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The construction industry, characterized by its inherent complexity, is a cornerstone of economic growth and development. In the era of Construction 4.0, accurate cost estimation within this sector is essential for successful project planning and execution. This research aims to predict construction project costs using real data from 203 projects, incorporating contract parameters provided by an engineering consulting firm. Among the eleven machine learning algorithms tested, the Partial Least Squares regression model emerged as the most effective. It achieved the highest ranking in the TOPSIS analysis, demonstrating a coefficient of determination of 91% for the test set.

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A Machine Learning Approach Based on Contract Parameters for Cost Forecasting in Construction

  • 会议论文

  • 发表日期：

    2025年

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  • Maroua Ben Talha,
  • Fatima-Zahrae Houari,
  • Asmaa Benghabrit,
  • Mohamed Sayf Eddine Rhazi

摘要

The construction industry, characterized by its inherent complexity, is a cornerstone of economic growth and development. In the era of Construction 4.0, accurate cost estimation within this sector is essential for successful project planning and execution. This research aims to predict construction project costs using real data from 203 projects, incorporating contract parameters provided by an engineering consulting firm. Among the eleven machine learning algorithms tested, the Partial Least Squares regression model emerged as the most effective. It achieved the highest ranking in the TOPSIS analysis, demonstrating a coefficient of determination of 91% for the test set.