Forecasting Construction Labor Productivity Metrics
Author:
Affiliation:
1. Dept. of Civil and Architectural Engineering, Aarhus Univ.. ORCID: .
2. Dept. of Civil and Mechanical Engineering, Technical Univ., Denmark. ORCID: .
3. Dept. of Civil and Architectural Engineering, Aarhus Univ. ORCID: .
Publisher
American Society of Civil Engineers
Link
https://ascelibrary.org/doi/pdf/10.1061/9780784485248.122
Reference23 articles.
1. A scientometric analysis and review of construction labour productivity research;Adebowale O. J.;International Journal of Productivity and Performance Management,2022
2. Box, G. E. P., and Jenkins, G. M. (1976). Time series analysis: Forecasting and control, Revised Edition, Holden Day, San Francisco, ISBN: 0816211043, 9780816211043.
3. Chen T. and Guestrin C. (2016). XGBoost: A Scalable Tree Boosting System Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pp. 785–794 https://doi.org/10.1145/2939672.2939785.
4. Dynamic feature selection for accurately predicting construction productivity using symbiotic organisms search-optimized least square support vector machine
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