路基工程
路基工程
로기공정
SUBGRADE ENGINEERING
2012年
4期
1-3
,共3页
沥青路面%平整度%卡尔曼滤波%时间序列
瀝青路麵%平整度%卡爾曼濾波%時間序列
력청로면%평정도%잡이만려파%시간서렬
asphalt pavement%roughness%Kalman filtering%time series
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。
路麵平整度的髮展趨勢受交通量、溫度及使用時間等許多因素的影響,很難建立綜閤全麵的預測模型。而時間序列法利用歷年的IRI值可解決這箇問題。針對傳統的時間序列法計算的不足,提齣瞭將卡爾曼濾波應用于時間序列IRI預測模型的方法,能充分利用觀測值對狀態估值進行實時脩正,可有效提高預測模型的預測精度,且無需儲存大量的歷史觀測數據。最後,通過實例證明瞭該模型有效可行。
로면평정도적발전추세수교통량、온도급사용시간등허다인소적영향,흔난건립종합전면적예측모형。이시간서렬법이용력년적IRI치가해결저개문제。침대전통적시간서렬법계산적불족,제출료장잡이만려파응용우시간서렬IRI예측모형적방법,능충분이용관측치대상태고치진행실시수정,가유효제고예측모형적예측정도,차무수저존대량적역사관측수거。최후,통과실예증명료해모형유효가행。
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.
