A data-driven framework for conceptual cost estimation of infrastructure projects using XGBoost and Bayesian optimization Article Swipe
Jiashu Zhang
,
Jingfeng Yuan
,
Amin Mahmoudi
,
Wenying Ji
,
Qiushi Fang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/13467581.2023.2294871
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/13467581.2023.2294871
Cost estimation is a key component of project plans, yet it is challenging to provide reliable and efficient estimations using conventional methods in the conceptual phase of infrastructure projects. This study proposes a framework that integrates feature selection, extreme gradient boosting (XGBoost), Bayesian optimization (BO), and SHapley Additive exPlanations (SHAP) to provide conceptual cost estimations and explain the results for early decision-making. Correlation analysis and forward search are combined to select the key features. XGBoost is developed as the estimator and enhanced by BO in accuracy and efficiency. Model explanations were presented using SHAP. The framework is demonstrated through a case study of electric substations containing 605 samples. The results show that the proposed framework can provide satisfactory performance on conceptual cost estimations, where BO-XGBoost outperforms the benchmark models (with ${R^2}$ ~0.9567, adjusted ${R^2}$ ~0.9549, RMSE ~ 0.8690, and MAE ~ 0.4875). SHAP reveals how the features contribute to the cost based on both global and local explanations. The framework provides a guideline for more accurate, efficient, and explainable cost estimations in the conceptual phase of infrastructure projects. It can support the government and project planners in early decision-making, including reliable project budget and plan alternatives selection.
Related Topics
Concepts
Cost estimate
Computer science
Benchmark (surveying)
Conceptual framework
Estimator
Selection (genetic algorithm)
Key (lock)
Feature selection
Bayesian probability
Data mining
Operations research
Machine learning
Artificial intelligence
Engineering
Systems engineering
Mathematics
Statistics
Computer security
Geography
Geodesy
Philosophy
Epistemology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/13467581.2023.2294871
- https://www.tandfonline.com/doi/pdf/10.1080/13467581.2023.2294871?needAccess=true
- OA Status
- gold
- Cited By
- 15
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390612190
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390612190Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/13467581.2023.2294871Digital Object Identifier
- Title
-
A data-driven framework for conceptual cost estimation of infrastructure projects using XGBoost and Bayesian optimizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-04Full publication date if available
- Authors
-
Jiashu Zhang, Jingfeng Yuan, Amin Mahmoudi, Wenying Ji, Qiushi FangList of authors in order
- Landing page
-
https://doi.org/10.1080/13467581.2023.2294871Publisher landing page
- PDF URL
-
https://www.tandfonline.com/doi/pdf/10.1080/13467581.2023.2294871?needAccess=trueDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.tandfonline.com/doi/pdf/10.1080/13467581.2023.2294871?needAccess=trueDirect OA link when available
- Concepts
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Cost estimate, Computer science, Benchmark (surveying), Conceptual framework, Estimator, Selection (genetic algorithm), Key (lock), Feature selection, Bayesian probability, Data mining, Operations research, Machine learning, Artificial intelligence, Engineering, Systems engineering, Mathematics, Statistics, Computer security, Geography, Geodesy, Philosophy, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 14, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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