【摘要】：本文主要較為深入的研究了深基坑工程施工過程中的變形監(jiān)測及預(yù)測。以黑龍江省雞西市萬達(dá)廣場深基坑工程為背景,全面總結(jié)了深基坑監(jiān)測技術(shù),驗證了灰色GM(1,1)模型、神經(jīng)網(wǎng)絡(luò)模型和灰色神經(jīng)網(wǎng)絡(luò)組合模型在深基坑變形預(yù)測中的可靠性與實用性。主要研究內(nèi)容及結(jié)論如下:1.介紹了深基坑變形機(jī)理及其常規(guī)變形監(jiān)測技術(shù),對變形監(jiān)測系統(tǒng)的布設(shè)、監(jiān)測數(shù)據(jù)處理等進(jìn)行了探討,介紹了用于深基坑變形預(yù)測的灰色GM(1,1)模型、BP神經(jīng)網(wǎng)絡(luò)模型、Elman神經(jīng)網(wǎng)絡(luò)模型的基本理論,分析了灰色模型和神經(jīng)網(wǎng)絡(luò)模型的串聯(lián)型組合在深基坑變形預(yù)測中的應(yīng)用。2.灰色GM(1,1)模型能夠?qū)ψ冃伪O(jiān)測數(shù)據(jù)進(jìn)行合格的擬合和預(yù)測,運(yùn)用灰色GM(1,1)模型進(jìn)行預(yù)測時,不宜將預(yù)測的時間段設(shè)計的太長。3.訓(xùn)練后的BP和Elman神經(jīng)網(wǎng)絡(luò)在深基坑的變形預(yù)測中可以達(dá)到較高的擬合精度,完全符合實際工程應(yīng)用的要求。通過對樣本數(shù)據(jù)的預(yù)測結(jié)果進(jìn)行研究分析得出,BP神經(jīng)網(wǎng)絡(luò)模型的預(yù)測精度最高。Elman神經(jīng)網(wǎng)絡(luò)的預(yù)測精度與BP神經(jīng)網(wǎng)絡(luò)接近,與應(yīng)用于其他方面的預(yù)測相比,其并沒有表現(xiàn)出預(yù)測精度方面的優(yōu)越性,BP神經(jīng)網(wǎng)絡(luò)應(yīng)該更適合應(yīng)用于基坑變形預(yù)測方面。4.結(jié)合具體實例研究發(fā)現(xiàn),灰色神經(jīng)網(wǎng)絡(luò)組合模型的預(yù)測精度要高于單一的GM(1,1)模型,這兩個不同模型的相互組合可以使組合模型對其各自所具有的優(yōu)點進(jìn)行發(fā)揮,既可以利用神經(jīng)網(wǎng)絡(luò)的高度非線性,又可以利用累加數(shù)據(jù)的規(guī)律性及灰色模型弱化數(shù)據(jù)的隨機(jī)性。5.通過對本文的幾種模型預(yù)測結(jié)果進(jìn)行對比分析可以得出,GM(1,1)模型、神經(jīng)網(wǎng)絡(luò)模型、灰色神經(jīng)網(wǎng)絡(luò)組合模型都能夠預(yù)測出較為準(zhǔn)確的結(jié)果,能夠有效的指導(dǎo)基坑工程的施工。
[Abstract]:In this paper, the deformation monitoring and prediction in the construction process of deep foundation pit are studied deeply. Based on the deep foundation pit engineering of Wanda Square in Jixi City, Heilongjiang Province, the monitoring technology of deep foundation pit is summarized, and the grey GM (1Q1) model is verified. Reliability and practicability of neural network model and grey neural network combined model in deep foundation pit deformation prediction. The main contents and conclusions are as follows: 1. This paper introduces the deformation mechanism of deep foundation pit and its conventional deformation monitoring technology, and discusses the layout of deformation monitoring system, monitoring data processing and so on. This paper introduces the basic theory of Elman neural network model based on grey GM (1 + 1) model and BP neural network model for deep foundation pit deformation prediction. The application of series combination of grey model and neural network model in deep foundation pit deformation prediction is analyzed. The grey GM (1K1) model can fit and predict the deformation monitoring data. When the grey GM (1K1) model is used to predict the deformation monitoring data, it is not appropriate to design the predicted time period for too long. 3. The trained BP and Elman neural networks can achieve high fitting accuracy in the prediction of deep foundation pit deformation, which fully meet the requirements of practical engineering application. By studying and analyzing the prediction results of sample data, it is concluded that the prediction accuracy of BP neural network model is the highest. Elman neural network is close to BP neural network, and compared with the prediction applied in other aspects. It does not show the superiority of prediction accuracy. BP neural network should be more suitable for foundation pit deformation prediction. It is found that the prediction accuracy of the combined grey neural network model is higher than that of the single GM (1K1) model, and the combination of these two different models can make the combined model give full play to their respective advantages. It can be used not only to make use of the high nonlinearity of the neural network, but also to use the regularity of the accumulated data and the weakening of the randomness of the data by the grey model. By comparing and analyzing the prediction results of several models in this paper, we can conclude that GM (1t1) model, neural network model and grey neural network combination model can all predict more accurate results and can effectively guide the construction of foundation pit engineering.
【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TU196.1

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