Classifying the level of bid price volatility based on machine learning with parameters from bid documents as risk factors
- 주제(키워드) Bid price volatility , Classification model , Machine learning (ML) , Prebid clarification document , Public project , Risk analysis , Risk management , Sustainable project management , Uncertainty in bid documents
- 등재 SCIE, SSCI, SCOPUS
- 발행기관 MDPI AG
- 발행년도 2021
- URI http://www.dcollection.net/handler/ewha/000000181789
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/su13073886
초록/요약
The purpose of this study is to classify the bid price volatility level with machine learning and parameters from bid documents as risk factors. To this end, we studied project-oriented risk factors affecting the bid price and pre-bid clarification document as the uncertainty of bid documents through preliminary research. The authors collected Caltrans’s bid summary and pre-bid clarification document from 2011-2018 as data samples. To train the classification model, the data were preprocessed to create a final dataset of 269 projects consisting of input and output parameters. The projects in which the bid inquiries were not resolved in the pre-bid clarification had higher bid averages and bid ranges than the risk-resolved projects. Besides this, regarding the two classification models with neural network (NN) algorithms, Model 2, which included the uncertainty in the bid documents as a parameter, predicted the bid average risk and bid range risk more accurately (52.5% and 72.5%, respectively) than Model 1 (26.4% and 23.3%, respectively). The accuracy of Model 2 was verified with 40 verification test datasets. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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