Elhag, T M S and Boussabaine, A H (1998) An artificial neural system for cost estimation of construction projects. In: Hughes, W (ed.) Proceedings of 14th Annual ARCOM Conference, 9-11 September 1998, Reading, UK.
Abstract
Cost estimation is an experience-based task, which involves evaluations of unknown circumstances and complex relationships of cost-influencing factors. An artificial neural network (ANN) is an analogy-based process, which best suits the cost forecasting domain. The primary advantages of ANNs include their ability to learn by examples (past projects), and to generalize solutions for forthcoming applications (future projects). ANNs do not require a prerequisite establishment of rules and reasoning which govern relationships between a desired output and its significant effective variables. Two ANN models have been developed to predict the lowest tender price of primary and secondary school buildings. Thirty projects were involved in this study and their pertaining data was extracted from the BCIS database. Model I utilizes 13 cost-determinant attributes, but in contrast only 4 input variables are involved in developing model II. The findings show that, the two ANN models effectively learned during training stage, and gained good generalization capabilities in testing session. The ANN model I and II managed to achieve average accuracy percentages of 79.3% and 82.2% respectively.
| Item Type: | Conference Paper (Paper) |
|---|---|
| Uncontrolled Keywords: | artificial neural network; back-propagation algorithm; cost forecasting techniques; cost influencing factors; lowest tender price |
| Index terms: | reasoning, cost estimating, accuracy, influencing factor, testing, secondary school, database, cost forecasting, artificial neural network, construction project, lowest tender, propagation, determinant |
| Subjects: | data management, cognitive psychology, modelling and simulation, professional development, financial and cost management, production management, tendering, professional practice, educational institutions, economics, engineering process, risk assessment |
| Topics: | Project Management, Education, Procurement, Cost Management, Risk Management, Digital Applications, Research Practice, Engineering Principles, Information Management |
| Descriptive scope: | 2 PC |
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