Mapping rework causes and effects using artificial neural networks
Rework can have adverse effects on the performance and productivity of construction projects. Techniques such as artificial neural networks (ANN) are widely used for prediction and classification problems and thus can be used to map the causes and effects of rework. The traditional back propagation...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Routledge
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/21344 |
| _version_ | 1848750563893706752 |
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| author | Palaneeswaran, E. Love, Peter Kumaraswamy, M. Ng, T. |
| author_facet | Palaneeswaran, E. Love, Peter Kumaraswamy, M. Ng, T. |
| author_sort | Palaneeswaran, E. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Rework can have adverse effects on the performance and productivity of construction projects. Techniques such as artificial neural networks (ANN) are widely used for prediction and classification problems and thus can be used to map the causes and effects of rework. The traditional back propagation neural network and general regression neural network data from 112 Hong Kong construction projects are used to examine the influence of rework causes on the various project performance indicators such as cost overrun, time overrun, and contractual claims. The results from this research could be used to develop forecasting systems and appropriate intelligent decision support frameworks for enhancing performance in construction projects. Furthermore, analysis of the neural network results indicates that the general regression neural network architecture is better suited for modelling rework causes and their impacts on project performance. |
| first_indexed | 2025-11-14T07:38:50Z |
| format | Journal Article |
| id | curtin-20.500.11937-21344 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:38:50Z |
| publishDate | 2008 |
| publisher | Routledge |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-213442017-09-13T13:54:26Z Mapping rework causes and effects using artificial neural networks Palaneeswaran, E. Love, Peter Kumaraswamy, M. Ng, T. Rework can have adverse effects on the performance and productivity of construction projects. Techniques such as artificial neural networks (ANN) are widely used for prediction and classification problems and thus can be used to map the causes and effects of rework. The traditional back propagation neural network and general regression neural network data from 112 Hong Kong construction projects are used to examine the influence of rework causes on the various project performance indicators such as cost overrun, time overrun, and contractual claims. The results from this research could be used to develop forecasting systems and appropriate intelligent decision support frameworks for enhancing performance in construction projects. Furthermore, analysis of the neural network results indicates that the general regression neural network architecture is better suited for modelling rework causes and their impacts on project performance. 2008 Journal Article http://hdl.handle.net/20.500.11937/21344 10.1080/09613210802128269 Routledge restricted |
| spellingShingle | Palaneeswaran, E. Love, Peter Kumaraswamy, M. Ng, T. Mapping rework causes and effects using artificial neural networks |
| title | Mapping rework causes and effects using artificial neural networks |
| title_full | Mapping rework causes and effects using artificial neural networks |
| title_fullStr | Mapping rework causes and effects using artificial neural networks |
| title_full_unstemmed | Mapping rework causes and effects using artificial neural networks |
| title_short | Mapping rework causes and effects using artificial neural networks |
| title_sort | mapping rework causes and effects using artificial neural networks |
| url | http://hdl.handle.net/20.500.11937/21344 |