Government Construction Project Budget Prediction Using Machine Learning

Kusonkhum, Wuttipong; Srinavin, Korb; Leungbootnak, Narong; Aksorn, Preenithi; Chaitongrat, Tanayut

doi:10.12720/jait.13.1.29-35

Cited by 29 publications

(6 citation statements)

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“…The authors developed a machine learning model for anticipating over-budget projects. The produced model, which had an accuracy of 0.86, was built using the KNN approach, but it used a few of the project's data [54].…”

Section: Machine Learning In Construction Researchmentioning

confidence: 99%

“…Using the procurement technique has a considerable impact on a contractor's cost [14,18]. In this paper, the project scales were also separated into five levels as show that in Table 3 [54]. The departments were divided according to their budgets in 2019 in Table 4.…”

Section: Specificmentioning

confidence: 99%

“…The project duration was specified according to the contract. The two factors that were considered in this study included a comparison between the winning price with an estimated price and the winning price with a budget [54]. However, comparing the budget and the winning price was the goal of the prediction model.…”

Section: Specificmentioning

confidence: 99%

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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

Kusonkhum,

Srinavin,

Chaitongrat

2023

Sustainability

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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.

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Section: Machine Learning In Construction Researchmentioning

confidence: 99%

Section: Specificmentioning

confidence: 99%

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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

Kusonkhum,

Srinavin,

Chaitongrat

2023

Sustainability

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“…Hence, much research on the application of KNN can be found in the construction domain. KNN can be applied in areas such as cost estimation, contractor selection, programming, budgeting, quality control, image crack recognition and knowledge sharing [29][30][31][32]. The precise results produced by KNN enhancement methods help solve practical problems [33][34][35].…”

Section: Rough Set Enhanced K-nearest Neighbor (Knn)mentioning

confidence: 99%

Manpower Allocation of Work Activities for Producing Precast Components: Empirical Study in Taiwan

Chen

Wei

2023

Sustainability

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The production of precast components in the construction industry is a labor-intensive process. The objectives of this study are to prove the feasibility of using rough set theory to classify and weigh impact attributes, and to develop a model to assess the total quantities of labor needed for precast structural elements using a rough set enhanced K-Nearest Neighbor (KNN). Three main building components (beams, girders, and columns) were collected from the production of precast elements in Taiwan. After trimming and analyzing the basic data, the rough set approach is used to classify and weight the attributes into three levels of impact based on their frequency. A rough set enhanced KNN is accordingly developed, yielding an accuracy rate of 92.36%, which is 8.09% higher than the result obtained when using the KNN algorithm. A practical and effective prediction model would assist managers to estimate the manpower requirement of precast projects.

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“…Currently, well-known Deep Learning networks include Convolution Neural Networks (CNNs), such as AlexNet [12], AlphaGo Zero [23], AlphaZero [22], AlphaFold [19], MuZero [18], AlphaDev [14], those in IBM Debater [24] among others [25], [1], [13]. For open contests with AlphaGo [21], this author alleged that humans did post-selections from multiple AlphaGo networks on the fly when test data were arriving from Lee Sedol or Ke Jie [37].…”

Section: Post-selection Misconductmentioning

confidence: 99%

A Developmental Method that Computes Optimal Networks without Post-Selections

Weng

2021

2021 IEEE International Conference on Development and Learning (ICDL)

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This is a theoretical paper on "Deep Learning" misconduct in particular and Post-Selection in general. As far as the author knows, the first peer-reviewed papers on Deep Learning misconduct are [32], [37], [36]. Regardless of learning modes, e.g., supervised, reinforcement, adversarial, and evolutional, almost all machine learning methods (except for a few methods that train a sole system) are rooted in the same misconduct-cheating and hiding-(1) cheating in the absence of a test and (2) hiding bad-looking data. It was reasoned in [32], [37], [36] that authors must report at least the average error of all trained networks, good and bad, on the validation set (called general cross-validation in this paper). Better, report also five percentage positions of ranked errors. From the new analysis here, we can see that the hidden culprit is Post-Selection. This is also true for Post-Selection on hand-tuned or searched hyperparameters, because they are random, depending on random observation data. Does cross-validation on data splits rescue Post-Selections from the Misconducts (1) and ( 2)? The new result here says: No. Specifically, this paper reveals that using cross-validation for data splits is insufficient to exonerate Post-Selections in machine learning. In general, Post-Selections of statistical learners based on their errors on the validation set are statistically invalid.

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Government Construction Project Budget Prediction Using Machine Learning

Cited by 29 publications

References 0 publications

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

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

Manpower Allocation of Work Activities for Producing Precast Components: Empirical Study in Taiwan

A Developmental Method that Computes Optimal Networks without Post-Selections

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