Version 1
: Received: 6 June 2023 / Approved: 7 June 2023 / Online: 7 June 2023 (10:50:29 CEST)
Version 2
: Received: 17 July 2023 / Approved: 2 August 2023 / Online: 3 August 2023 (10:55:35 CEST)
Kusonkhum, W.; Srinavin, K.; Chaitongrat, T. The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project. Sustainability2023, 15, 12836.
Kusonkhum, W.; Srinavin, K.; Chaitongrat, T. The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project. Sustainability 2023, 15, 12836.
Kusonkhum, W.; Srinavin, K.; Chaitongrat, T. The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project. Sustainability2023, 15, 12836.
Kusonkhum, W.; Srinavin, K.; Chaitongrat, T. The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project. Sustainability 2023, 15, 12836.
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 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 project owner department, type of construction project, bidding method, project duration, project level, winning price over estimated price, and winning price over budget. A range of classification techniques, including an artificial neural network (ANN), a decision tree (DC), and K-nearest neighbor (KNN), were used in this study (ANN). According to the results, after hyperparameter tuning, ANN had the greatest prediction accuracy with 78.9 percent. This study confirms that data from the Thai government procurement system can be investigated using machine learning techniques from big data technologies.
Keywords
Artificial neural network; Procurement management; Construction budgeting; Machine learning
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
