Using extreme gradient boosting (XGBoost) machine learning to predict construction cost overruns-Reference-Cited by-同舟云学术

Using extreme gradient boosting (XGBoost) machine learning to predict construction cost overruns

Published:2023-12-09 Issue: Volume: Page:1-9
ISSN:1562-3599
Container-title:International Journal of Construction Management
language:en
Short-container-title:International Journal of Construction Management

Author:

Coffie G. H.¹,Cudjoe S. K. F.²

Affiliation:

1. Department of Building Technology, Ho Technical University, Ho, Volta Region, Ghana

2. Wits Business School, University of the Witwatersrand, Johannesburg, South Africa

Publisher

Informa UK Limited

Subject

Management of Technology and Innovation,Strategy and Management,Building and Construction,Architecture

Link

https://www.tandfonline.com/doi/pdf/10.1080/15623599.2023.2289754

Reference71 articles.

1. Exploring the Causes of Delays in Construction Industry Using a Cause-and-Effect Diagram: Case Study for Iran

2. Acharya NK Lee YD Kim SY Lee JC Kim CS. 2006. Analysis of construction delay factor: a Korean perspective. Proceedings of the 7th Asia Pacific Industrial Engineering and Management Systems Conference 2006 p. 883–895.

3. Risk perception and Bayesian analysis of international construction contract risks: The case of payment delays in a developing economy

4. Ahiaga-Dagbui DD Smith SD. 2012. Neural networks for modelling the final target cost of water projects.

5. Systematic literature review of project manager's leadership competencies

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