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Area of Science:

  • Construction Project Management
  • Artificial Intelligence in Engineering
  • Data Science Applications

Background:

  • Project analytics traditionally rely on retrospective reporting for decision-making.
  • Machine learning (ML) applications are prevalent in various fields but underexplored in construction project delivery.
  • A gap exists in evaluating ML methods for real-time construction project analytics and cost overrun prediction.

Purpose of the Study:

  • To evaluate specific machine learning algorithms for construction project analytics.
  • To propose and illustrate a machine learning-based, data-driven research framework for project analytics.
  • To identify the most accurate ML model for predicting construction project cost overrun frequency.

Main Methods:

  • Development of a data-driven research framework using ML algorithms.
  • Application of the framework to construction project data, including cost overrun frequencies.
  • Evaluation of Support Vector Machine, Logistic Regression, K-Nearest Neighbour, Random Forest, Stacking, and Artificial Neural Network models using feature selection and confusion matrix methods.

Main Results:

  • Several ML models were tested and evaluated on construction project data with 44 independent variables.
  • Feature selection techniques (Univariate, RFE, SelectFromModel) and confusion matrices were used to determine model accuracy.
  • The study identified the most accurate ML model for predicting project cost overrun frequency within the tested algorithms.

Conclusions:

  • The proposed ML-based framework offers a novel approach to construction project analytics.
  • The framework demonstrates the potential of AI in improving decision-making and addressing project-related issues.
  • The framework's generalisability suggests its applicability to broader project management contexts.