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首页> 外文期刊>Journal of Infrastructure Systems >Estimation of Pavement Performance Deterioration Using Bayesian Approach
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Estimation of Pavement Performance Deterioration Using Bayesian Approach

机译：用贝叶斯方法估算路面性能恶化

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This paper investigates an incremental pavement performance model based on experimental data from the American Association of State Highway Officials road test. Structural properties, environmental effects, and traffic loading, the three main factors dominating the characteristic of pavement performance, are incorporated into the model. Due to the limited number of variables that can be controlled and observed, unobserved heterogeneity is almost inevitable. Most of the existing models did not fully account for the heterogeneity issue. In this paper, the Bayesian approach is adopted for its ability to address the issue of interest. The Bayesian approach aims to obtain probabilistic parameter distributions through a combination of existing knowledge (prior) and information from the data collected. The Markov chain Monte Carlo simulation is applied to estimate parameter distributions. Due to significant variability in the parameters, the need exists to address heterogeneity in modeling pavement performance. Furthermore, it is shown that not all the parameters are normally distributed. It is therefore suggested that the performance model developed in this research provides a more realistic forecast than most previous models. In addition, pavement deterioration forecast based on the Gibbs output is performed at different confidence levels with varying inspection frequencies, which can enhance the decision-making process in pavement management. In general, the Bayesian approach presented in this paper provides an effective and flexible alternative for model estimation and updating, which can be applied to both the road test data sites and other data sources of interest.
机译：本文基于美国国家公路官员协会道路测试的实验数据研究了增量路面性能模型。结构特性，环境影响和交通负荷是支配路面性能特征的三个主要因素，已纳入模型。由于可以控制和观察的变量数量有限，因此无法避免的异质性几乎是不可避免的。大多数现有模型并未完全解决异质性问题。本文采用贝叶斯方法来解决感兴趣的问题。贝叶斯方法旨在通过结合现有知识（先验知识）和来自所收集数据的信息来获得概率参数分布。马尔可夫链蒙特卡罗模拟用于估计参数分布。由于参数的显着差异，因此需要解决路面性能建模中的异质性问题。此外，示出了并非所有参数都是正态分布的。因此，建议在本研究中开发的性能模型比大多数以前的模型提供更现实的预测。另外，基于吉布斯输出的路面劣化预测在不同的置信度下以不同的检查频率执行，这可以增强路面管理中的决策过程。通常，本文提出的贝叶斯方法为模型估计和更新提供了一种有效而灵活的替代方法，该方法可以应用于路测数据站点和其他感兴趣的数据源。

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