ArticleScientific reports2026
Predicting sustainability performance in construction projects using machine learning: a comparative study.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
The construction sector plays a major role in global environmental degradation, contributing significantly to carbon emissions, energy consumption, and waste generation. Despite this urgency, limited studies have explored predictive modelling of sustainability performance using survey-based project data, particularly within Saudi Arabia. This study addresses this gap by applying supervised machine learning techniques to predict carbon emissions and classify projects into emission-level categories. A structured survey generated 150 validated responses from key stakeholders across major Saudi cities, covering 19 project and sustainability attributes. Three machine learning models, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB) were trained and evaluated using nested 10 × 5-fold cross-validation. RF achieved the strongest regression performance (mean CV R
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