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Predicting sustainability performance in construction projects using machine learning: a comparative study.

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This study uses machine learning to predict construction carbon emissions in Saudi Arabia. Random Forest model shows strong performance, identifying waste and energy use as key emission drivers.

Keywords:
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Area of Science:

  • Environmental Science
  • Construction Management
  • Data Science

Background:

  • The construction sector significantly contributes to global environmental issues like carbon emissions and waste.
  • Predictive modeling for construction sustainability using project data is under-researched, especially in Saudi Arabia.

Purpose of the Study:

  • To address the research gap by applying supervised machine learning to predict carbon emissions in construction projects.
  • To classify projects into emission-level categories using survey data from Saudi Arabia.

Main Methods:

  • A survey collected data on 19 project and sustainability attributes from 150 stakeholders in major Saudi cities.
  • Supervised machine learning models including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB) were employed.
  • Nested 10x5-fold cross-validation was used for model training and evaluation, with SHAP analysis for feature importance.

Main Results:

  • The Random Forest model demonstrated superior performance in both predicting carbon emissions (test R²=0.734) and classifying projects (78% test accuracy).
  • Waste generation, energy consumption, and project duration were identified as the most significant predictors of carbon emissions.
  • The Random Forest model outperformed SVM and XGB in both regression and classification tasks.

Conclusions:

  • The study provides a data-driven framework for early sustainability assessment in construction projects.
  • Findings support informed policy-making and planning for sustainable development, aligning with Saudi Vision 2030.
  • Machine learning offers a valuable tool for predicting and managing environmental impacts in the construction sector.