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Falling Weight Deflectometer Testing Based Mechanistic-Empirical Overlay Thickness Design Approach for Low-Volume Roads in Illinois

机译:伊利诺伊州小体积道路降落式折偏仪测试的机械-经验叠加厚度设计方法

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摘要

A recent Illinois Center for Transportation (ICT) research project at the University of Illinois has aimed at evaluating the use of Falling Weight Deflectometer in the assessment of structural conditions of in-service low volume roads that are in need of rehabilitation. Ten different pavement sections were selected from four counties in Illinois with varying structural and traffic characteristics to conduct Falling Weight Deflectometer (FWD) tests. A neural-network based pavement analyzer, ANN-Pro, previously developed at the University of Illinois at Urbana-Champaign based on ILLI-PAVE finite element program solutions, was used to analyze the FWD data in order to determine and monitor the structural adequacies of existing pavement sections. This paper presents a new Mechanistic-Empirical (M-E) Overlay Design method that was introduced as part of the ICT research project to adequately assess the structural conditions of existing pavements and subsequently recommend required overlay thickness values from critical pavement responses computed from FWD field deflections. The M-E Overlay Design methodology compares the critical pavement responses to threshold values for the pre-established fatigue and/or rutting damage algorithms.
机译:最近伊利诺伊大学的伊利诺伊州运输中心(ICT)研究项目旨在评估在适用于康复的役低批量道路的结构条件的评估中使用下降重量偏转仪。从伊利诺伊州的四个县中选择了十个不同的路面部分,具有不同的结构和交通特性,以进行下降的重量偏转仪(FWD)测试。基于神经网络的路面分析仪,以前在伊利诺伊州厄巴纳 - 香槟大学的基于Illi-Pave有限元计划解决方案的伊利诺伊大学开发的Ann-Pro用于分析FWD数据,以确定和监控结构的结构性安全性现有的路面部分。本文提出了一种新的机制 - 经验(M-E)覆盖设计方法,作为ICT研究项目的一部分引入,以充分评估现有路面的结构条件,随后推荐从来自FWD场偏转计算的关键路面响应所需的覆盖厚度值。 M-E覆盖设计方法与预先建立的疲劳和/或车辙损伤算法的阈值进行比较关键路面响应。

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