Determining the probability of project cost overruns
The statistical characteristics of cost overruns experienced from contract award in 276 Australian construction and engineering projects were analyzed. The skewness and kurtosis values of the cost overruns are computed to determine if the empirical distribution of the data follows a normal distribut...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
American Society of Civil Engineers
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/41028 |
| _version_ | 1848756031256002560 |
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| author | Love, Peter Wang, Xiangyu Sing, Michael Tiong, Robert |
| author_facet | Love, Peter Wang, Xiangyu Sing, Michael Tiong, Robert |
| author_sort | Love, Peter |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The statistical characteristics of cost overruns experienced from contract award in 276 Australian construction and engineering projects were analyzed. The skewness and kurtosis values of the cost overruns are computed to determine if the empirical distribution of the data follows a normal distribution. The Kolmogorov-Smirnov, Anderson-Darling, and chi-squared nonparametric tests are used to determine the goodness of fit of the selected probability distributions. A three-parameter Frechet probability function is found to describe the behavior of cost overruns and provide the best overall distribution fit. The Frechet distribution is then used to calculate the probability of a cost overrun being experienced. The statistical characteristics of contract size and cost overruns were also analyzed. The Cauchy ([Math Processing Error]), Wakeby (A$1 to 10 million, [Math Processing Error]) and four-parameter Burr (A$11 to 50 million) tests were found to provide the best distribution fits and used to calculate cost overrun probabilities by contract size. Ascertaining the best fit probability distribution from an empirical distribution at contract award can produce realistic probabilities of cost overruns, which should then be incorporated into a construction cost contingency. |
| first_indexed | 2025-11-14T09:05:44Z |
| format | Journal Article |
| id | curtin-20.500.11937-41028 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:05:44Z |
| publishDate | 2013 |
| publisher | American Society of Civil Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-410282017-09-13T14:28:47Z Determining the probability of project cost overruns Love, Peter Wang, Xiangyu Sing, Michael Tiong, Robert Probability Cost overrun Probability distribution Australia Distribution fitting The statistical characteristics of cost overruns experienced from contract award in 276 Australian construction and engineering projects were analyzed. The skewness and kurtosis values of the cost overruns are computed to determine if the empirical distribution of the data follows a normal distribution. The Kolmogorov-Smirnov, Anderson-Darling, and chi-squared nonparametric tests are used to determine the goodness of fit of the selected probability distributions. A three-parameter Frechet probability function is found to describe the behavior of cost overruns and provide the best overall distribution fit. The Frechet distribution is then used to calculate the probability of a cost overrun being experienced. The statistical characteristics of contract size and cost overruns were also analyzed. The Cauchy ([Math Processing Error]), Wakeby (A$1 to 10 million, [Math Processing Error]) and four-parameter Burr (A$11 to 50 million) tests were found to provide the best distribution fits and used to calculate cost overrun probabilities by contract size. Ascertaining the best fit probability distribution from an empirical distribution at contract award can produce realistic probabilities of cost overruns, which should then be incorporated into a construction cost contingency. 2013 Journal Article http://hdl.handle.net/20.500.11937/41028 10.1061/(ASCE)CO.1943-7862.0000575 American Society of Civil Engineers restricted |
| spellingShingle | Probability Cost overrun Probability distribution Australia Distribution fitting Love, Peter Wang, Xiangyu Sing, Michael Tiong, Robert Determining the probability of project cost overruns |
| title | Determining the probability of project cost overruns |
| title_full | Determining the probability of project cost overruns |
| title_fullStr | Determining the probability of project cost overruns |
| title_full_unstemmed | Determining the probability of project cost overruns |
| title_short | Determining the probability of project cost overruns |
| title_sort | determining the probability of project cost overruns |
| topic | Probability Cost overrun Probability distribution Australia Distribution fitting |
| url | http://hdl.handle.net/20.500.11937/41028 |