【摘要】：隨著城市建設(shè)的日益密集化和地下空間的深度化,如何保證基坑施工過程中周邊環(huán)境和構(gòu)筑物的安全成為工程的重點(diǎn)和難點(diǎn)�；庸こ绦枰獙⒅ёo(hù)、降水、挖土、監(jiān)測(cè)以及信息化施工等作為一個(gè)系統(tǒng)來研究,因此必須對(duì)基坑開挖過程中周邊構(gòu)筑物的沉降進(jìn)行監(jiān)測(cè)、預(yù)測(cè)和控制,保證在基坑施工中基坑和周邊環(huán)境的安全可靠。本文根據(jù)上海前灘地區(qū)基坑和周邊環(huán)境的特點(diǎn),通過對(duì)理論和預(yù)測(cè)方法的研究,結(jié)合現(xiàn)場(chǎng)監(jiān)測(cè)數(shù)據(jù)計(jì)算并預(yù)測(cè)基坑周邊地面沉降,研究基坑開挖對(duì)周邊層的沉降影響。在此基礎(chǔ)上,采用神經(jīng)網(wǎng)絡(luò)模型預(yù)測(cè)基坑開挖造成的周邊路面沉降,主要研究內(nèi)容如下:1、基坑開挖引起周邊地面沉降不可避免,論文對(duì)基坑周邊地面沉降理論及誘因進(jìn)行了闡述,沉降預(yù)測(cè)分析方法分為基于土力學(xué)的理論計(jì)算方法和基于實(shí)測(cè)數(shù)據(jù)的預(yù)測(cè)模型,相對(duì)于理論的公式計(jì)算,基于實(shí)測(cè)靜態(tài)和動(dòng)態(tài)擬合預(yù)測(cè)法的適用性更好,詳細(xì)分析了靜態(tài)擬合曲線法、灰色理論預(yù)測(cè)模型以及神經(jīng)網(wǎng)絡(luò)模型的計(jì)算理論和國內(nèi)外研究現(xiàn)狀。2、根據(jù)上海前灘地區(qū)基坑的相關(guān)資料,對(duì)基坑周邊地面沉降監(jiān)測(cè)方案和監(jiān)測(cè)數(shù)據(jù)進(jìn)行整理,分析具有代表性的監(jiān)測(cè)點(diǎn)的變形規(guī)律,在實(shí)測(cè)基礎(chǔ)上,利用雙曲線擬合法、星野擬合法和灰色系統(tǒng)理論GM(1,1)建立預(yù)測(cè)模型,分析討論三種預(yù)測(cè)值與實(shí)測(cè)值之間的擬合預(yù)測(cè)精度。3、分別采用基于粒子群算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)和廣義回歸神經(jīng)網(wǎng)絡(luò)模型,分別采用基于時(shí)間序列的方式進(jìn)行對(duì)于分析和預(yù)測(cè)下一階段的開挖造成的基坑周邊地面沉降變形,采用粒子群優(yōu)化的BP模型進(jìn)行基于變形影響因素的預(yù)測(cè)模型研究,并對(duì)算法進(jìn)行優(yōu)化設(shè)計(jì)分析。預(yù)測(cè)結(jié)果表明,三種神經(jīng)網(wǎng)絡(luò)模型預(yù)測(cè)結(jié)果的誤差在合理范圍之內(nèi),表明神經(jīng)網(wǎng)絡(luò)模型可以很好的預(yù)測(cè)基坑地面變形情況。保證基坑周邊構(gòu)筑物在基坑開挖過程中以及開挖后續(xù)一段時(shí)間內(nèi)處于安全狀態(tài)。
[Abstract]:With the increasing density of urban construction and the depth of underground space, how to ensure the safety of surrounding environment and structure in the process of foundation pit construction has become an important and difficult point. The foundation pit engineering needs to study the support, dewatering, excavation, monitoring and information construction as a system, so it is necessary to monitor, predict and control the settlement of the surrounding structures in the excavation process of foundation pit. To ensure the safety and reliability of the foundation pit and surrounding environment in the foundation pit construction. According to the characteristics of foundation pit and surrounding environment in Qiantan area of Shanghai, this paper studies the influence of excavation on the settlement of surrounding layer by studying the theory and forecasting method, and combining with the field monitoring data to calculate and predict the ground settlement around the foundation pit. On this basis, the neural network model is used to predict the settlement of the surrounding pavement caused by the excavation of foundation pit. The main research contents are as follows: 1, the settlement of the surrounding ground caused by the excavation of the foundation pit is inevitable. The theory and inducement of ground subsidence around foundation pit are expounded in this paper. The settlement prediction and analysis methods are divided into theoretical calculation method based on soil mechanics and prediction model based on measured data, which is relative to the formula calculation of theory. Based on the better applicability of static and dynamic fitting prediction methods, the calculation theory of static fitting curve method, grey theory prediction model and neural network model are analyzed in detail. According to the relevant data of foundation pit in Qiantan area of Shanghai, the monitoring scheme and monitoring data of ground subsidence around the foundation pit are arranged, and the deformation law of representative monitoring points is analyzed. On the basis of actual measurement, hyperbolic fitting method is used. The field fitting method and grey system theory GM (1 / 1) are used to establish the prediction model, and the fitting accuracy between the three predicted values and the measured values is analyzed and discussed. The BP neural network and generalized regression neural network model based on particle swarm optimization are used to analyze and predict the ground settlement deformation caused by excavation in the next stage. The BP model of particle swarm optimization is used to study the prediction model based on the influence factors of deformation, and the optimization design of the algorithm is analyzed. The prediction results show that the error of the prediction results of the three neural network models is within a reasonable range, which indicates that the neural network model can well predict the ground deformation of foundation pit. It is ensured that the surrounding structure of foundation pit is in safe condition during the excavation process and in the following period of excavation.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P642.26

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