Modelling labour productivity using SVM and RF: a comparative study on classifiers performance

• Main Authors: Momade, Mohammed Hamza, Shahid, Shamsuddin, Hainin, Mohd. Rosli, Nashwan, Mohamed Salem, Umar, Abdulhakim Tahir
• Format: Article
• Published: Taylor and Francis Ltd. 2020
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/id/eprint/90632/; http://dx.doi.org/10.1080/15623599.2020.1744799

The purpose of this paper is to propose a data-driven approach for preparation of Construction Labour Productivity (CLP) models from influencing labour factors. Two state-of-art machine learning-based classifiers, Support Vector Machine (SVM) and Random Forest (RF) were used for modelling CLP. First...