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Classifying the level of bid price volatility based on machine learning with parameters from bid documents as risk factors
- Title
- Classifying the level of bid price volatility based on machine learning with parameters from bid documents as risk factors
- Authors
- Jang Y.; Son J.; Yi J.-S.
- Ewha Authors
- 이준성; 손정욱
- SCOPUS Author ID
- 이준성; 손정욱
- Issue Date
- 2021
- Journal Title
- Sustainability (Switzerland)
- ISSN
- 2071-1050
- Citation
- Sustainability (Switzerland) vol. 13, no. 7
- Keywords
- Bid price volatility; Classification model; Machine learning (ML); Prebid clarification document; Public project; Risk analysis; Risk management; Sustainable project management; Uncertainty in bid documents
- Publisher
- MDPI AG
- Indexed
- SCIE; SSCI; SCOPUS
- Document Type
- Article
- Abstract
- 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.
- DOI
- 10.3390/su13073886
- Appears in Collections:
- 공과대학 > 건축도시시스템공학과 > Journal papers
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