Development of pavement roughness master curves using Markov Chain
Development of pavement roughness master curves using Markov Chain
复制标题
使用马尔可夫链开发路面粗糙度主曲线
DOI：
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发表时间：
2020
影响因子：
3.8
通讯作者：
Seyed Mohammad Asgharzadeh
中科院分区：
文献类型：
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作者：
Saeid Alimoradi;A. Golroo;Seyed Mohammad Asgharzadeh
摘要目前，以马尔可夫链过程(MCP)为代表的概率预测模型在路面管理系统中受到越来越多的关注。此外，粗糙度条件指数提供了一个宽泛的判断。对路面平整度影响最大的一个因素是路面的初始平整度，而在MCP开发的预测模型中没有考虑到这一点。另一方面，MCP的预测结果是对整个道路网的平均预测，不便于在决策程序中使用。本文利用MCP对路面平整度的初值进行预测。选取长期路面使用性能(LTPP)数据库中提取的国际平整度指数(IRI)进行分析。根据M&R历史，介绍了四大家族，共计1770个路面路段。对MCP的预测过程进行了改进，直接预测了IRI值，而不是对路面路段的劣化部分进行预测。从主曲线的概念出发，提出了一个框架，为优化程序应用于MCP结果铺平了道路。合成的主曲线的均方根误差非常低，平均均方误差为0.00675。
ABSTRACT Nowadays, probabilistic prediction models commonly represented by Markov Chain Process (MCP) attract more attention in pavement management systems. Additionally, roughness condition indices provide a broad judgment. One element that contributes the most to the roughness progression is the initial roughness of pavements which has not been considered in the prediction models developed by MCP. On the other hand, the prediction results of MCP, which address the whole pavement network as an average, are inconvenient to be deployed in decision-making programmes. This paper utilised MCP to forecast pavement roughness regarding its initial value. International Roughness Index (IRI), extracted from the long-term pavement performance (LTPP) database, was selected to be analyzed. Based on M&R history, four major families, including 1770 pavement sections in total, were introduced. The prediction process of MCP was modified led to the direct prediction of IRI values instead of the deteriorated portion of the pavement sections. A framework, which had derived from the concept of Master Curves, is proposed to pave the way for the optimisation programmes to be applied to the MCP results. The Root Mean Squared Errors (RMSE) of the composed master curves were significantly low; the average RMSE was 0.00675.