Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects
Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective LCCA comparison of (sub)component material specifications. To address t...
| Main Authors: | , |
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| Format: | Journal Article |
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Australian Institute of Quantity Surveyors
2013
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| Online Access: | http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/3363 http://hdl.handle.net/20.500.11937/16292 |
| _version_ | 1848749134405697536 |
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| author | Alqahtani, Ayedh Whyte, Andrew |
| author_facet | Alqahtani, Ayedh Whyte, Andrew |
| author_sort | Alqahtani, Ayedh |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective LCCA comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANN is a powerful means to handle non-linear problems and subsequently map relationships between complex input/output data and address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method using MATLAB SOFTWARE; and secondly, spread-sheet optimisation using Microsoft Excel Solver. The best network used 19 hidden nodes, with the tangent sigmoid used as a transfer function for both methods. The results is that in both models, the accuracy of the developed NN model is 1% (via Excel-solver) and 2% (via back-propagation) respectively. |
| first_indexed | 2025-11-14T07:16:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-16292 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:16:06Z |
| publishDate | 2013 |
| publisher | Australian Institute of Quantity Surveyors |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-162922017-01-30T11:54:58Z Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects Alqahtani, Ayedh Whyte, Andrew Excel solver Life Cycle Cost Analysis (LCCA) Cost Significant Items (CSIs) Back-propagation Artificial Neural Networks (ANNs) Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective LCCA comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANN is a powerful means to handle non-linear problems and subsequently map relationships between complex input/output data and address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method using MATLAB SOFTWARE; and secondly, spread-sheet optimisation using Microsoft Excel Solver. The best network used 19 hidden nodes, with the tangent sigmoid used as a transfer function for both methods. The results is that in both models, the accuracy of the developed NN model is 1% (via Excel-solver) and 2% (via back-propagation) respectively. 2013 Journal Article http://hdl.handle.net/20.500.11937/16292 http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/3363 Australian Institute of Quantity Surveyors fulltext |
| spellingShingle | Excel solver Life Cycle Cost Analysis (LCCA) Cost Significant Items (CSIs) Back-propagation Artificial Neural Networks (ANNs) Alqahtani, Ayedh Whyte, Andrew Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects |
| title | Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects |
| title_full | Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects |
| title_fullStr | Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects |
| title_full_unstemmed | Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects |
| title_short | Artificial Neural Networks Incorporating Cost Significant Items towards Enhancing Estimation for (life-cycle) Costing of Construction Projects |
| title_sort | artificial neural networks incorporating cost significant items towards enhancing estimation for (life-cycle) costing of construction projects |
| topic | Excel solver Life Cycle Cost Analysis (LCCA) Cost Significant Items (CSIs) Back-propagation Artificial Neural Networks (ANNs) |
| url | http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/3363 http://hdl.handle.net/20.500.11937/16292 |