Li, H; Shen, L Y and Love, P E D (1999) Ann-based mark-up estimation system with self-explanatory capacities. Journal of Construction Engineering and Management, 125(3), pp. 185-189. ISSN 0733-9364
Abstract
Artificial neural networks (ANNs) have been applied to support construction mark-up estimation. The major drawback of this application, however, is that an ANN system is unable to explain why and how a particular recommendation is made. This significantly affects the user-acceptance of the system and its results. The research presented in this paper investigates the use of the KT-1 method for automatically extracting rules from a trained neural network. The KT-1 method is implemented and tested on collected bidding data, and the results from the investigation indicate the usefulness of the KT-1 method. Discussions on the difficulties of generating automated explanations are also presented.
| Item Type: | Article |
|---|---|
| Index terms: | mark-up, bidding, investigation, estimation, artificial neural network, neural network |
| Subjects: | price determination, artificial intelligence, modelling and simulation, financial and cost management, data collection methods, bidding |
| Topics: | Cost Management, Digital Applications, Procurement, Research Practice |
| 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