Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects
| dc.contributor.author | Alqahtani, Ayedh Mohammad A | |
| dc.contributor.supervisor | Prof. Hamid Nikraz | |
| dc.contributor.supervisor | Dr Andrew Whyte | |
| dc.date.accessioned | 2017-01-30T10:20:43Z | |
| dc.date.available | 2017-01-30T10:20:43Z | |
| dc.date.created | 2015-12-10T07:44:06Z | |
| dc.date.issued | 2015 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/2354 | |
| dc.description.abstract |
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%. | |
| dc.language | en | |
| dc.publisher | Curtin University | |
| dc.title | Utilising artificial neural networks (ANNs) towards accurate estimation of life-cycle costs for construction projects | |
| dc.type | Thesis | |
| dcterms.educationLevel | PhD | |
| curtin.department | Department of Civil Engineering | |
| curtin.accessStatus | Open access |