本文選題：BP神經(jīng)網(wǎng)絡(luò) + 小波神經(jīng)網(wǎng)絡(luò)�。� 參考：《石家莊鐵道大學(xué)》2015年碩士論文

【摘要】：盾構(gòu)法隧道施工憑借其自動(dòng)化程度高、對(duì)環(huán)境影響小等特點(diǎn)逐漸成為主流的地鐵隧道施工方式。由盾構(gòu)施工引起的地表沉降對(duì)周圍的建筑物、地下管線等影響較大,如不對(duì)其進(jìn)行有效控制,可能會(huì)引起重大的安全事故。本文首先介紹了盾構(gòu)施工地表沉降預(yù)測(cè)在國(guó)內(nèi)外的研究概況,詳細(xì)敘述了土壓平衡盾構(gòu)機(jī)的結(jié)構(gòu)和工作原理,介紹了北京地鐵6號(hào)線二期工程?hào)|小營(yíng)站~東部新城站為工程概況,研究了地表沉降的機(jī)理和發(fā)展歷程。針對(duì)研究對(duì)象,本文選用BP神經(jīng)網(wǎng)絡(luò)和小波神經(jīng)網(wǎng)絡(luò)技術(shù)進(jìn)行研究。在充分考慮盾構(gòu)施工地表沉降的機(jī)理基礎(chǔ)上,選取了對(duì)地表沉降較為敏感的參數(shù),建立神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型。通過對(duì)施工現(xiàn)場(chǎng)獲得了較多的監(jiān)測(cè)數(shù)據(jù)進(jìn)行整理,最終選取一定數(shù)量的實(shí)驗(yàn)和預(yù)測(cè)樣本,并使用已經(jīng)建立的神經(jīng)網(wǎng)絡(luò)模型進(jìn)行沉降預(yù)測(cè)研究。為了達(dá)到較好的參數(shù)優(yōu)化目的,本文首先選用蟻群算法,但優(yōu)化效果易受到蟻群算法收斂速度慢、搜索停滯等缺陷影響,故使用一種收斂速度和穩(wěn)定性都較好的差分進(jìn)化算法與其結(jié)合來(lái)增強(qiáng)算法的性能。通過建立差分進(jìn)化蟻群神經(jīng)網(wǎng)絡(luò)模型,分別對(duì)BP神經(jīng)網(wǎng)絡(luò)和小波神經(jīng)網(wǎng)絡(luò)的初始權(quán)值、閾值(伸縮參數(shù)和平移參數(shù))進(jìn)行優(yōu)化,同時(shí)對(duì)比了差分進(jìn)化蟻群算法相對(duì)于基本蟻群算法在收斂速度和求解精度方面的優(yōu)勢(shì)。論文在最后使用優(yōu)化后的BP神經(jīng)網(wǎng)絡(luò)和小波神經(jīng)網(wǎng)絡(luò)分別進(jìn)行沉降預(yù)測(cè),對(duì)比了兩種模型在收斂速度和預(yù)測(cè)精度等方面的差異。結(jié)果顯示,兩種模型的預(yù)測(cè)結(jié)果均在工程實(shí)際允許范圍內(nèi),具有較高的參考價(jià)值。在實(shí)際應(yīng)用中,應(yīng)針對(duì)各自特點(diǎn),根據(jù)工程具體情況進(jìn)行選取。論文的研究工作對(duì)隧道盾構(gòu)施工的質(zhì)量和進(jìn)度提高均具有較高的應(yīng)用價(jià)值。
[Abstract]:Due to its high degree of automation and small impact on the environment, shield tunneling has gradually become the mainstream subway tunnel construction method. The ground subsidence caused by shield tunneling has a great influence on the surrounding buildings and underground pipelines. If it is not controlled effectively, it may cause serious safety accidents. This paper first introduces the research situation of ground subsidence prediction in shield construction at home and abroad, and describes the structure and working principle of earth pressure balance shield machine in detail. This paper introduces the general situation of Dongxiaoying station ~ east Xincheng station in the second phase of Beijing Metro Line 6, and studies the mechanism and development course of surface subsidence. In this paper, BP neural network and wavelet neural network are selected for research. On the basis of fully considering the mechanism of ground subsidence in shield construction, the more sensitive parameters for surface settlement are selected, and the neural network prediction model is established. More monitoring data were obtained from the construction site, and a certain number of experimental and prediction samples were selected, and the established neural network model was used to predict the settlement. In order to achieve a better goal of parameter optimization, ant colony algorithm is first selected in this paper, but the optimization effect is easily affected by the slow convergence speed of ant colony algorithm, the stagnation of search and so on. Therefore, a differential evolutionary algorithm with good convergence rate and stability is combined to enhance the performance of the algorithm. By establishing a differential evolution ant colony neural network model, the initial weights, thresholds (telescopic parameters and translation parameters) of BP neural network and wavelet neural network are optimized, respectively. At the same time, the difference evolution ant colony algorithm is compared with the basic ant colony algorithm in terms of convergence speed and accuracy. Finally, the optimized BP neural network and wavelet neural network are used to predict the settlement, and the differences of convergence speed and prediction accuracy between the two models are compared. The results show that the predicted results of the two models are within the scope of engineering practice and have a high reference value. In the practical application, according to their characteristics, according to the specific conditions of the project to select. The research work of this paper has high application value to improve the quality and progress of shield tunneling construction.
【學(xué)位授予單位】：石家莊鐵道大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U455.43

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本文編號(hào)：1923582