机场跑道性能退化随机过程模型的贝叶斯数据更新方法  

Data updating method with Bayesian analysis for stochastic process model of performance degradation of airport runway

作　　者：魏保立[1,2] 郭成超 王复明 闫卫红[4] WEI Baoli;GUO Chengchao;WANG Fuming;YAN Weihong(School of Civil and Architecture,Zhengzhou University of Aeronautics,450046 Zhengzhou,China;School of Water Conservancy and Environment,Zhengzhou University,450001 Zhengzhou,China;School of Civil Engineering,Sun Yat-sen University,519082 Zhuhai,China;Henan Airporl Group,Zhengzhou 450000,China)

机构地区：[1]郑州航空工业管理学院土木建筑学院,郑州450046 [2]郑州大学水利科学与工程学院,郑州450001 [3]中山大学土木工程学院,珠海519082 [4]河南省机场集团有限公司,郑州450000

出　　处：《应用力学学报》2022年第2期356-366,共11页Chinese Journal of Applied Mechanics

基　　金：河南省高等学校重点科研资助项目(No.21B580008);河南省科技攻关资助项目(No.182102310747);郑州新郑国际机场三期扩建工程技术研究资助项目(No.NFXQGZ-033)。

摘　　要：为了精确预测民用机场跑道的剩余使用寿命,利用数据融合技术将两种数据集进行联合分析,采用贝叶斯概率预测方法对机场跑道定期检测数据进行更新。考虑机场道面退化过程的随机性和动态性,建立了一种机场道面性能退化的动态半马尔可夫随机过程模型,利用生存分析对半马尔可夫过程模型的转移概率进行了估计。考虑飞行交通量和道面厚度的双重影响,采用某地方民用机场2007—2017年的道面性能定期检测数据,分析了两种影响因素的作用。利用半马尔可夫过程模型的转移概率对某地方民用机场跑道性能退化过程进行了预测,并采用马尔可夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)模拟技术,基于贝叶斯统计分析方法,利用不定期检测数据得到的先验信息对半马尔可夫过程模型的转移概率进行了更新,将更新后的模型应用于民用机场跑道性能预测,并将预测结果和未更新的动态半马尔可夫过程模型预测结果进行了对比分析。结果表明,基于MCMC的贝叶斯分析方法能够融合定期和不定期检测数据确定动态半马尔可夫过程模型的参数先验信息,可以有效地更新机场跑道的性能预估模型,提高模型的预测精度。In order to accurately predict residual service life of civil airport runway,two data sets were analyzed by using the data fusion technology and joint estimation approach.The periodic test data of airport runways were updated with Bayesian probability prediction method with the irregularly nondestructive testing data of runways.Considering the randomness and dynamics of airport pavement degradation,a dynamic semi-Markov stochastic process model of airport pavement performance degradation was established.The transition probability of the semi-Markov process model was estimated by using the Weibull function parameter model of survival analysis.In the process of building the model,the dual effects of flight traffic and pavement surface thickness were considered,and the measured pavement performance data set of local civil airports was adopted to analyze the influence of these two factors in Henan Province from 2007 to 2017.The performance degradation process of runways of the civil airport in Henan province was predicted by using the transition probability of the semi-Markov process model.The transition probability was updated by adopting the Bayesian statistical analysis method based on Markov Chain Monte Carlo(MCMC)simulation technology with Metropolis-Hasting(MH)sampling method,using a priori information obtained from the irregular detection data of the airport runway.Finally,the updated model is applied to predict the performance of the local civil airport runway in Henan Province.The results show that the Bayesian analysis method based on MCMC can fully integrate the regular and irregular detection data to determine the prior information of parameters of the dynamic semi-Markov process model,and can effectively update the performance prediction model of the airport runway and improve the prediction accuracy of the model.

关 键 词：机场跑道 性能退化 半马尔可夫过程 生存分析 贝叶斯分析 

分 类 号：U461.216[机械工程—车辆工程] V315.11[交通运输工程—载运工具运用工程]