Emsley, M W; Lowe, D J; Duff, A R; Harding, A and Hickson, A (2002) Data modelling and the application of a neural network approach to the prediction of total construction costs. Construction Management and Economics, 20(6), pp. 465-472. ISSN 01446193
Abstract
Neural network cost models have been developed using data collected from nearly 300 building projects. Data were collected from predominantly primary sources using real-life data contained in project files, with some data obtained from the Building Cost Information Service, supplemented with further information, and some from a questionnaire distributed nationwide. The data collected included final account sums and, so that the model could evaluate the total cost to the client, clients' external and internal costs, in addition to construction costs. Models based on linear regression techniques have been used as a benchmark for evaluation of the neural network models. The results showed that the major benefit of the neural network approach was the ability of neural networks to model the nonlinearity in the data. The 'best' model obtained so far gives a mean absolute percentage error (MAPE) of 16.6%, which includes a percentage (unknown) for client changes. This compares favourably with traditional estimating where values of MAPE between 20.8% and 27.9% have been reported. However, it is anticipated that further analyses will result in the development of even more reliable models.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | cost modelling; linear regression analysis; neural networks |
| Index terms: | construction cost, cost information, regression analysis, data modelling, modelling, neural network, estimating, final account, questionnaire, cost model |
| Subjects: | accounting and finance, statistical analysis, payment, data collection methods, analytical methods, financial and cost management, data science, artificial intelligence |
| Topics: | Cost Management, Engineering Principles, Research Practice, Contract Administration, Digital Applications |
| Descriptive scope: | 3 PCA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here