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The Improved Algorithm Based on Cloud Model and the Application of the Deformation Analysis and Prediction for Earth-rockfill Dam

Author: YangHaiYan
Tutor: WangTengJun
School: Chang'an University
Course: Geodesy and Survey Engineering
Keywords: dam deformation monitor cloud model exponential smoothing RBFNN
CLC: TV698.1
Type: Master's thesis
Year: 2009
Downloads: 347
Quote: 4
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Abstract


Deformation monitoring of dam is an important part of safety monitoring, is important measure of ensuring safe operation of the dam, is also effective mean of inspecting design achievements and the quality of the project. A large amount of monitoring data implicated deformation rules of dam. Therefore, it is very important for analyzing the dam deformation monitoring data, established monitoring model timely and accurately, then predicted scientifically. Based on the monitoring data of xiaolangdi dam, the application of cloud model and two algorithms based on the cloud model from the following several aspects:Introduced the definition of cloud model and basic characteristics, forward cloud generator and backward cloud generator, cloud uncertainty reason and cloud transform. Based on the xiaolangdi dam settlement monitoring data, realized the conversion between thequalitative analysis and quantitative analysis, and verifid the essence of cloud theory------theuncertainty transformation model between qualitative and quantitative.Then, discussed the fitting method of monitor data based on the cloud transform, introduced the specific implementation steps, gived an example base on the actual monitor data, compared the result of the cloud transform fitted method to the result of the least-square subsection fitted method. The results showed that the precision of the cloud transform fittted is higher.Established three exponential smoothing model based on the xiaolangdi dam settlement monitoring data. But it is difficult to determine smoothing coefficient, proposed a new method based on cloud logical reason, this is to improve the original method. To tested the improved model with the monitoring data. The results showed that the precision of fitting and prediction is higher with the improved method.Combined the advantages of RBFNN and cloud model, proposed the improved method of RBFNN with the cloud model, it is used the three characteristics of cloud model to determine the value of RBFNN for modeling is necessary part of parameters. Setted up two kinds of common RBFNN model:①RBFNN model based on OLS,②RBFNN model based on the nearest neighbor clustering algorithm, based on the settlement monitoring data, temperature, water level and time data. At the same time, the improved RBFNN model is established with the same data set. The results showed that the fitted and predicted accuracy of the improved model is higher than other two models. But the improved method considered the fuzziness and the randomness of the sample data, and the prediction error is still great room for improvement.

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CLC: > Industrial Technology > Hydraulic Engineering > Water control,hydraulic structures > Hydraulic structures management > Monitoring of hydraulic structures and prototype observation
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