本文關(guān)鍵詞：基于模糊kohonen聚類算法的橋梁健康監(jiān)測數(shù)據(jù)挖掘模型的建立　出處：《哈爾濱工業(yè)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 健康監(jiān)測 數(shù)據(jù)挖掘 Kohonen算法 有限元模型 數(shù)據(jù)預(yù)處理 聚類分析

【摘要】：近年來，我國基礎(chǔ)設(shè)施建設(shè)發(fā)展迅速，橋梁結(jié)構(gòu)作為交通運輸?shù)闹匾A(chǔ)設(shè)施，安全問題備受人們的關(guān)注。為保障橋梁的安全運營，橋梁結(jié)構(gòu)健康監(jiān)測系統(tǒng)得到了廣泛的應(yīng)用。在橋梁健康監(jiān)測系統(tǒng)中，包括數(shù)量眾多的傳感器，這些傳感器持續(xù)、長期、實時的采集各類數(shù)據(jù)。隨著監(jiān)測時間的不斷增長，形成海量數(shù)據(jù)。本文通過研究基于kohonen算法的數(shù)據(jù)挖掘方法，對海量監(jiān)測數(shù)據(jù)進行分析計算，為橋梁的后期評估以及預(yù)警提供基礎(chǔ)數(shù)據(jù)。主要研究內(nèi)容如下： 第一，以三個隨機數(shù)組為例對算法進行試驗，通過三種數(shù)組的聚類結(jié)果分析算法中存在的不足。針對算法收斂速度和計算準確性兩方面的不足提出了算法的改進，通過三個例子對比算法改進前后的收斂速度以及聚類準確性，證明算法的改進具有很好的效果。 第二，利用有限元分析軟件邁達斯，根據(jù)甬江特大橋的設(shè)計圖紙建立大橋的初始有限元模型，并對大橋進行了初步的分析。分析橋梁初始有限元模型中可能存在的三種誤差，確定產(chǎn)生每一種誤差的原因。為了獲得結(jié)構(gòu)的基準有限元模型，減小各方面的誤差，對結(jié)構(gòu)的初始有限元模型進行修正。以結(jié)構(gòu)的動力特性作為目標量對模型進行修正，修正后達到了很好的效果。利用修正后的結(jié)構(gòu)基準有限元模型，模擬橋梁試驗荷載，對橋梁存在的靜力誤差進行驗證，，通過結(jié)果可以看出，所得的結(jié)構(gòu)基準有限元模型的靜力方面誤差要小于未進行修正的誤差。 第三，對健康監(jiān)測原始數(shù)據(jù)進行預(yù)處理，進行濾波處理。結(jié)合kohonen算法與結(jié)構(gòu)的基準有限元模型建立橋梁健康監(jiān)測的數(shù)據(jù)挖掘聚類分析模型，確定識別異常數(shù)據(jù)的異常閥值。最后對得到的聚類模型進行驗證，證明聚類分析模型的有效性。
[Abstract]:In recent years, the construction of infrastructure in China has developed rapidly. Bridge structure, as an important infrastructure for transportation, has attracted people's attention in order to ensure the safe operation of bridges. Bridge structural health monitoring system has been widely used. In the bridge health monitoring system, including a large number of sensors, these sensors continue, long-term. Collect all kinds of data in real time. With the continuous growth of monitoring time, the formation of massive data. This paper studies the method of data mining based on kohonen algorithm to analyze and calculate the massive monitoring data. To provide basic data for post-assessment and early warning of bridges. The main contents of this study are as follows: First, take three random arrays as an example to test the algorithm. Through the analysis of three kinds of array clustering results, the shortcomings of the algorithm, aiming at the convergence speed and computational accuracy of the algorithm two aspects of the improvement of the algorithm is put forward. By comparing the convergence rate and clustering accuracy of the improved algorithm with three examples, it is proved that the improved algorithm has a good effect. Secondly, the initial finite element model of the bridge is established according to the design drawings of Yongjiang Bridge by using the finite element analysis software Midas. Three possible errors in the initial finite element model of the bridge are analyzed, and the causes of each error are determined. In order to obtain the benchmark finite element model of the structure, the bridge is analyzed preliminarily. The initial finite element model of the structure is modified by reducing the errors in all aspects. The dynamic characteristics of the structure are taken as the target to modify the model. The modified finite element model is used to simulate the experimental load of the bridge, and the static error of the bridge is verified, which can be seen from the results. The static error of the structural benchmark finite element model is smaller than that of uncorrected finite element model. Thirdly, preprocessing the original data of health monitoring and filtering. Combining kohonen algorithm with the benchmark finite element model of structure, the data mining cluster analysis model of bridge health monitoring is established. Finally, the clustering model is verified to prove the validity of the clustering analysis model.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U446

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