本文關鍵詞： 智能神經(jīng)網(wǎng)絡 變形預測 BP神經(jīng)網(wǎng)絡群 組合模型 隧道安全評估　出處：《西南交通大學》2015年碩士論文　論文類型：學位論文

【摘要】：變形監(jiān)測包括對變形體的變形現(xiàn)象進行持續(xù)觀測、對變形體變形性態(tài)的分析和對變形體的發(fā)展態(tài)勢進行預測及安全評估等內容。隨著信息化測繪的發(fā)展,變形監(jiān)測觀測手段由傳統(tǒng)的人工測量向基于衛(wèi)星定位、多傳感器、互聯(lián)網(wǎng)、移動通信等多項技術融合的物聯(lián)網(wǎng)模式的現(xiàn)代化監(jiān)測方式轉變,如何在多源海量監(jiān)測數(shù)據(jù)下對工程的安全狀態(tài)進行預測評估是目前面臨的最大挑戰(zhàn)。特別是受經(jīng)濟條件及技術水平所限而修建的鹽水溝隧道,其運營安全關系到整個西氣東輸重點工程的順利進行,由于隧址環(huán)境惡劣、地質條件復雜并且已存在多處安全隱患,其安全嚴峻狀況理應得到足夠重視。然而,目前我國仍沒有一部系統(tǒng)化的隧道安全評價行業(yè)規(guī)范,在實際中只能形成簡單框架型的評價思路,可操作性十分有限。因此,科學地研究隧道變形預測與安全評估顯得尤為重要。文章以新疆西氣東輸管道——鹽水溝隧道變形監(jiān)測工程為依托,以變形監(jiān)測理論為基礎,以數(shù)據(jù)分析為核心,借助人工神經(jīng)網(wǎng)絡與模型組合的優(yōu)勢,研究智能神經(jīng)網(wǎng)絡在運營期隧道變形預測分析與安全評估中的應用,對鹽水溝隧道變形監(jiān)測期間得到的大量監(jiān)測數(shù)據(jù)進行了分析評估,保證隧道的運營安全。在論文研究過程中,筆者深入研究了各環(huán)節(jié)的關鍵技術,主要工作如下：1、針對標準BP算法的缺陷,研究動量-自適應學習速率算法、L-M算法及遺傳算法對標準BP的改進,將三種改進方法與標準BP算法進行仿真實驗對比分析,結果表明改進算法在收斂速度與擬合精度兩方面均顯著優(yōu)于標準BP算法。2、將組合模型分成串聯(lián)型、并聯(lián)型與混聯(lián)型三種組合方式,設計BP神經(jīng)網(wǎng)絡組合器,將三種改進模型與組合器以混聯(lián)方式構建一種最優(yōu)的智能神經(jīng)網(wǎng)絡模型—雙重BP神經(jīng)網(wǎng)絡。實例分析表明,智能神經(jīng)網(wǎng)絡組合模型能有效提高單項模型的擬合精度。3、結合物聯(lián)網(wǎng)模式下的多傳感器融合隧道變形監(jiān)測工程特點與難點,從基于時間序列和基于隧道變形影響因素兩方面研究智能神經(jīng)網(wǎng)絡在隧道運營期變形預測中的應用,解決微空間灰色系統(tǒng)多關聯(lián)因素下高精度隧道變形預測問題。4、研究隧道安全評價多層次指標體系構建及指標度量方法,設計五級評價集,用智能神經(jīng)網(wǎng)絡學習專家知識,對隧道運營安全評價指標進行逐級遞歸式的綜合評判,根據(jù)評判結果確定運營隧道監(jiān)測斷面的安全等級。
[Abstract]:Deformation monitoring includes continuous observation of deformation phenomena of deformable bodies, analysis of deformability of deformable bodies, prediction and safety evaluation of deformability of deformable bodies, and so on. Deformation monitoring and observation means have changed from traditional manual measurement to modern monitoring mode of Internet of things based on satellite positioning, multi-sensor, Internet, mobile communication and so on. How to predict and evaluate the safety state of the project under the condition of multi-source massive monitoring data is the biggest challenge at present, especially the salt ditch tunnel, which is built due to the limitation of economic conditions and technical level. Its operation safety is related to the smooth progress of the whole key project of gas transmission from the west to the east. Due to the harsh environment of the tunnel site, the complex geological conditions and the existence of many potential safety risks, the serious security situation of the tunnel should be paid enough attention to. At present, there is still no systematic industry standard for tunnel safety evaluation in our country. In practice, it can only form a simple frame type evaluation thought, and its maneuverability is very limited. It is very important to study the tunnel deformation prediction and safety assessment scientifically. This paper bases on the deformation monitoring project of Xinjiang West to East Gas Pipeline-Salt Water Channel Tunnel, based on the deformation monitoring theory, and takes the data analysis as the core. With the advantage of combination of artificial neural network and model, the application of intelligent neural network in tunnel deformation prediction and safety assessment during operation period is studied. A large number of monitoring data obtained during monitoring of tunnel deformation in saline ditch are analyzed and evaluated. In the research process of this paper, the key technologies of each link are deeply studied. The main work is as follows: 1, aiming at the defects of the standard BP algorithm, This paper studies the improvement of the standard BP by the momentum adaptive learning rate algorithm and genetic algorithm. The three improved methods and the standard BP algorithm are compared with each other in the simulation experiment. The results show that the improved algorithm is superior to the standard BP algorithm in terms of convergence speed and fitting accuracy. The combined model is divided into three types: series type, parallel type and hybrid type, and BP neural network combiner is designed. An optimal intelligent neural network model, double BP neural network, is constructed by mixing the three improved models and combiners. Intelligent neural network combination model can effectively improve the fitting accuracy of single model. Combining with the characteristics and difficulties of multi-sensor fusion tunnel deformation monitoring engineering under the mode of Internet of things, intelligent neural network combination model can effectively improve the fitting accuracy of single model. The application of intelligent neural network in tunnel deformation prediction is studied based on time series and influence factors of tunnel deformation. To solve the problem of high-precision tunnel deformation prediction under multi-correlation factors in micro-space grey system, to study the multi-level index system construction and index measurement method of tunnel safety evaluation, to design a five-level evaluation set, and to use intelligent neural network to learn expert knowledge. The safety evaluation index of tunnel operation is evaluated by recursive method, and the safety grade of monitoring section is determined according to the result of evaluation.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U456.3;TP183

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