Scholarlink

<p>This research investigates the reliability of asphalt pavement design in hot regions by integrating the combined effects of traffic and temperature variation through the Environmental Damage Index (EDI). The study employs a novel approach using Sobol-based Quasi-Monte Carlo sampling and weighted EDI to model and evaluate the influence of the Traffic Damage Factor (TDF) and Temperature Damage Factor (TeDF) with improved precision. Traffic inputs were based on axle-load survey data from Rajasthan, and temperature distributions were fitted using India Meteorological Department (IMD) records for Jaipur’s summer months. Normality and fit tests validated the input models. The 256 Sobol-based QMC samples yielded stable 95th and 99th percentile EDI estimates, with <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:\le\:1.5\%\)</EquationSource> </InlineEquation> variability. Statistical analysis, including lognormal distribution modeling, sensitivity analysis, and cumulative distribution functions, is utilized to identify critical EDI thresholds for ensuring pavement longevity. The findings reveal that a 95% reliability benchmark corresponds to an EDI threshold of approximately 23.61, with a mean EDI value of 14.80 under typical hot-climate conditions. Confidence intervals and range-based thresholds underscore the variability in pavement performance under environmental and traffic stresses. Since both TDF and TeDF are modeled as normalized damage ratios, the resulting EDI is a dimensionless metric, enabling a commensurate and interpretable combination of traffic and temperature-induced damage effects. The study emphasizes the dominant impact of temperature in regions with high thermal variability, highlighting the need for tailored design adjustments to enhance pavement resilience. The framework uses 256 Sobol-based Quasi-Monte Carlo samples, which were validated via convergence analysis to yield stable 95th and 99th percentile estimates (≤ 1.5% variability), derived from statistically validated traffic and temperature data specific to hot regions such as Jaipur, India. This research offers a robust, region-specific framework for reliability-based pavement design and paves the way for further exploration of environmental and traffic impacts on long-term pavement performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Reliability-Based Pavement Design in Hot Climates: A Probabilistic Framework Using Environmental Damage Index

  • 期刊论文

  • 发表日期：

    2025年12月23日

查看全文
  • Priyam Nath Bhowmik,
  • Kezia Saini

摘要

This research investigates the reliability of asphalt pavement design in hot regions by integrating the combined effects of traffic and temperature variation through the Environmental Damage Index (EDI). The study employs a novel approach using Sobol-based Quasi-Monte Carlo sampling and weighted EDI to model and evaluate the influence of the Traffic Damage Factor (TDF) and Temperature Damage Factor (TeDF) with improved precision. Traffic inputs were based on axle-load survey data from Rajasthan, and temperature distributions were fitted using India Meteorological Department (IMD) records for Jaipur’s summer months. Normality and fit tests validated the input models. The 256 Sobol-based QMC samples yielded stable 95th and 99th percentile EDI estimates, with \(\:\le\:1.5\%\) variability. Statistical analysis, including lognormal distribution modeling, sensitivity analysis, and cumulative distribution functions, is utilized to identify critical EDI thresholds for ensuring pavement longevity. The findings reveal that a 95% reliability benchmark corresponds to an EDI threshold of approximately 23.61, with a mean EDI value of 14.80 under typical hot-climate conditions. Confidence intervals and range-based thresholds underscore the variability in pavement performance under environmental and traffic stresses. Since both TDF and TeDF are modeled as normalized damage ratios, the resulting EDI is a dimensionless metric, enabling a commensurate and interpretable combination of traffic and temperature-induced damage effects. The study emphasizes the dominant impact of temperature in regions with high thermal variability, highlighting the need for tailored design adjustments to enhance pavement resilience. The framework uses 256 Sobol-based Quasi-Monte Carlo samples, which were validated via convergence analysis to yield stable 95th and 99th percentile estimates (≤ 1.5% variability), derived from statistically validated traffic and temperature data specific to hot regions such as Jaipur, India. This research offers a robust, region-specific framework for reliability-based pavement design and paves the way for further exploration of environmental and traffic impacts on long-term pavement performance.