Abstract
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. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, 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 was adopted (using MATLAB SOFTWARE); and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver). The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.
Highlights
Stakeholders of built assets are increasingly required to extend estimation beyond the initial capital-cost
There are two types of data needed to create a neural network model: input data consisting of data identified as key to the result of the cost estimation model representing cost significant items (CSI) and the important non-cost factors; and output data consisting of the data collected from the database representing the actual value of total costs of previous projects
The results indicate that the running cost model developed by back-propagation neural network model performs well; no important difference could is recognised between the estimated and actual running costs
Summary
Stakeholders of built assets are increasingly required to extend estimation beyond the initial capital-cost. This includes all stages of an asset’s life-cycle, through incorporation of approaches such as benefit-to-cost ratio, internal rate of return and life cycle cost analysis (LCCA) to evaluate the total life cost of construction (Anurag Shankar et al 2010). Estimates of inflation and a Australasian Journal of Construction Economics and Building ‘correct’ discount rate measuring the time-value-of-money so that design alternatives for a project, may be compared are deemed somewhat overly subjective, adding to uncertainty and risk and affecting LCC result accuracy. Traditional methods remain restrictive, as LCC prediction of the large number of variable factors affects the ‘value’ of the construction cost, compounded by complicated interaction between these factors (Cheng et al 2010), and coupled with the reluctance of busy practitioner’s to predict future discount-rate multipliers, continues to restrict LCCA usage by the construction industry
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