本文關鍵詞：拱橋在線監(jiān)測系統(tǒng)及預警指標體系研究與實踐　出處：《云南大學》2015年碩士論文　論文類型：學位論文

  更多相關文章： 傳感器布置 結(jié)構溫度效應 神經(jīng)網(wǎng)絡 損傷識別 預警指標

【摘要】：伴隨著全國交通基礎建設的不斷完善,如何保證已建工程的健康運營便成了重中之重。近年來,隨著橋梁損傷評估理論、傳感技術,傳輸技術和計算機技術的發(fā)展,國內(nèi)許多重要的橋梁已實現(xiàn)了SHMS(健康監(jiān)測系統(tǒng))的布設,在橋梁的養(yǎng)護運營中起到了重要作用。 針對不同橋梁健康監(jiān)測重點的不同,其SHMS包含的內(nèi)容也不盡相同,一般情況下橋梁的SHMS包括傳感器的布置、數(shù)據(jù)采集傳輸系統(tǒng)、數(shù)據(jù)處理系統(tǒng)和橋梁預警評估體系。本文針對實際工程項目著重研究了基于危險控制截面的傳感器布置方法、傳輸數(shù)據(jù)的溫度修正、BP神經(jīng)網(wǎng)絡的損傷識別法和分級預警指標限值的設定,主要研究內(nèi)容如下： (1)根據(jù)上承式空腹拱橋的受力特點進行危險性分析,將可靠度較低、受力較大的危險截面位置作為傳感器布置的控制測點位置。然后基于誤差傳遞最小準則和彎曲應變能的方法分別對T梁和主拱圈的測點選擇進行了計算分析。 (2)在傳感器測試原理的基礎上,著重研究了傳感器所測構件在整體結(jié)構中的熱效應系數(shù)β值,在無法完全獲取實時溫度場的條件下,通過不同構件由于溫差作用導致的構件與構件之間相互作用大小來確定β值,并為各個測點處的修正系數(shù)β提出了溫度效應矩陣的計算方法。 (3)由于實際的傳感器數(shù)據(jù)采集系統(tǒng),是一種對車輛通行中橋梁響應數(shù)據(jù)的抽樣檢驗,所以為了接近這種隨機抽樣行為,把移動荷載時程分析數(shù)據(jù)作為神經(jīng)網(wǎng)絡的訓練樣本,進行了BP神經(jīng)網(wǎng)絡橋梁損傷識別的研究。 (4)根據(jù)橋梁當前的技術狀況等級確定其極限承載能力,結(jié)合交通量活載修正系數(shù)設定了橙色和紅色兩級安全預警指標限值。
[Abstract]:With the continuous improvement of the national transportation infrastructure, how to ensure the healthy operation of the built projects has become the top priority. In recent years, with the bridge damage assessment theory, sensing technology. With the development of transmission technology and computer technology, many important bridges in China have been installed in SHMS (Health Monitoring system), which plays an important role in the maintenance and operation of bridges. According to the different emphasis of bridge health monitoring, its SHMS contains different contents. In general, the bridge SHMS includes sensor layout, data acquisition and transmission system. Data processing system and bridge early warning evaluation system. This paper focuses on the sensor layout method based on hazard control section and the temperature correction of transmission data for actual engineering projects. The damage identification method of BP neural network and the setting of the limit value of grading warning index are studied as follows: 1) according to the stress characteristics of the upper bearing hollow arch bridge, the reliability of the bridge is low. The position of dangerous section with large force is used as the control point position of sensor arrangement, and then the selection of measuring points of T beam and main arch ring is calculated and analyzed based on the minimum error transfer criterion and the method of bending strain energy. 2) based on the principle of sensor testing, the thermal effect coefficient 尾 value of the component measured by the sensor in the whole structure is studied emphatically, under the condition that the real time temperature field can not be completely obtained. The value of 尾 is determined by the interaction between component and component caused by different component temperature difference, and the calculation method of temperature effect matrix is put forward for the correction coefficient 尾 of each measuring point. Because the actual sensor data acquisition system is a sampling inspection of bridge response data in vehicle traffic, so to approach this random sampling behavior. Taking the moving load time history analysis data as the training sample of neural network, the research of BP neural network bridge damage identification is carried out. According to the current technical status of the bridge, the ultimate bearing capacity of the bridge is determined, and the orange and red safety warning index limits are set up in combination with the traffic volume live load correction coefficient.
【學位授予單位】：云南大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U448.22;U446

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