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<p>This study presents the application of a multiple linear regression (MLR) model to forecast the deterioration of a national highway in Nepal by analyzing a road condition dataset spanning from 2012 to 2022. The deterioration model estimates the remaining lifespan of the pavement, providing valuable insights for maintenance and repair planning, budget allocation, life-cycle cost analysis, performance prediction, and pavement management. Assessing the quality and usability of pavements through such models offers significant advantages to governing bodies in budget allocation and strategic planning. Moreover, deterioration models play a crucial role in prioritizing improvements within road networks, ensuring efficient resource utilization. The constructed MLR model for the national highways in Nepal incorporates five independent variables: International Roughness Index (IRI), Pavement Age, Annual Total Rainfall, Annual Maximum and Minimum Temperature Difference, and Equivalent Single Axle Load. The data used for model development were sourced from the Department of Roads Nepal, the Department of Hydrology and Meteorology Nepal, and Roads Board Nepal, covering traffic, climate, and pavement roughness aspects. The MLR model demonstrated reasonable accuracy in predicting the Surface Distress Index (SDI) using a multi-linear equation based on the selected independent variables. The MLR model showed an R<sup>2</sup> of 0.73, a Root Mean Squared Error (RMSE) of 0.44, a Mean Absolute Error (MAE) of 0.37, and a Mean Absolute Percentage Error (MAPE) of 20.12%. Sensitivity analysis revealed that pavement age and IRI were the most influential variables. The model can forecast SDI values in the assessment of highways, supporting efficient and cost-effective maintenance strategies.</p>

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A Multiple Regression Pavement Deterioration Model for National Highways of Nepal

  • 期刊论文

  • 发表日期：

    2025年3月28日

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  • Krishna Singh Basnet,
  • Jagat Kumar Shrestha,
  • Rabindra Nath Shrestha

摘要

This study presents the application of a multiple linear regression (MLR) model to forecast the deterioration of a national highway in Nepal by analyzing a road condition dataset spanning from 2012 to 2022. The deterioration model estimates the remaining lifespan of the pavement, providing valuable insights for maintenance and repair planning, budget allocation, life-cycle cost analysis, performance prediction, and pavement management. Assessing the quality and usability of pavements through such models offers significant advantages to governing bodies in budget allocation and strategic planning. Moreover, deterioration models play a crucial role in prioritizing improvements within road networks, ensuring efficient resource utilization. The constructed MLR model for the national highways in Nepal incorporates five independent variables: International Roughness Index (IRI), Pavement Age, Annual Total Rainfall, Annual Maximum and Minimum Temperature Difference, and Equivalent Single Axle Load. The data used for model development were sourced from the Department of Roads Nepal, the Department of Hydrology and Meteorology Nepal, and Roads Board Nepal, covering traffic, climate, and pavement roughness aspects. The MLR model demonstrated reasonable accuracy in predicting the Surface Distress Index (SDI) using a multi-linear equation based on the selected independent variables. The MLR model showed an R2 of 0.73, a Root Mean Squared Error (RMSE) of 0.44, a Mean Absolute Error (MAE) of 0.37, and a Mean Absolute Percentage Error (MAPE) of 20.12%. Sensitivity analysis revealed that pavement age and IRI were the most influential variables. The model can forecast SDI values in the assessment of highways, supporting efficient and cost-effective maintenance strategies.