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August 13, 2026Scientific ReportsOpen Access

An Integrated explainable machine learning framework for pavement condition assessment and maintenance prioritization: A Saudi Arabian case study

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Implication

Randomized trial assesses pavement condition and prioritizes maintenance in Saudi Arabia, indicating effective management strategies.

Key Points

  • The aim is to develop and validate a machine-learning framework for predicting pavement condition based on various influencing factors.
  • Used 4 years of traffic, pavement distress, and weather data from a 190 km-long highway.
  • Trained models using Synthetic Minority Over-sampling Technique to address data imbalance.
  • Evaluated seven machine learning models and performed feature importance and SHAP analyses.
  • XGBoost and CatBoost achieved the highest classification accuracy of 0.87.
  • Traffic loading was identified as the most significant predictor of International Roughness Index (IRI).
  • Pavement deterioration curve indicated IRI reaching a critical threshold of 4.0 in 6.6–6.7 years without major rehabilitation.

Cite This Study

A 2026 study studied this question.

synapsesocial.com/papers/6a7d75c72b0e0cff3f63e941https://doi.org/10.1038/s41598-026-63353-x
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions2024 · 28 citations
  2. 2Predicting pavement roughness evolution in data-limited contexts using spatially validated probabilistic hindcasting2026
  3. 3An Interpretable Pretrained Tabular Modeling Framework for Predicting IRI Across Multiple Pavement Structural Configurations2026
  4. 4Assessing Functional Performance of Asphalt Pavements under Data Sparsity: A Probabilistic-Deterministic Approach2026
  5. 5Machine Learning–Based Dual Prediction of Pavement Roughness and Condition Rating with Taylor and Information-Theoretic Validation2026