Predicting project cost overrun levels in bidding stage using ensemble learning
Predicting project cost overruns in the bidding stage has undergone significant changes with the application of state-of-the-art techniques. Both modeling techniques and domain knowledge should be integrated to enhance predictions of cost performance. This study developed an ensemble-learning classi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-11-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2020.1765171 |
| _version_ | 1797755671582605312 |
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| author | Hyosoo Moon Trefor P. Williams Hyun-Soo Lee Moonseo Park |
| author_facet | Hyosoo Moon Trefor P. Williams Hyun-Soo Lee Moonseo Park |
| author_sort | Hyosoo Moon |
| collection | DOAJ |
| description | Predicting project cost overruns in the bidding stage has undergone significant changes with the application of state-of-the-art techniques. Both modeling techniques and domain knowledge should be integrated to enhance predictions of cost performance. This study developed an ensemble-learning classification model to predict the expected cost-overrun levels of public projects and derive explanatory factors and key predictors. A database of 234 public-sector projects in South Korea was used, including project characteristics (i.e., project delivery method, project types, cost, and schedule) in combination with bidding characteristics (i.e., award method, number of bidders, bid to estimate ratio, number of joint ventures). The results yielded an average accuracy of 61.41% for five model runs. Furthermore, information on the project type being constructed is an important contributor to prediction accuracy. Results of the model enable project owners and managers to screen projects that are expected to incur excessive cost overruns and to anticipate budget loss during the bidding stage and before contracts are finalized. |
| first_indexed | 2024-03-12T17:50:31Z |
| format | Article |
| id | doaj.art-64f364f2efc54598be5fa5b95b69a6af |
| institution | Directory Open Access Journal |
| issn | 1347-2852 |
| language | English |
| last_indexed | 2024-03-12T17:50:31Z |
| publishDate | 2020-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj.art-64f364f2efc54598be5fa5b95b69a6af2023-08-03T09:07:49ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522020-11-0119658659910.1080/13467581.2020.17651711765171Predicting project cost overrun levels in bidding stage using ensemble learningHyosoo Moon0Trefor P. Williams1Hyun-Soo Lee2Moonseo Park3Seoul National UniversityRutgers UniversitySeoul National UniversitySeoul National UniversityPredicting project cost overruns in the bidding stage has undergone significant changes with the application of state-of-the-art techniques. Both modeling techniques and domain knowledge should be integrated to enhance predictions of cost performance. This study developed an ensemble-learning classification model to predict the expected cost-overrun levels of public projects and derive explanatory factors and key predictors. A database of 234 public-sector projects in South Korea was used, including project characteristics (i.e., project delivery method, project types, cost, and schedule) in combination with bidding characteristics (i.e., award method, number of bidders, bid to estimate ratio, number of joint ventures). The results yielded an average accuracy of 61.41% for five model runs. Furthermore, information on the project type being constructed is an important contributor to prediction accuracy. Results of the model enable project owners and managers to screen projects that are expected to incur excessive cost overruns and to anticipate budget loss during the bidding stage and before contracts are finalized.http://dx.doi.org/10.1080/13467581.2020.1765171predicting cost overrun levelsensemble learningbidding characteristicsproject characteristicsproject typeproject delivery method |
| spellingShingle | Hyosoo Moon Trefor P. Williams Hyun-Soo Lee Moonseo Park Predicting project cost overrun levels in bidding stage using ensemble learning Journal of Asian Architecture and Building Engineering predicting cost overrun levels ensemble learning bidding characteristics project characteristics project type project delivery method |
| title | Predicting project cost overrun levels in bidding stage using ensemble learning |
| title_full | Predicting project cost overrun levels in bidding stage using ensemble learning |
| title_fullStr | Predicting project cost overrun levels in bidding stage using ensemble learning |
| title_full_unstemmed | Predicting project cost overrun levels in bidding stage using ensemble learning |
| title_short | Predicting project cost overrun levels in bidding stage using ensemble learning |
| title_sort | predicting project cost overrun levels in bidding stage using ensemble learning |
| topic | predicting cost overrun levels ensemble learning bidding characteristics project characteristics project type project delivery method |
| url | http://dx.doi.org/10.1080/13467581.2020.1765171 |
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