港口湾水库大坝变形监测模型的研究分析

发布时间：2018-08-03 08:08

【摘要】：变形监测是监测变形体是否安全的一种重要手段，具体过程是通过对变形体的监测取得原始变形数据，然后对变形数据进行分析处理，判断出变形体的安全状况。在初期蓄水和长期运行中，大坝都存在着发生事故的可能性。大坝一旦出现异常状态，必须及时发现和处理，否则可能导致严重后果。但是对于大坝类型监测对象的变形分析，除了要关注过去的变形状态，还应该预测变形体将来可能发生的变形，实时监控变形体的安全状况，，做到安全事故的提前预防，这就需要建立监测体变形预测模型。变形监测模型是对已有的变形监测数据结果做统计分析，找出数据间的规律性，利用建模工具建立监测模型，从而达到预测出下一周期的可能的形变量。本文以港口湾水库大坝的形变为变形监测对象，获取了2003年至2012年各个年度不同监测周期的大量变形监测数据，以具有强大的数值计算和仿真功能的Matlab软件为工具，采用极限误差法和拉格朗日插值方法对原始监测数据进行预处理，然后根据灰色系统分析理论和人工神经网络理论，分别以月度、季度、年度为周期整理原始数据，建立GM(1，1)模型和BP神经网络模型进行预报和分析，与港口湾水库大坝实际情况相结合分析模型的可靠性，得出一些适用于港口湾水库大坝安全监测的结论。
[Abstract]:Deformation monitoring is an important means to monitor the safety of deformable body. The concrete process is to obtain the original deformation data by monitoring the deformable body, and then analyze and process the deformation data to judge the safety condition of the deformable body. In the initial water storage and long-term operation, the dam has the possibility of accidents. In case of abnormal state of dam, it must be detected and dealt with in time, otherwise it may lead to serious consequences. But for the deformation analysis of dam type monitoring object, in addition to paying attention to the deformation state in the past, we should also predict the deformation that may occur in the future, monitor the safety condition of the deformable body in real time, and prevent the safety accident in advance. Therefore, it is necessary to establish the model of deformation prediction for monitoring body. The deformation monitoring model is to make statistical analysis of the existing deformation monitoring data, to find out the regularity between the data, and to establish the monitoring model by using modeling tools, so as to predict the possible shape variables of the next cycle. In this paper, the deformation of the dam of Port Bay Reservoir is taken as the object of deformation monitoring, and a large number of deformation monitoring data of different monitoring periods from 2003 to 2012 are obtained. The Matlab software, which has a powerful function of numerical calculation and simulation, is used as a tool. The limit error method and Lagrange interpolation method are used to preprocess the original monitoring data. According to the grey system analysis theory and the artificial neural network theory, the original data are sorted out in monthly, quarterly and annual periods, respectively. The GM (1 + 1) model and BP neural network model are established for prediction and analysis. The reliability of the model is analyzed in combination with the actual situation of the port bay reservoir dam, and some conclusions suitable for dam safety monitoring of the port bay reservoir are obtained.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TV698.1

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