Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects
This study aimed to establish a new model of Life Cycle Cost (LCC) for construction projects using Artificial Neural Networks (ANNs). Survey research and Costs Significant Items (CSIs) methods were conducted to identify the most important cost and non-cost factors affecting the estimation of LCC. T...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
Curtin University
2015
|
| Online Access: | http://hdl.handle.net/20.500.11937/2354 |
| _version_ | 1848743931551940608 |
|---|---|
| author | Alqahtani, Ayedh Mohammad A |
| author_facet | Alqahtani, Ayedh Mohammad A |
| author_sort | Alqahtani, Ayedh Mohammad A |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This study aimed to establish a new model of Life Cycle Cost (LCC) for construction projects using Artificial Neural Networks (ANNs). Survey research and Costs Significant Items (CSIs) methods were conducted to identify the most important cost and non-cost factors affecting the estimation of LCC. These important factors are considered as input factors of the model. The results indicated that neural network models were able to estimate the cost with an average accuracy between 91%-95%. |
| first_indexed | 2025-11-14T05:53:25Z |
| format | Thesis |
| id | curtin-20.500.11937-2354 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:53:25Z |
| publishDate | 2015 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-23542017-02-20T06:38:10Z Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects Alqahtani, Ayedh Mohammad A This study aimed to establish a new model of Life Cycle Cost (LCC) for construction projects using Artificial Neural Networks (ANNs). Survey research and Costs Significant Items (CSIs) methods were conducted to identify the most important cost and non-cost factors affecting the estimation of LCC. These important factors are considered as input factors of the model. The results indicated that neural network models were able to estimate the cost with an average accuracy between 91%-95%. 2015 Thesis http://hdl.handle.net/20.500.11937/2354 en Curtin University fulltext |
| spellingShingle | Alqahtani, Ayedh Mohammad A Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects |
| title | Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects |
| title_full | Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects |
| title_fullStr | Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects |
| title_full_unstemmed | Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects |
| title_short | Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects |
| title_sort | utilising artificial neural networks (anns) towards accurate estimation of life-cycle costs for construction projects |
| url | http://hdl.handle.net/20.500.11937/2354 |