Wang, X. and Irani, Z. and Thwala, D. 2014. Overruns in Transportation Infrastructure Projects. Structure and Infrastructure Engineering. 10 (2): pp. 141-159.
    Source Title
    Structure and Infrastructure Engineering
    DOI
    10.1080/15732479.2012.715173
    ISSN
    1573-2479
    School
    Department of Civil Engineering
    URI
    http://hdl.handle.net/20.500.11937/8114
    Collection
    • Curtin Research Publications
    Abstract

    Transportation infrastructure projects are prone to cost and schedule overruns. At the time of contract award, a construction contingency budget is often used to accommodate for unplanned events such as scope changes. Recent empirical research has shown that rework during construction as a result of design changes, errors and omission are the major contributors of overruns in projects. The statistical characteristics of rework, and cost and schedule overruns that are experienced from a project’s contract award for 58 Australian transportation infrastructureprojects are analysed. Theoretical probability distributions are fitted to the rework, cost and schedule overrun data. Goodness of fit tests are used in conjunction with probability-probability (P-P) plots to compare the sample distribution from the known theoretical distribution. A Generalised Logistic probability density function is found to describe the behaviour of cost-overruns and provides the best overall distribution fit. The best fitting distribution for schedule overruns and rework data were the Four Parameter Burr and a Johnson SB distribution, respectively. Thedistributions are used to calculate the probability of rework, cost and schedule overruns being experienced. A case illustration is presented and discussed to demonstrate how the derived probabilities may be utilised in practice.

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