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Stochastic Modeling of Future Highway Maintenance Costs for Flexible Type Highway Pavement Construction Projects

机译:柔性型公路路面建设项目未来公路养护费用的随机模型

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摘要

The transportation infrastructure systems in the United States were built between the 50's and 80's, with 20 years design life. As most of them already exceeded their original life expectancy, state transportation agencies (STAs) are now under increased needs to rebuild deteriorated transportation networks. For major highway maintenance projects, a federal rule enforces to perform a life-cycle cost analysis (LCCA).The lack of analytical methods for LCCA creates many challenges of STAs to comply with the rule. To address these critical issues, this study aims at developing a new methodology for quantifying the future maintenance cost to assist STAs in performing a LCCA. The major objectives of this research are twofold: 1) identify the critical factors that affect pavement performances; 2) develop a stochastic model that predicts future maintenance costs of flexible-type pavement in Texas.The study data were gathered through the Pavement Management Information System (PMIS) containing more than 190,000 highway sections in Texas. These data were then grouped by critical performance-driven factor which was identified by K-means cluster analysis. Many factors were evaluated to identify the most critical factors that affect pavement maintenance need. With these data, a series of regression analyses were carried out to develop predictive models. Lastly, a validation study with PRESS statistics was conducted to evaluate reliability of the model. The research results reveal that three factors, annual average temperature, annual precipitation, and pavement age, were the most critical factors under very low traffic volume conditions. This research effort was the first of its kind undertaken in this subject. The maintenance cost lookup tables and stochastic model will assist STAs in carrying out a LCCA, with the reliable estimation of maintenance costs. This research also provides the research community with the first view and systematic estimation method that STAs can use to determine long-term maintenance costs in estimating life-cycle costs. It will reduce the agency's expenses in the time and effort required for conducting a LCCA. Estimating long-term maintenance cost is a core component of the LCCA. Therefore, methods developed from this project have the great potential to improve the accuracy of LCCA.
机译:美国的交通基础设施系统建于50到80年代之间,设计寿命为20年。由于它们中的大多数已经超过了其最初的预期寿命,因此州运输机构(STA)现在对重建恶化的运输网络的需求日益增加。对于大型公路养护项目,联邦法规强制执行生命周期成本分析(LCCA).LCCA缺乏分析方法会给STA遵守法规带来许多挑战。为了解决这些关键问题,本研究旨在开发一种量化未来维护成本的新方法,以帮助STA执行LCCA。这项研究的主要目的是双重的:1)确定影响路面性能的关键因素; 2)建立一个随机模型来预测德克萨斯州柔性路面的未来维护成本。研究数据是通过包含德克萨斯州190,000多个高速公路路段的路面管理信息系统(PMIS)收集的。然后,将这些数据按关键的性能驱动因素进行分组,该因素由K-means聚类分析确定。对许多因素进行了评估,以找出影响路面维护需求的最关键因素。利用这些数据,进行了一系列回归分析以建立预测模型。最后,使用PRESS统计数据进行了验证研究,以评估模型的可靠性。研究结果表明,在交通流量非常低的情况下,年平均温度,年降水量和路面使用年龄这三个因素是最关键的因素。这项研究工作是此主题中进行的首次此类工作。维护成本查找表和随机模型将帮助STA执行LCCA,并可靠地估计维护成本。这项研究还为研究人员提供了第一种观点和系统的估算方法,STA可以使用它们来确定估算生命周期成本的长期维护成本。这将减少机构进行LCCA所需的时间和精力。估算长期维护成本是LCCA的核心组成部分。因此,从该项目开发的方法具有提高LCCA准确性的巨大潜力。

著录项

  • 作者

    Kim Yoo Hyun;

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  • 年度 2012
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  • 原文格式 PDF
  • 正文语种 en_US
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