本文關(guān)鍵詞： 深基坑工程 灰色GM模型 馬爾可夫鏈 BP神經(jīng)網(wǎng)絡(luò) 組合模型　出處：《武漢理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著我國經(jīng)濟(jì)的高速發(fā)展,基建投資的加大,深基坑工程的安全成了建筑安全的重點,其支護(hù)形式、安全監(jiān)測與變形預(yù)測則就成了巖土工程領(lǐng)域的重要研究課題�；釉陂_挖過程中,必然會引起基坑的變形。然而,如何找到一種科學(xué)準(zhǔn)確的基坑形變預(yù)測方法對工程實踐具有重要意義。 本文以武漢王家墩中央商務(wù)區(qū)地下交通環(huán)廊第一標(biāo)深基坑工程為背景,分析了地下交通環(huán)廊的SMW工法樁、雙軸攪拌樁及高壓旋噴樁支護(hù)的施工方法,分析了地下交通環(huán)廊深基坑的特點。在前人的研究基礎(chǔ)上,分析了灰色系統(tǒng)理論和馬爾可夫理論及BP神經(jīng)網(wǎng)絡(luò)原理,并建立了基于灰色系統(tǒng)、灰色馬爾可夫鏈、BP神經(jīng)網(wǎng)絡(luò)及灰色馬爾可夫-BP神經(jīng)網(wǎng)絡(luò)的變形預(yù)測模型。最后以王家墩中央商務(wù)區(qū)地下交通環(huán)廊第一標(biāo)深基坑為實例進(jìn)行預(yù)測,在MATLAB7.0的工具箱上編譯四種理論方法,通過選擇不同的參數(shù)訓(xùn)練,經(jīng)對比分析說明BP神經(jīng)網(wǎng)絡(luò)模型用于深基坑變形預(yù)測精度較其它模型要高,能很好的預(yù)測未來變形的發(fā)展趨勢,而GM(1,1)模型、灰色馬爾可夫模型及灰色馬爾可夫-BP神經(jīng)網(wǎng)絡(luò)模型在數(shù)據(jù)不多的情況下可以作為一種變形預(yù)測手段。本文得出了以下結(jié)論： 1)由于BP神經(jīng)網(wǎng)絡(luò)自身的容錯能力和自適應(yīng)學(xué)習(xí)能力,本文在地下交通環(huán)廊深基坑監(jiān)測預(yù)測中,其模型的預(yù)測精度高于灰色GM(1,1)模型和灰色馬爾可夫模型。 2)結(jié)合灰色GM(1,1),馬爾可夫鏈、BP神經(jīng)網(wǎng)絡(luò)各自的優(yōu)點,建立了灰色馬爾可夫-BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型。在面對地下交通環(huán)廊深基坑的監(jiān)測預(yù)測中,少量樣本信息也可以獲得較高的精度。 3)利用所建立的模型對武漢王家墩中央商務(wù)區(qū)地下交通環(huán)廊第一標(biāo)深基坑工程在開挖過程進(jìn)行監(jiān)測中,對基坑圍護(hù)結(jié)構(gòu)變形進(jìn)行了預(yù)測,通過與實測值的對比研究,表明本文所建立的預(yù)測模型有較好的適用性。
[Abstract]:With the rapid development of our economy and the increase of capital investment, the safety of deep foundation pit has become the key point of construction safety, and its supporting form. Safety monitoring and deformation prediction have become an important research topic in the field of geotechnical engineering. How to find a scientific and accurate method of foundation pit deformation prediction is of great significance to engineering practice. Based on the first standard deep foundation pit project of the underground traffic ring corridor in Wuhan Wangjiadun central business district, this paper analyzes the construction methods of the SMW pile, the two-axis mixing pile and the high-pressure rotary jet pile supporting the underground traffic ring corridor. The characteristics of deep foundation pit of underground traffic ring corridor are analyzed. On the basis of previous research, grey system theory, Markov theory and BP neural network theory are analyzed, and grey system based on grey system is established. The deformation prediction model of grey Markov chain BP neural network and grey Markov BP neural network is presented. Finally, the first standard deep foundation pit of underground traffic ring corridor in Wangjiadun central business district is taken as an example. Four theoretical methods are compiled on the toolbox of MATLAB7.0. By selecting different parameter training, it is proved that the BP neural network model is more accurate than other models in predicting the deformation of deep foundation pit. It can well predict the future trend of deformation, and GM1 / 1) model. The grey Markov model and the grey Markov BP neural network model can be used as a method to predict the deformation when the data are not much. In this paper, the following conclusions are drawn: 1) because of the fault tolerance and adaptive learning ability of BP neural network, the prediction accuracy of the model is higher than that of grey GM(1 in the monitoring and prediction of deep foundation pit of underground traffic ring-corridor. 1) model and grey Markov model. 2) the advantages of Markov chain BP neural network combined with grey GM1 / 1 / 1 / 1 / 1 / 1 / 1 / 1 / 1, respectively. The grey Markov BP neural network prediction model is established. In the monitoring and prediction of deep foundation pit of underground traffic ring corridor, a small amount of sample information can also obtain high accuracy. 3) using the established model, the deformation of the retaining structure of the foundation pit is forecasted during the excavation process of the first standard deep foundation pit of the underground traffic ring corridor in the central commercial district of Wangjiadun, Wuhan. The comparison with the measured data shows that the proposed prediction model has good applicability.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TU753

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