Classifying the level of bid price volatility based on machine learning with parameters from bid documents as risk factors

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3 Scopus citations


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.

Original languageEnglish
Article number3886
JournalSustainability (Switzerland)
Issue number7
StatePublished - 1 Apr 2021

Bibliographical note

Funding Information:
Funding: This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21ORPS-B158109-02). This study is also supported by the Ewha Womans University Scholarship of 2019.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Bid price volatility
  • Classification model
  • Machine learning (ML)
  • Prebid clarification document
  • Public project
  • Risk analysis
  • Risk management
  • Sustainable project management
  • Uncertainty in bid documents


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