基于空間相關(guān)性的索力傳感器優(yōu)化布置及全橋索力反演預測研究
發(fā)布時間：2018-11-04 21:44
【摘要】：作為纜索體系橋梁中的重要承重結(jié)構(gòu),拉索成為橋梁結(jié)構(gòu)安全評價的一個重要因素,索力監(jiān)測在大跨徑斜拉橋和懸索橋的健康監(jiān)測系統(tǒng)中已經(jīng)成為必不可少的監(jiān)測項目。一個合理的拉索監(jiān)測方案,特別是索力監(jiān)測位置極大程度地影響著橋梁結(jié)構(gòu)安全評估的準確性,也是建造經(jīng)濟節(jié)約型健康監(jiān)測系統(tǒng)的關(guān)鍵。本文從拉索群荷載響應(yīng)的空間相關(guān)性出發(fā)提出了基于索力相關(guān)性的傳感器優(yōu)化方法,并利用最優(yōu)測點處的索力信息實現(xiàn)了對未監(jiān)測位置處拉索索力的估計,達到了利用有限的傳感器最大限度地獲取全橋拉索荷載響應(yīng)的目的。首先采用B樣條擬合法提取由環(huán)境因素引起的索力趨勢項為研究對象,以Pearson相關(guān)系數(shù)、最大信息系數(shù)(MIC)和互信息系數(shù)(MI)三個系數(shù)作為拉索群空間相關(guān)性的度量指標,深入地挖掘不同位置拉索間荷載響應(yīng)的內(nèi)在關(guān)聯(lián)。Pearson相關(guān)系數(shù)只能描述變量間的線性關(guān)系,但是橋梁的荷載響應(yīng)的復雜程度遠遠超過線性關(guān)聯(lián);最大信息系數(shù)因為噪聲的存在使其難以發(fā)揮在相關(guān)性探索中的優(yōu)勢,而拉索間的散點圖卻呈現(xiàn)出“寬帶”的特點;相比而言,基于核密度估計的互信息系數(shù)更能探索拉索群間的非線性關(guān)系,因此選取互信息系數(shù)作為拉索群空間相關(guān)性建模的依據(jù);其次利用鍵能算法(BEA)對拉索群相關(guān)系數(shù)矩陣進行聚類分析,并根據(jù)測點在聚類關(guān)聯(lián)度中的排列順序進行傳感器測點分類和最優(yōu)測點選擇。以南京長江三橋上游84根拉索為研究對象,以0.05為間距,討論了相關(guān)性閾值取0.9~0.6時上游拉索群傳感器優(yōu)化布置方案。當閾值取0.9時,有接近1/2的拉索被選擇為監(jiān)測對象,并且最優(yōu)測點的個數(shù)隨著相關(guān)性閾值的減少而逐漸減少,優(yōu)化結(jié)果論證了該方法的有效性;最后提出了基于粒子群算法的核極限學習機模型(PSO_KELM)以實現(xiàn)利用有限的監(jiān)測拉索對未監(jiān)測處拉索索力變化的估計。從預測精度、誤差分布等角度對比分析了不同的激活函數(shù)和核函數(shù)下的極限學習機模型(ELM)、多元線性回歸模型(MLR)和自適應(yīng)回歸樣條模型(MARS)在全橋索力反演預測中的性能,發(fā)現(xiàn)具有RBF核函數(shù)的RBF_KELM模型具有更高的預測精度和泛化能力,索力預測最大均方根為2.79,并且絕對誤差落在[-3,3]區(qū)間內(nèi)的概率為99.54%,預測精度滿足實際工程的需要;文中還利用MARS模型對基于空間相關(guān)性的傳感器優(yōu)化方法進行驗證,驗證了選取互信息系數(shù)為相關(guān)性指標的合理性和優(yōu)化方法的有效性。
[Abstract]:As an important load-bearing structure in cable-system bridges, cable has become an important factor in the safety evaluation of bridge structures. Cable force monitoring has become an indispensable monitoring project in the health monitoring system of long-span cable-stayed bridges and suspension bridges. A reasonable cable monitoring scheme, especially the location of cable force monitoring greatly affects the accuracy of bridge structure safety assessment, and is also the key to the construction of economical and economical health monitoring system. Based on the spatial correlation of load response of cable group, a sensor optimization method based on cable force correlation is proposed in this paper, and the cable force estimation at unmonitored position is realized by using the cable force information at the optimal measuring point. The finite sensor is used to obtain the load response of the whole bridge cable to the maximum extent. Firstly, using B-spline fitting method to extract the trend term of cable force caused by environmental factors as the research object, taking Pearson correlation coefficient, maximum information coefficient (MIC) and mutual information coefficient (MI) as the measurement index of spatial correlation of cable group, the following three coefficients are used to measure the spatial correlation of cable group, such as Pearson correlation coefficient, maximum information coefficient (MIC) and mutual information coefficient (MI). The Pearson correlation coefficient can only describe the linear relationship between variables, but the complexity of load response of bridge is far more than linear correlation. Because of the existence of noise, the maximum information coefficient makes it difficult to play its advantage in the exploration of correlation, but the scattered plot between cables shows the characteristic of "broadband". In contrast, the mutual information coefficient based on kernel density estimation can better explore the nonlinear relationship between cable groups, so the mutual information coefficient is chosen as the basis of spatial correlation modeling of cable groups. Secondly, the correlation coefficient matrix of cable group is clustered by (BEA), and the sensor points are classified and the optimal points are selected according to the arrangement order of the measured points in the cluster correlation degree. Taking 84 cables in the upper reaches of Nanjing Yangtze River third Bridge as the research object and 0.05 as the spacing, the optimal arrangement scheme of the upstream cable group sensors is discussed when the correlation threshold is 0.90. 6. When the threshold is 0.9, nearly 1 / 2 of the cables are selected as the monitoring object, and the number of the optimal measuring points decreases with the decrease of the correlation threshold. The optimization results demonstrate the effectiveness of the method. Finally, a kernel limit learning machine model (PSO_KELM) based on particle swarm optimization algorithm is proposed to estimate the variation of cable forces in unmonitored cables using finite monitoring cables. This paper compares and analyzes the extreme learning machine model (ELM), under different activation function and kernel function from the angles of prediction precision and error distribution. The performance of multivariate linear regression model (MLR) and adaptive regression spline model (MARS) in full-bridge cable force inversion prediction is studied. It is found that the RBF_KELM model with RBF kernel function has higher prediction accuracy and generalization ability. The maximum root mean square (RMS) of cable force prediction is 2.79, and the probability of absolute error falling in the range of [-3] is 99.54. The prediction accuracy meets the needs of practical engineering. The MARS model is also used to validate the sensor optimization method based on spatial correlation, which verifies the rationality of selecting mutual information coefficient as the correlation index and the effectiveness of the optimization method.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U446
本文編號：2311229
[Abstract]:As an important load-bearing structure in cable-system bridges, cable has become an important factor in the safety evaluation of bridge structures. Cable force monitoring has become an indispensable monitoring project in the health monitoring system of long-span cable-stayed bridges and suspension bridges. A reasonable cable monitoring scheme, especially the location of cable force monitoring greatly affects the accuracy of bridge structure safety assessment, and is also the key to the construction of economical and economical health monitoring system. Based on the spatial correlation of load response of cable group, a sensor optimization method based on cable force correlation is proposed in this paper, and the cable force estimation at unmonitored position is realized by using the cable force information at the optimal measuring point. The finite sensor is used to obtain the load response of the whole bridge cable to the maximum extent. Firstly, using B-spline fitting method to extract the trend term of cable force caused by environmental factors as the research object, taking Pearson correlation coefficient, maximum information coefficient (MIC) and mutual information coefficient (MI) as the measurement index of spatial correlation of cable group, the following three coefficients are used to measure the spatial correlation of cable group, such as Pearson correlation coefficient, maximum information coefficient (MIC) and mutual information coefficient (MI). The Pearson correlation coefficient can only describe the linear relationship between variables, but the complexity of load response of bridge is far more than linear correlation. Because of the existence of noise, the maximum information coefficient makes it difficult to play its advantage in the exploration of correlation, but the scattered plot between cables shows the characteristic of "broadband". In contrast, the mutual information coefficient based on kernel density estimation can better explore the nonlinear relationship between cable groups, so the mutual information coefficient is chosen as the basis of spatial correlation modeling of cable groups. Secondly, the correlation coefficient matrix of cable group is clustered by (BEA), and the sensor points are classified and the optimal points are selected according to the arrangement order of the measured points in the cluster correlation degree. Taking 84 cables in the upper reaches of Nanjing Yangtze River third Bridge as the research object and 0.05 as the spacing, the optimal arrangement scheme of the upstream cable group sensors is discussed when the correlation threshold is 0.90. 6. When the threshold is 0.9, nearly 1 / 2 of the cables are selected as the monitoring object, and the number of the optimal measuring points decreases with the decrease of the correlation threshold. The optimization results demonstrate the effectiveness of the method. Finally, a kernel limit learning machine model (PSO_KELM) based on particle swarm optimization algorithm is proposed to estimate the variation of cable forces in unmonitored cables using finite monitoring cables. This paper compares and analyzes the extreme learning machine model (ELM), under different activation function and kernel function from the angles of prediction precision and error distribution. The performance of multivariate linear regression model (MLR) and adaptive regression spline model (MARS) in full-bridge cable force inversion prediction is studied. It is found that the RBF_KELM model with RBF kernel function has higher prediction accuracy and generalization ability. The maximum root mean square (RMS) of cable force prediction is 2.79, and the probability of absolute error falling in the range of [-3] is 99.54. The prediction accuracy meets the needs of practical engineering. The MARS model is also used to validate the sensor optimization method based on spatial correlation, which verifies the rationality of selecting mutual information coefficient as the correlation index and the effectiveness of the optimization method.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U446
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