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<p>India’s rapidly expanding infrastructure has made highway transportation a crucial component of economic development, accounting for nearly 80% of total transportation. Effective pavement management is essential for maintaining road quality, optimizing maintenance strategies, and ensuring long-term sustainability. This study focuses on the evaluation and prediction of rigid pavement distress using a quadratic regression model. By conducting a two-year survey on highway and city roads various types of pavement distress, including longitudinal cracks, transverse cracks, ravelling, potholes, and deformations, were analysed using Minitab software. The research introduces predictive models to estimate future pavement conditions, aiming to improve distress detection and reduce false positives compared to traditional human assessment methods. The findings offer insights into regression-based prediction techniques, highlighting their potential for enhancing road maintenance planning and decision-making.</p>

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Development of Future Prediction Models of Rigid Pavement for Maintenance Management in India Using Regression Analysis

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Tanu Chaturvedi,
  • S. S. Goliya,
  • P. K. Agrawal,
  • Rakesh Mehar

摘要

India’s rapidly expanding infrastructure has made highway transportation a crucial component of economic development, accounting for nearly 80% of total transportation. Effective pavement management is essential for maintaining road quality, optimizing maintenance strategies, and ensuring long-term sustainability. This study focuses on the evaluation and prediction of rigid pavement distress using a quadratic regression model. By conducting a two-year survey on highway and city roads various types of pavement distress, including longitudinal cracks, transverse cracks, ravelling, potholes, and deformations, were analysed using Minitab software. The research introduces predictive models to estimate future pavement conditions, aiming to improve distress detection and reduce false positives compared to traditional human assessment methods. The findings offer insights into regression-based prediction techniques, highlighting their potential for enhancing road maintenance planning and decision-making.