IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i7p3886-d527872.html
   My bibliography  Save this article

Classifying the Level of Bid Price Volatility Based on Machine Learning with Parameters from Bid Documents as Risk Factors

Author

Listed:
  • YeEun Jang

    (Department of Architectural & Urban Systems Engineering, Ewha Womans University, Seoul 03760, Korea)

  • JeongWook Son

    (Department of Architectural & Urban Systems Engineering, Ewha Womans University, Seoul 03760, Korea)

  • June-Seong Yi

    (Department of Architectural & Urban Systems Engineering, Ewha Womans University, Seoul 03760, Korea)

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.

Suggested Citation

  • YeEun Jang & JeongWook Son & June-Seong Yi, 2021. "Classifying the Level of Bid Price Volatility Based on Machine Learning with Parameters from Bid Documents as Risk Factors," Sustainability, MDPI, vol. 13(7), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3886-:d:527872
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/7/3886/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/7/3886/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. Wanous & A. H. Boussabaine & J. Lewis, 2000. "To bid or not to bid: a parametric solution," Construction Management and Economics, Taylor & Francis Journals, vol. 18(4), pages 457-466.
    2. Roger Miller & Donald R. Lessard, 2008. "Evolving Strategy: Risk Management and the Shaping of Mega-Projects," Chapters, in: Hugo Priemus & Bent Flyvbjerg & Bert van Wee (ed.), Decision-Making on Mega-Projects, chapter 8, Edward Elgar Publishing.
    3. Borna Dasović & Mario Galić & Uroš Klanšek, 2020. "A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    4. Dominic D. Ahiaga-Dagbui & Simon D. Smith, 2014. "Dealing with construction cost overruns using data mining," Construction Management and Economics, Taylor & Francis Journals, vol. 32(7-8), pages 682-694, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sunkuk Kim, 2021. "Technology and Management for Sustainable Buildings and Infrastructures," Sustainability, MDPI, vol. 13(16), pages 1-3, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chia-Nan Wang & Nhat-Luong Nhieu & Trang Thi Thu Tran, 2021. "Stochastic Chebyshev Goal Programming Mixed Integer Linear Model for Sustainable Global Production Planning," Mathematics, MDPI, vol. 9(5), pages 1-22, February.
    2. Qiao, Yu & Labi, Samuel & Fricker, Jon D., 2021. "Does highway project bundling policy affect bidding competition? Insights from a mixed ordinal logistic model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 228-242.
    3. Martijn Leijten, 2013. "Real-world decision-making on mega-projects: politics, bias and strategic behaviour," Chapters, in: Hugo Priemus & Bert van Wee (ed.), International Handbook on Mega-Projects, chapter 4, pages 57-82, Edward Elgar Publishing.
    4. Borna Dasović & Uroš Klanšek, 2022. "A Review of Energy-Efficient and Sustainable Construction Scheduling Supported with Optimization Tools," Energies, MDPI, vol. 15(7), pages 1-17, March.
    5. Watson, Jim & Gross, Rob & Ketsopoulou, Ioanna & Winskel, Mark, 2015. "The impact of uncertainties on the UK's medium-term climate change targets," Energy Policy, Elsevier, vol. 87(C), pages 685-695.
    6. Kyunghwan Kim, 2020. "Generalized Resource-Constrained Critical Path Method to Improve Sustainability in Construction Project Scheduling," Sustainability, MDPI, vol. 12(21), pages 1-19, October.
    7. Michał Tomczak & Piotr Jaśkowski, 2021. "Preferences of Construction Managers Regarding the Quality and Optimization Criteria of Project Schedules," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    8. Ren Tan & Kairong Hong, 2021. "Research on Extreme Dispute Decisions of Large-Scale Engineering Projects from the Perspective of Multidimensional Preferences," Mathematics, MDPI, vol. 9(22), pages 1-24, November.
    9. Love, Peter E.D. & Ahiaga-Dagbui, Dominic D. & Irani, Zahir, 2016. "Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 184-194.
    10. Martin Ekstrom & Hans Bjornsson & Clifford Nass, 2003. "Accounting for rater credibility when evaluating AEC subcontractors," Construction Management and Economics, Taylor & Francis Journals, vol. 21(2), pages 197-208.
    11. Graeme Hodge & Carsten Greve, 2013. "Public–private partnership in developing and governing mega-projects," Chapters, in: Hugo Priemus & Bert van Wee (ed.), International Handbook on Mega-Projects, chapter 9, pages 182-208, Edward Elgar Publishing.
    12. Love, Peter E.D. & Ahiaga-Dagbui, Dominic D., 2018. "Debunking fake news in a post-truth era: The plausible untruths of cost underestimation in transport infrastructure projects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 357-368.
    13. Wei He & Wenjing Li & Wei Wang, 2021. "Developing a Resource Allocation Approach for Resource-Constrained Construction Operation under Multi-Objective Operation," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    14. Fernando Acebes & David Poza & Jose M Gonzalez-Varona & Javier Pajares & Adolfo Lopez-Paredes, 2024. "On the project risk baseline: integrating aleatory uncertainty into project scheduling," Papers 2406.00077, arXiv.org.
    15. Jeng-Wen Lin & Pu Fun Shen & Bing-Jean Lee, 2015. "Repetitive Model Refinement for Questionnaire Design Improvement in the Evaluation of Working Characteristics in Construction Enterprises," Sustainability, MDPI, vol. 7(11), pages 1-15, November.
    16. Orlando Lima & Gabriela Fernandes & Anabela Tereso, 2023. "Benefits of Adopting Innovation and Sustainability Practices in Project Management within the SME Context," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    17. Borna Dasović & Uroš Klanšek, 2021. "Integration of Mixed-Integer Nonlinear Program and Project Management Tool to Support Sustainable Cost-Optimal Construction Scheduling," Sustainability, MDPI, vol. 13(21), pages 1-20, November.
    18. Mateusz Trzeciak & Izabela Jonek-Kowalska, 2021. "Monitoring and Control in Program Management as Effectiveness Drivers in Polish Energy Sector. Diagnosis and Directions of Improvement," Energies, MDPI, vol. 14(15), pages 1-25, July.
    19. Fuchigami, Helio Yochihiro & Tuni, Andrea & Barbosa, Luísa Queiroz & Severino, Maico Roris & Rentizelas, Athanasios, 2021. "Supporting Brazilian smallholder farmers decision making in supplying institutional markets," European Journal of Operational Research, Elsevier, vol. 295(1), pages 321-335.
    20. Joe Wong & Eddie Hui, 2006. "Construction project risks: further considerations for constructors' pricing in Hong Kong," Construction Management and Economics, Taylor & Francis Journals, vol. 24(4), pages 425-438.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3886-:d:527872. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.