摘要
为了找到一种能够精确有效地预测桥梁运营状况的方法,提出一种基于灰色GM(1,1)理论模型并用马尔科夫链修正的灰色-马尔科夫预测模型.结合河北省某地区的159座桥梁数据对该方法进行应用检验,结果表明:灰色-马尔科夫模型预测数据的平均相对误差为-0.11%,相比灰色GM(1,1)理论模型预测数据的平均相对误差-0.34%,在精度上有了明显的提高,而且灰色-马尔科夫模型预测出的数据更加稳定.利用马尔科夫链优化过的灰色GM(1,1)理论模型预测出2017年至2019年该地区一类桥的数量分别为49座、39座以及34座.由此可知灰色-马尔科夫模型在已知的桥梁定期检查数据基础上可以提供较为精确的预测,相较于灰色GM(1,1)预测模型,该方法具有更高的精度和稳定性.
In order to obtain a high-precision and high-efficiency method to predict the bridge operating conditions, this paper presents a Gray-Markov GM(1,1) prediction model based on gray theoretical model and modified with Markov chain. This method was applied to test the data of a total of 159 bridges in a certain area of Hebei Province. The results show that based on the bridge data from 2007 to 2016, the average relative error of the Gray-Markov model is -0.11%, and the average relative error of the gray theoretical model is -0.34%, which is obviously improved, and the Gray-Markov model offers more stable data. Using the gray theory model optimized by Markov chain to predict the number of first-class bridges from 2017 to 2019,we can see that the number of the first-class bridges is 49, 39 and 34 respectively. Thus we can see that the Gray-Markov model can provide a more accurate prediction on the basis of known periodic inspection data. Compared with the single gray prediction model, the Gray-Markov model shaws higher accuracy and stability.
作者
刘历波
裴彧
裴同松
LIU Libo;PEI Yu;PEI Tongsong(College of Civil Engineering,Hebei University of Engineering,Handan 056038,China;Department of Civil Engineering,Hebei Jiaotong Vocational and Technical College,Shijiazhuang 050035,China;Department of Electrical and Information Engineering,HebeiJiaotong Vocational and Technical College,Shijiazhuang 050035,China)
出处
《河北大学学报(自然科学版)》
CAS
北大核心
2019年第1期11-17,共7页
Journal of Hebei University(Natural Science Edition)
基金
河北省高校百名优秀人才计划项目(BR206)