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基于时间序列技术的中小桥梁监测数据分析技术研究

发布时间:2018-08-05 09:48
【摘要】:目前国内外在桥梁长期监测技术的研究领域主要集中于大型桥梁。由于中小桥梁数量更多,垮塌事故主要发生在中小桥梁,因此,应加大中小桥梁的长期监测系统的研究。本文依托《基于物联网的中小桥梁长期安全研究》(国家973科研项目课题),采用基于时间序列的数据挖掘技术,对中小桥梁监测数据进行挖掘和分析,为中小桥梁监测数据处理提供了新的思路。取得主要工作成果有:①介绍了时间序列分析方法的基本理论,分析了时间序列中的各个数据的影响因素,为建立数学模型提供理论基础。②根据桥梁长期监测系统采集到的数据特点,论证了以时间序列作为理论基础建立ARMA模型对数据进行分析预测的可行性。并对ARMA模型的建立过程进行了详细论述,其中包括数据的最初采集,数据平稳化、标准化处理,模型参数的估计,模型阶数的确定等问题。通过比选,ARMA模型参数的估计选择了最小二乘估计法,模型阶数的确定选择了AIC准则。③以一座中小桥——偏岩子桥为依托,将基于时间序列理论的预测理论应用于数据挖掘中。利用MATLAB软件对该桥的裂缝测点、挠度测点、倾斜度测点、应变测点的监测数据,建立ARMA模型,调整到合适的模型阶数及模型参数后,进行每次向外延伸5步的短期预测,对预测值与实际监测值进行比较,从而验证基于ARMA模型的中小桥梁监测数据挖掘方法的可行性与有效性。
[Abstract]:At present, the research field of long-term bridge monitoring technology at home and abroad mainly concentrates on large-scale bridges. Because of the large number of small and medium-sized bridges, the collapse accidents mainly occur in small and medium-sized bridges, therefore, the long-term monitoring system of small and medium-sized bridges should be studied. Based on the long term Safety Research of small and Medium-sized Bridges based on the Internet of things (the national 973 scientific research project), this paper uses the data mining technology based on time series to mine and analyze the monitoring data of small and medium-sized bridges. It provides a new idea for monitoring data processing of medium and small bridges. The main achievements are: 1 introduces the basic theory of time series analysis method, analyzes the influence factors of each data in time series, In order to provide theoretical basis for the establishment of mathematical models, according to the characteristics of the data collected by the bridge long-term monitoring system, the feasibility of establishing the ARMA model to analyze and predict the data based on the time series is demonstrated. The establishment process of ARMA model is discussed in detail, including the initial collection of data, the stabilization of data, the standardization of processing, the estimation of model parameters, the determination of model order and so on. The least square estimation method is used to estimate the parameters of the model. The order of the model is determined by AIC criterion .3. The prediction theory based on the time series theory is applied to the data mining based on a medium and small bridge. By using MATLAB software, the monitoring data of crack, deflection, inclination and strain measurement points of the bridge are measured, and the ARMA model is established. After adjusting to the appropriate model order and model parameters, the short term prediction of 5 steps extending out each time is carried out. By comparing the predicted values with the actual monitoring values, the feasibility and effectiveness of the monitoring data mining method for small and medium-sized bridges based on ARMA model are verified.
【学位授予单位】:重庆交通大学
【学位级别】:硕士
【学位授予年份】:2015
【分类号】:U446

【参考文献】

相关期刊论文 前1条

1 梁宗保;陈伟民;符欲梅;胡顺仁;朱永;;混凝土桥梁结构应变监测的温度效应分离方法研究[J];混凝土;2005年12期



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