A data-driven framework for conceptual cost estimation of infrastructure projects using XGBoost and Bayesian optimization
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 (XGBoos...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-03-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2023.2294871 |
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