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The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy.

A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs.

The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects.

A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample.

This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

A rich variety of cost models is used in the world's construction industries. In countries exposed to British practice, the use of traditional bills of quantities is common. Elsewhere, bills of quantities may not be used at all. This paper briefly reviews the nature and purpose of cost models both in the UK and in south‐east Asia. It explains how the principle of cost‐significance can lead to a simplified method of measurement which is both well‐structured and sufficiently accurate ‐ a half‐way house between traditional bills and a single lump sum. By way of example, the derivation of a cost‐significant model for student hostels in Singapore is presented. Representing no more than a first step, the problems still to be resolved are outlined. Nevertheless, the techniques seem to hold much promise for the future, and others are encouraged to explore where they might most effectively be applied.