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: | , |
|---|---|
| Format: | Journal Article |
| Published: |
Australian Institute of Quantity Surveyors
2013
|
| Subjects: | |
| Online Access: | http://epress.lib.uts.edu.au/journals/index.php/AJCEB/article/view/3363 http://hdl.handle.net/20.500.11937/16292 |
| Summary: | 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. |
|---|