【摘要】：建立合理的健康監(jiān)測(cè)系統(tǒng),發(fā)現(xiàn)橋梁結(jié)構(gòu)的早期結(jié)構(gòu)整體性能變化對(duì)避免安全事故與過(guò)大的經(jīng)濟(jì)損失的發(fā)生具有重大意義。本文通過(guò)建立連續(xù)動(dòng)態(tài)健康監(jiān)測(cè)系統(tǒng),獲取橋梁結(jié)構(gòu)的動(dòng)態(tài)響應(yīng)信號(hào),基于自動(dòng)模態(tài)參數(shù)提取方法獲取大量動(dòng)態(tài)參數(shù),進(jìn)而建立動(dòng)態(tài)參數(shù)與環(huán)境因素的統(tǒng)計(jì)識(shí)別模型,去除環(huán)境因素對(duì)動(dòng)態(tài)參數(shù)的影響,提取只對(duì)結(jié)構(gòu)整體變化敏感的指標(biāo),識(shí)別結(jié)構(gòu)整體性能變化。為了獲取大量準(zhǔn)確的模態(tài)參數(shù),本文對(duì)連續(xù)動(dòng)態(tài)數(shù)據(jù)運(yùn)用協(xié)方差驅(qū)動(dòng)的隨機(jī)子空間方法(SSI-COV)基本理論和模糊聚類算法,得到不含虛假模態(tài)的穩(wěn)定圖,實(shí)現(xiàn)自動(dòng)模態(tài)參數(shù)提取。通過(guò)運(yùn)用多重線性回歸方法,對(duì)大量的動(dòng)態(tài)參數(shù)與相應(yīng)環(huán)境因素建立統(tǒng)計(jì)模型。對(duì)最優(yōu)統(tǒng)計(jì)學(xué)模型計(jì)算得到的殘差矩陣進(jìn)行離群值分析,得到只對(duì)結(jié)構(gòu)性能變化敏感的新穎度指標(biāo),識(shí)別結(jié)構(gòu)整體性能變化。本文選用適用于National Instrument的Lab VIEW軟件環(huán)境,開(kāi)發(fā)數(shù)據(jù)連續(xù)采集和自動(dòng)模態(tài)參數(shù)提取程序。數(shù)據(jù)采集程序通過(guò)模塊化的編譯方式實(shí)現(xiàn)數(shù)據(jù)的連續(xù)采集、數(shù)據(jù)預(yù)處理和自動(dòng)定時(shí)存儲(chǔ)功能。自動(dòng)模態(tài)參數(shù)提取程序運(yùn)用SSI-COV理論獲取穩(wěn)定圖,通過(guò)去除穩(wěn)定圖中的虛假模態(tài),實(shí)現(xiàn)自動(dòng)模態(tài)參數(shù)提取。通過(guò)濱州黃河大橋模型9 d的連續(xù)自動(dòng)采集與識(shí)別,檢驗(yàn)了自動(dòng)采集程序和自動(dòng)模態(tài)參數(shù)提取程序的適用性�；谏钲诖笊澈訕虻臄�(shù)值模擬結(jié)果與模態(tài)實(shí)驗(yàn)結(jié)果,建立連續(xù)動(dòng)態(tài)監(jiān)測(cè)系統(tǒng);通過(guò)42 d的自動(dòng)連續(xù)采集與自動(dòng)模態(tài)分析,得出施工階段大沙河模態(tài)頻率的變化,并對(duì)42 d結(jié)構(gòu)頻率與溫度數(shù)據(jù)進(jìn)行相關(guān)性分析,識(shí)別出施工階段結(jié)構(gòu)性能變化。運(yùn)用葡萄牙Pedro e Inês橋的5年監(jiān)測(cè)數(shù)據(jù)識(shí)別運(yùn)營(yíng)階段結(jié)構(gòu)性能變化。運(yùn)用多重線性回歸的方法建立第1年溫度與結(jié)構(gòu)頻率之間的非線性關(guān)系。以此作為基準(zhǔn)模型,去除環(huán)境因素對(duì)結(jié)構(gòu)頻率的影響,提取只對(duì)結(jié)構(gòu)性能變化敏感的指標(biāo),得到5年內(nèi)反映結(jié)構(gòu)性能變化的新穎度指標(biāo)的變化,識(shí)別結(jié)構(gòu)早期性能變化。
[Abstract]:By establishing a reasonable health monitoring system, it is found that the change of the whole performance of the bridge structure in the early stage is of great significance to avoid the occurrence of safety accidents and excessive economic losses. In this paper, a continuous dynamic health monitoring system is established to obtain the dynamic response signals of the bridge structure, and a large number of dynamic parameters are obtained based on the automatic modal parameter extraction method, and then the statistical identification model of the dynamic parameters and environmental factors is established. Removing the influence of environmental factors on the dynamic parameters, extracting the index which is sensitive to the overall change of the structure, and identifying the overall performance change of the structure. In order to obtain a large number of accurate modal parameters, the basic theory of covariance-driven stochastic subspace method (SSI-COV) and fuzzy clustering algorithm are applied to the continuous dynamic data in this paper, and the stability graph with no false modal is obtained, and the automatic modal parameter extraction is realized. By using multiple linear regression method, a statistical model is established for a large number of dynamic parameters and corresponding environmental factors. The outlier value of the residual matrix calculated by the optimal statistical model is analyzed and a novel index which is only sensitive to the structural performance change is obtained to identify the overall performance change of the structure. In this paper, a Lab VIEW software environment suitable for National Instrument is used to develop a program for continuous data acquisition and automatic modal parameter extraction. The data acquisition program realizes continuous data acquisition, data preprocessing and automatic timing storage through modular compilation. The automatic modal parameter extraction program uses SSI-COV theory to obtain the stability diagram. By removing the false modal in the stability diagram, the automatic modal parameter extraction is realized. Through continuous automatic acquisition and identification of the model of Binzhou Yellow River Bridge for 9 days, the applicability of the automatic acquisition program and the automatic modal parameter extraction program are tested. Based on the numerical simulation results and modal experiment results of Dasha River Bridge in Shenzhen, a continuous dynamic monitoring system is established, and the modal frequency variation of Dasha River in construction stage is obtained by 42 days of automatic continuous acquisition and automatic modal analysis. The correlation analysis of 42 d structure frequency and temperature data is carried out to identify the structural performance changes in construction stage. The 5-year monitoring data of Pedro e In 錨 s bridge in Portugal are used to identify the structural performance changes in operation phase. The nonlinear relationship between the temperature of the first year and the frequency of the structure is established by the method of multiple linear regression. The model is used as a benchmark to remove the influence of environmental factors on the structural frequency and to extract the index which is only sensitive to the structural performance change. The variation of the novelty index reflecting the structural performance change within 5 years can be obtained and the early structural performance change can be identified.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U446

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