Development and application of tender evaluation decision-making and risk early warning system for water projects based on KDD
Assessing bidders tender is very important for an employer in deciding whether to award or not. Marking scheme method is generally used to evaluate the tenders of construction project in China. And the mainly advantage of this method is the application of the experts personal knowledge and experience which makes the contractor selection more reasonable. However, if the low scores given by the experts are usually omitted when the marking scheme method is applied, the contractor with certain potential risks may win the bid, which may make the project not be executed and completed in accordance with the contract. To overcome these weaknesses, the outlier detection model, one of Knowledge Discovery in Database (KDD) method, based on similar coefficient sum was set up in this paper. In this model, the outliers were detected based on the experts tender evaluation data of the water projects. Then, the risk factors of contractors attributes were obtained and the risk early warning was carried out. The proposed model is used in the tender evaluation decision-making and risk early warning of the water projects in Tianjin, China. The empirical survey was also made to verify the proposed model. From the feedback of the questionnaires, the survey showed that the outliers detection model could detect the latent risks effectively. Accordingly, some advice was given to the employers to prevent the potential risks. Finally, the proposed method and model were introduced into a tender evaluation decision-making and risk early warning system (TEDREWS) for convenient and efficient tender evaluation and risk management.
tender evaluation risk early warning water projects outlier detection knowledge discovery in database (kdd) decision making