| 卡尔曼时间序列模型预测路面平整度指数 |
| |
| 引用本文： | 颜可珍,张邹,林峰.卡尔曼时间序列模型预测路面平整度指数[J].路基工程,2012,0(4):1-3. |
| |
| 作者姓名： | 颜可珍 张邹 林峰 |
| |
| 作者单位： | 1.1山区道路建设与维护技术重庆市重点实验室，重庆 400074 |
| |
| 基金项目： | 重庆市重点实验室开放基金项目:重庆交通大学山区道路建设与维护技术 |
| |
| 摘 要： | 路面平整度的发展趋势受交通量、温度及使用时间等许多因素的影响，很难建立综合全面的预测模型。而时间序列法利用历年的IRI值可解决这个问题。针对传统的时间序列法计算的不足，提出了将卡尔曼滤波应用于时间序列IRI预测模型的方法，能充分利用观测值对状态估值进行实时修正，可有效提高预测模型的预测精度，且无需储存大量的历史观测数据。最后，通过实例证明了该模型有效可行。
|
| 关 键 词： | 沥青路面 平整度 卡尔曼滤波 时间序列 |
| 收稿时间： | 2019-01-25 |
Prediction on Roughness Index of Pavement by Kalman Time Series Model |
| |
| Authors: | YAN Ke-zhen ZHANG Zou LIN Feng |
| |
| Affiliation: | 1. Chongqing Hi-tech Laboratory for Mountain Highway Construction and Maintenance, Chongqing 400074, China; 2. School of Civil Engineering, Hunan University, Changsha 410082, China) |
| |
| Abstract: | Pavement roughness is developed with traffic volume, temperature and service time, as a result, the comprehensive prediction model is difficult to be established. However, it could be solved by time series model using IRI value over the years. In relation to the deficiencies in calculation by traditional time series model, the way in which Kalman filtering is applied to time series prediction model using IRI is suggested, which can carry out real-time correction on state estimates according to observed value and effectively increases the accuracy of the prediction model without a large quantity of observation data stored. Finally, the availability and feasibility of the model is proved through examples. |
| |
| Keywords: | asphalt pavement roughness Kalman filtering time series |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《路基工程》浏览原始摘要信息 |
|
点击此处可从《路基工程》下载全文 |
