【摘要】：橋梁這項(xiàng)建筑工程量大、造價(jià)高且在交通經(jīng)濟(jì)中重要，為確保其在運(yùn)營(yíng)期間符合生命安全標(biāo)準(zhǔn)，因此橋梁結(jié)構(gòu)損傷的早期識(shí)別和評(píng)估是必要的。橋梁損傷位置和程度識(shí)別是這項(xiàng)研究的核心。當(dāng)模態(tài)特征未知時(shí)，模式識(shí)別被廣泛的用于此項(xiàng)研究，是一種用來(lái)幫助進(jìn)行損傷識(shí)別的典型方法。模式識(shí)別方法是具備隱性識(shí)別相關(guān)和不相關(guān)變量的非線性關(guān)系的能力，具有自學(xué)習(xí)和容錯(cuò)能力。這些優(yōu)勢(shì)讓它能夠恰當(dāng)?shù)淖钚』陧憫?yīng)測(cè)度和有限元模型結(jié)構(gòu)的負(fù)面影響。對(duì)于橋梁這樣的大型結(jié)構(gòu)，我們顯然不能去了解每一個(gè)個(gè)體，也就是說(shuō)每一個(gè)實(shí)測(cè)部位的狀態(tài)，而是通過(guò)模式識(shí)別方法有效地實(shí)現(xiàn)對(duì)橋梁的損傷進(jìn)行快速、精確和智能化識(shí)別，從而保障橋梁這樣的重大工程結(jié)構(gòu)的安全性、完整性、適用性與耐久性。目前還鮮有利用一系列模式識(shí)別算法進(jìn)行系統(tǒng)的橋梁靜力和動(dòng)力損傷識(shí)別研究，因此全文作了該方面系統(tǒng)的研究工作如下： 1、用模式識(shí)別對(duì)橋梁進(jìn)行損傷識(shí)別的前提是數(shù)據(jù)的真實(shí)性，但是這些數(shù)據(jù)是海量的，其有效性難以通過(guò)一些常規(guī)手段進(jìn)行檢驗(yàn)。傳感器最優(yōu)布置的建模方式和智能算法的選取是解決問(wèn)題的關(guān)鍵，因此本論文中傳感器的最優(yōu)化布置問(wèn)題將從這兩個(gè)方面入手解決。一是建立了以模態(tài)振型為隨機(jī)變量的傳感器優(yōu)化布置單目標(biāo)和多目標(biāo)整數(shù)規(guī)劃期望值模型，二是利用DNA遺傳算法求解此類問(wèn)題的優(yōu)點(diǎn)，并設(shè)計(jì)了求解算法，最后通過(guò)徐葛大橋?yàn)閷?shí)例驗(yàn)證了算法的可行性和有效性。 2、利用SVM（支持向量機(jī)）這種模式識(shí)別方法做橋梁靜力損傷識(shí)別，關(guān)鍵在于利用ANSYS進(jìn)行數(shù)值模擬構(gòu)造無(wú)損傷和損傷時(shí)的訓(xùn)練集與實(shí)際工程的相似性，以表現(xiàn)其抗干擾能力；用帶有噪聲的測(cè)試集去判定損傷位置和損傷程度的精確度以表現(xiàn)其對(duì)損傷的區(qū)分能力。針對(duì)這個(gè)問(wèn)題，，本論文一是詳細(xì)給出了不同噪聲情況和不同工況加載模式下的撓度響應(yīng)的高精度模式識(shí)別結(jié)果，二是利用專業(yè)數(shù)據(jù)挖掘軟件WEKA作對(duì)比分析，證明了本方法的有效性。正確進(jìn)行橋梁靜力損傷識(shí)別后，另一個(gè)問(wèn)題是加載模式的識(shí)別。本論文將輪廓系數(shù)應(yīng)用到橋梁靜力加載模式的識(shí)別當(dāng)中，結(jié)果表明有很好的識(shí)別效果，并且具有一定的實(shí)際應(yīng)用價(jià)值。利用SVM（支持向量機(jī)）這種模式識(shí)別方法做大型橋梁頻域損傷識(shí)別，關(guān)鍵在于損傷節(jié)點(diǎn)和單元的選擇和計(jì)算識(shí)別的精度與抗噪性。針對(duì)這兩個(gè)問(wèn)題，本文首先根據(jù)前文傳感器優(yōu)化布置點(diǎn)作為損傷識(shí)別對(duì)象，然后利用SVM模式識(shí)別方法進(jìn)行損傷位置和程度識(shí)別的精確識(shí)別和噪聲檢驗(yàn)，最后利用專業(yè)數(shù)據(jù)挖掘軟件WEKA作對(duì)比分析，證明了本方法具有一定的合理性和優(yōu)勢(shì)。在討論車過(guò)橋的時(shí)域損傷的模式識(shí)別時(shí)，首要問(wèn)題是怎樣采用ANSYS軟件進(jìn)行模擬計(jì)算，獲取相應(yīng)的損傷指標(biāo)數(shù)據(jù)�；谀芰勘鹊臅r(shí)域指標(biāo)可以根據(jù)測(cè)點(diǎn)采樣間隔獲取車從上橋到下橋的速度響應(yīng)，以損傷前后的能量比作為該測(cè)點(diǎn)的損傷指標(biāo)。由此本文提出利用能量比指標(biāo)的SVM損傷識(shí)別方法。 3、采取分步識(shí)別的橋梁損傷識(shí)別的模式識(shí)別方法主要分為兩個(gè)步驟：損傷位置識(shí)別和損傷程度識(shí)別，其根本分別就是分類和回歸問(wèn)題。本文提出以SOM神經(jīng)網(wǎng)絡(luò)做損傷位置識(shí)別聚類分析，RBF神經(jīng)網(wǎng)絡(luò)做損傷程度識(shí)別回歸分析。損傷位置識(shí)別是損傷識(shí)別的關(guān)鍵一步，在無(wú)先驗(yàn)知識(shí)的情況下只能進(jìn)行聚類識(shí)別。雖然存在許多的聚類方法，但是沒(méi)有一個(gè)通用的萬(wàn)能的聚類方法，能夠適用于所有的聚類問(wèn)題，聚類集成算法因此被提出并證明能解決更多的問(wèn)題。本文利用基于Co-occurrence相似度的聚類集成（CSCE）和基于矩陣變換的聚類集成方法去識(shí)別桁架架構(gòu)和徐葛大橋的損傷位置，達(dá)到完全識(shí)別。本文最后還利用專業(yè)數(shù)據(jù)挖掘軟件WEKA作對(duì)比分析，證明了本方法的有效性�；诖植诩木垲惙椒芙Y(jié)合集合方法和概率方法計(jì)算樣本的相似度，具有很好的聚類效果。本文利用粗聚類方法去識(shí)別橋梁的損傷位置，達(dá)到良好的識(shí)別效果，并與模糊聚類（FCM）作了比較。根據(jù)該方法還能夠得到樣本的屬性約簡(jiǎn)結(jié)果和約簡(jiǎn)規(guī)則，為進(jìn)一步研究樣本特征提供了參考數(shù)據(jù)。
[Abstract]:Bridge construction has a large quantity, high cost and important in traffic economy. In order to ensure that the bridge conforms to the life safety standard during operation, it is necessary to identify and evaluate the damage of bridge structure early. The location and degree identification of bridge damage is the core of this research. When the modal characteristics are unknown, pattern recognition is widely used in this study. Item research is a typical method used to assist in the identification of damage. The pattern recognition method is the ability to recognize the nonlinear relation of the related and unrelated variables, and has the ability of self learning and fault tolerance. These advantages make it able to minimize the negative effects on the response measure and the structure of the finite element model. It is obvious that we can not understand each individual, that is, every state of the measured site, but through the pattern recognition method, we can effectively realize the damage of the bridge quickly, accurately and intelligently, so as to guarantee the safety, integrity, applicability and durability of the bridge such as the bridge. A series of pattern recognition algorithms have been used to identify the static and dynamic damage identification of the bridge, so the full text of this system is as follows:
1, the premise of identification of bridge damage by pattern recognition is the authenticity of the data, but the data are massive, its effectiveness is difficult to be tested by some conventional means. The key to solve the problem is the modeling method of the optimal layout of the sensor and the selection of intelligent algorithms. Therefore, the optimization layout of the sensor in this paper is a problem. The first is to solve these two aspects. One is to set up a single objective and multi-objective integer programming expectation model with the modal vibration type as the random variable. Two is the advantage of using the DNA genetic algorithm to solve the problem, and the solution algorithm is designed. Finally, the feasibility and the feasibility of the algorithm are verified by the Xu Ge bridge. Efficiency.
2, the key is to use the SVM (support vector machine) to identify the bridge static damage. The key is to use ANSYS to simulate the similarity between the training set and the actual project without damage and damage, in order to show its anti-interference ability, and to determine the accuracy of the damage position and degree of damage by using a test set with noise. In this paper, the high precision pattern recognition results of the different noise conditions and the deflection responses under different loading modes are given in this paper. Two is a comparative analysis using the professional data mining software WEKA, which proves the validity of this method. Then, another problem is the identification of loading mode. In this paper, the contour coefficient is applied to the identification of bridge static loading mode. The result shows that it has good recognition effect and has a certain practical application value. The key to damage identification of large bridge in frequency domain by using the SVM (support vector machine) pattern recognition method is the damage node. The selection of point and unit and the accuracy and noise resistance of calculation recognition. Aiming at these two problems, first of all, this paper is based on the optimization points of the previous sensor as the damage identification object, and then uses the SVM pattern recognition method to identify the damage location and degree recognition and the noise test. Finally, the comparison of the professional data mining software WEKA is used as a contrast. The analysis shows that this method has a certain rationality and advantages. When discussing the pattern recognition of time domain damage in the vehicle bridge, the first problem is how to use ANSYS software to simulate and obtain the corresponding damage index data. Based on the time domain index of the energy ratio, the speed of the vehicle from the upper bridge to the lower bridge can be obtained by the sampling interval of the measurement point. In response, the energy ratio before and after injury is taken as the damage index of the measuring point. In this paper, a SVM damage identification method using energy ratio index is proposed.
3, the pattern recognition method of bridge damage recognition by step identification is divided into two steps: damage location identification and damage degree recognition, which are classified and regression problems. This paper proposes a SOM neural network for damage location identification clustering analysis, RBF neural network network damage identification regression analysis. Recognition is a key step in the identification of damage, which can only be identified without prior knowledge. Although there are many clustering methods, there is no universal universal clustering method, which can be applied to all clustering problems. Therefore, the clustering integration algorithm has been proposed and proved to be able to solve more problems. This paper is based on Co. -occurrence similarity clustering integration (CSCE) and cluster integration method based on matrix transformation are used to identify the damage location of the truss structure and Xu Ge bridge to complete recognition. In the end, the validity of this method is proved by the comparative analysis of the professional data mining software WEKA. The clustering method based on Rough Sets can combine the collective cube. The method and probability method are used to calculate the similarity of the sample. This paper uses the rough clustering method to identify the damage location of the bridge, and achieves a good recognition effect, and compares it with the fuzzy clustering (FCM). According to this method, the results of the reduction and reduction of the sample are also obtained, which can be used for the further study of the sample characteristics. For reference data.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：U446

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王慎超;苗奪謙;陳敏;王睿智;;基于覆蓋的粗糙聚類算法[J];電子與信息學(xué)報(bào);2008年07期

2 李戈,秦權(quán),董聰;用遺傳算法選擇懸索橋監(jiān)測(cè)系統(tǒng)中傳感器的最優(yōu)布點(diǎn)[J];工程力學(xué);2000年01期

3 何明;馮博琴;馬兆豐;傅向華;;一種基于高斯混合模型的無(wú)監(jiān)督粗糙聚類方法[J];哈爾濱工業(yè)大學(xué)學(xué)報(bào);2006年02期

4 王淑超,王乘;改進(jìn)DNA遺傳算法求解非線性多約束規(guī)劃研究[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2004年06期

5 胡云;苗奪謙;王睿智;陳敏;;一種基于粗糙k均值的雙聚類算法[J];計(jì)算機(jī)科學(xué);2007年11期

6 張瓊;張瑩;白清源;謝麗聰;謝伙生;;一種新的基于粗糙集的leader聚類算法[J];計(jì)算機(jī)科學(xué);2008年03期

7 何明;;一種基于粒度的粗糙聚類分析方法[J];計(jì)算機(jī)工程;2008年08期

8 王明春;唐萬(wàn)生;江琪;劉鑫;;基于相對(duì)距離的改進(jìn)粗K-means方法[J];計(jì)算機(jī)應(yīng)用;2009年04期

9 周濤;張艷寧;袁和金;陸惠玲;鄧方安;;粗糙核k-means聚類算法[J];系統(tǒng)仿真學(xué)報(bào);2008年04期

10 ;Chance Constrained Integer Programming and Stochastic Simulation Based Genetic Algorithm[J];Journal of Systems Science and Systems Engineering;1998年01期

本文編號(hào)：2133291