Li, H and Love, P E D (1997) Combining rule-based expert systems and artificial neural networks for mark-up estimation. Construction Management and Economics, 17(2), pp. 169-176. ISSN 01446193
Abstract
Rule-based expert systems and artificial neural networks are two major systems for developing intelligent decision support systems. The integration of the two systems can generate a new system which shares the strengths of both rule-based and artificial neural network systems. This research presents a computer based mark-up decision support system called InMES (integrated mark-up estimation system) that integrates a rule-based expert system and an artificial neural network (ANN) based expert system. The computer system represents an innovative approach for estimating a contractor' s mark-up percentage for a construction project. A rule extraction method is developed to generate rules from a trained ANN. By using the explanation facility embedded in the rule-based expert system, InMES provides users with a clear explanation to justify the rationality of the estimated mark-up output. Cost data derived from a contractor' s successful bids were used to train an ANN and, in conjunction with a rule-based expert system, select the expected mark-up for a project. The combination of both ANN-and rule-based expert systems for estimating mark-up allows significant benefits to be made from each individual system, such as understanding why and how the estimated mark-up was derived and also the effects of imposing rules and constraints on a company' s mark-up estimation. The mark-up decision support system presented can assist contractors in preparing a rational mark-up percentage for a project. Moreover, InMES as proposed will assist contractors in their tender decision making, that is, whether or not to submit a bid for a project considering the estimated mark-up.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | explanation facility; hybrid systems; mark-up decision; rule extraction |
| Index terms: | hybrid system, artificial neural network, decision support, rationality, mark-up, computer system, estimating, expert system, construction project, decision-making, integration, estimation, cost data |
| Subjects: | computing systems, modelling and simulation, financial and cost management, decision-making and reasoning, accounting and finance, production management, decision analysis, organizational analysis, data management, systems and processes, price determination |
| Topics: | Digital Applications, Organizational Design, Engineering Principles, Research Practice, Project Management, Cost Management, Procurement, Risk Management |
| Descriptive scope: | 3 PCT |
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