Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-32511-3_166
Reference20 articles.
1. Cheng MY, Cao MT Mendrofa AYJ (2021) Dynamic feature selection for accurately predicting construction productivity using symbiotic organisms search-optimized least square support vector machine. J Build Eng 35:101973. https://doi.org/10.1016/j.jobe.2020.101973
2. Dave B, Buda A, Nurminen A, Framling K (2018) A framework for integrating BIM and IoT through open standards. Autom Constr 95:35–45. https://doi.org/10.1016/j.autcon.2018.07.022
3. Ebrahimi S, Fayek AR, Sumati V (2021) Hybrid artificial intelligence HFS-RF-PSO model for construction labor productivity prediction and optimization. Algorithms 14(7):214. https://doi.org/10.3390/a14070214
4. Ebrahimi S, Raoufi M, Fayek AR (2020) Framework for integrating an artificial neural network and a genetic algorithm to develop a predictive model for construction labor productivity. Constr Res Congr: Comput Appl:58–66
5. El-Gohary KM, Aziz RF, Abdel-Khalek HA (2017) Engineering approach using ANN to improve and predict construction labor productivity under different influences. J Constr Eng Manag 143(8):04017045. https://doi.org/10.1061/(asce)co.1943-7862.0001340
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