Development of Fatigue Cracking Performance Prediction Models for Flexible Pavements Using LTPP Database
Development of Fatigue Cracking Performance Prediction Models for Flexible Pavements Using LTPP Database
复制标题
使用 LTPP 数据库开发柔性路面疲劳开裂性能预测模型
DOI：
--
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Pei
中科院分区：
文献类型：
--
作者：
H. Ker;Ying;Pei
本研究的主要目的是开发改进的柔性路面的疲劳开裂模型，使用长期路面性能（LTPP）数据库。数据库的检索、准备和清理都是以一种更系统和自动化的方法仔细处理的。在改进的2002年AASHTO指南中实施的现有预测模型的预测精度被认为是不够的。探索性数据分析表明，使用常规回归技术的随机误差和恒定方差的正态性假设可能不适用于本研究。为此，采用广义线性模型（GLM）和广义加性模型（GAM）等现代回归技术，沿着泊松分布假设和拟似然估计方法进行建模。由此产生的机械经验模型包括几个变量，如每年KESALs，路面年龄，年降水量，年温度，AC表层下的临界拉伸应变，和冻融循环疲劳开裂的预测。通过相关解释参数的显著性检验和各种敏感性分析，进一步检查了模型拟合的优度。初步提出的预测模型似乎合理地同意路面性能数据，但其进一步的增强是可能的，并建议。
The main objective of this study is to develop improved fatigue cracking models for flexible pavements using the Long-Term Pavement Performance (LTPP) database. The retrieval, preparation, and cleaning of the database were carefully handled in a more systematic and automatic approach. The prediction accuracy of the existing prediction models implemented in the improved 2002 AASHTO guide was found to be inadequate. Exploratory data analysis indicated that the normality assumption with random errors and constant variance using conventional regression techniques might not be appropriate for this study. Therefore, several modern regression techniques including generalized linear model (GLM) and generalized additive model (GAM) along with the assumption of Poisson distribution and quasi-likelihood estimation method were adopted for the modeling process. The resulting mechanistic-empirical model included several variables such as yearly KESALs, pavement age, annual precipitation, annual temperature, critical tensile strain under the AC surface layer, and freeze-thaw cycle for the prediction of fatigue cracking. The goodness of the model fit was further examined through the significant testing and various sensitivity analyses of pertinent explanatory parameters. The tentatively proposed predictive models appeared to reasonably agree with the pavement performance data although their further enhancements are possible and recommended.