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The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project

Author

Listed:
  • Wuttipong Kusonkhum

    (Department of Civil Engineering, Northeastern University, Khon Kaen 40000, Thailand)

  • Korb Srinavin

    (Department of Civil Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Tanayut Chaitongrat

    (Construction and Project Management Center, Faculty of Architecture, Urban Design and Creative Arts, Mahasarakham University, Maha Sarakham 44150, Thailand)

Abstract

Big data technologies are disruptive technologies that affect every business, including those in the construction industry. The Thai government has also been affected and attempted to use machine learning techniques with the analytics of big data technologies to predict which construction projects have a winning price over the project budget. However, this technology was never developed, and the government did not implement it because they had data obtained via a traditional data collection process. In this study, traditional data were processed to predict the behavior in Thai government construction projects using a machine learning model. The data were collected from the government procurement system in 2019. There were seven input data, including the project owner department, type of construction project, bidding method, project duration, project scale, winning price overestimated price, and winning price over budget. A range of classification techniques, including an artificial neural network (ANN), a decision tree (DC), and a K-nearest neighbor (KNN), were used in this study. According to the results, after hyperparameter tuning, the ANN had the greatest prediction accuracy of 78.9 percent. This study confirms that the data from the Thai government procurement system can be investigated using machine learning techniques from big data technologies.

Suggested Citation

  • Wuttipong Kusonkhum & Korb Srinavin & Tanayut Chaitongrat, 2023. "The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project," Sustainability, MDPI, vol. 15(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12836-:d:1224527
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    References listed on IDEAS

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    1. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    2. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, December.
    3. Rajat Roy & Margaret Low & John Waller, 2005. "Documentation, standardization and improvement of the construction process in house building," Construction Management and Economics, Taylor & Francis Journals, vol. 23(1), pages 57-67.
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    Cited by:

    1. Chaiyan Junsiri & Pruethsan Sutthichaimethee & Nathaporn Phong-a-ran, 2024. "Modeling CO 2 Emission Forecasting in Energy Consumption of the Industrial Building Sector under Sustainability Policy in Thailand: Enhancing the LISREL-LGM Model," Forecasting, MDPI, vol. 6(3), pages 1-17, June.

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