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Machine learning for construction crew productivity prediction using daily work reports

Sadatnya, A ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1016/j.autcon.2023.104891
  3. Publisher: Elsevier B.V , 2023
  4. Abstract:
  5. Construction productivity estimation lacks a comprehensive, standard, and task-type-independent framework to generate and serialize Machine Learning (ML) models. This research aims to develop an ML management framework for estimating the work crew productivity (crew outputs over their working hours) by addressing various operation and project types. The framework takes advantage of historical data, including information regarding operations' progress, weather conditions, the number of resources, and their composition in a work crew. Daily work reports are used as a principal source of historical data. Various hyperparameters-tuned ML algorithms are adopted and ranked based on their computational complexity and prediction accuracy. The generated productivity prediction models have the flexibility to be reused for the effective planning of various construction projects. Applying the proposed framework to a case study of nine disciplines provided estimation models with high accuracies. This study also discusses the theoretical and practical implications of the presented model development procedure. © 2023 Elsevier B.V
  6. Keywords:
  7. Construction productivity ; Daily work reports ; Data mining ; Data-driven prediction models ; Ensemble methods ; Feature engineering ; Machine learning ; Neural network ; Productivity estimation
  8. Source: Automation in Construction ; Volume 152 , 2023 ; 09265805 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0926580523001516