發(fā)布時間：2018-06-13 19:40

  本文選題：深基坑 + 變型預(yù)測　； 參考：《江西理工大學(xué)》2013年碩士論文

【摘要】：隨著城市化進程的快速發(fā)展，多層及高層建筑的建造及大量地下空間的開發(fā)，大量的深基坑工程不斷涌現(xiàn)。深基坑變形監(jiān)測與預(yù)測是深基坑設(shè)計施工中的一個重要的環(huán)節(jié)，也是基坑工程領(lǐng)域研究的熱點問題之一。準(zhǔn)確地預(yù)測深基坑未來的變形，是深基坑變形監(jiān)測的最終目的。針對傳統(tǒng)常用預(yù)測方法存在一定的局限性，結(jié)合支持向量機的研究現(xiàn)狀，提出將能夠有效地解決小樣本、非線性、高維數(shù)、局部極小等問題的支持向量機模型應(yīng)用于深基坑變形預(yù)測。 首先，闡述了深基坑變形預(yù)測的意義，對深基坑變形預(yù)測研究現(xiàn)狀作了全面的闡述，，分析了深基坑施工過程中的變形，提出了預(yù)測誤差最小法來確定樣本集的嵌入維數(shù)以及時間延遲，實現(xiàn)對樣本數(shù)據(jù)的構(gòu)造，針對傳統(tǒng)支持向量機預(yù)測模型參數(shù)難以確定的問題，提出采用粒子群算法，通過種群隨機初始化、適應(yīng)度函數(shù)設(shè)置、粒子更新、終止條件設(shè)置，對支持向量機的相關(guān)參數(shù)進行尋優(yōu)，得到基于粒子群算法的改進支持向量機預(yù)測模型。 其次，結(jié)合深基坑圍護樁樁體兩個不同深度的實例測斜數(shù)據(jù)，根據(jù)預(yù)測誤差最小法求出樣本集的嵌入維數(shù)以及時間延遲，對變形數(shù)據(jù)序列進行坐標(biāo)延遲相空間重構(gòu)，利用相空間域的相點，通過建立的相空間結(jié)構(gòu)得到學(xué)習(xí)樣本；然后，在Matlab7.14平臺上結(jié)合Microsoft Visual C++6.0編譯器，利用libsvm工具箱進行擴展編程實現(xiàn)對傳統(tǒng)支持向量機模型和改進支持向量機模型的訓(xùn)練和預(yù)測。 最后，根據(jù)編制的Matlab程序，將改進支持向量機預(yù)測模型與傳統(tǒng)的支持向量機模型以及Elman動態(tài)神經(jīng)網(wǎng)絡(luò)模型的預(yù)測結(jié)果，采用均方誤差、平方和誤差、平均相對誤差對預(yù)測效果進行評價，得出改進支持向量機預(yù)測模型的均方誤差、平方和誤差、平均相對誤差分別為0.0155和0.0164、0.1550和0.1639、1.2511%和4.2205%。實驗結(jié)果表明，基于改進支持向量機預(yù)測模型的預(yù)測結(jié)果均方誤差、平方和誤差、平均相對誤差均優(yōu)于傳統(tǒng)的支持向量機模型和Elman網(wǎng)絡(luò)模型，通過粒子群算法優(yōu)選支持向量機預(yù)測模型的相關(guān)參數(shù)，能夠得到較好的改進支持向量機預(yù)測模型，且擬合效果、泛化性能、穩(wěn)定性能均更好，具有較高的預(yù)測精度，證明了基于改進支持向量機預(yù)測模型能更好地反映深基坑系統(tǒng)的動態(tài)非線性特點，具有一定的優(yōu)越性與工程應(yīng)用推廣價值。
[Abstract]:With the rapid development of urbanization, the construction of multi-storey and high-rise buildings and the development of a large number of underground space, a large number of deep foundation pit projects continue to emerge. The deformation monitoring and prediction of deep foundation pit is an important link in the design and construction of deep foundation pit, and it is also one of the hot issues in the field of foundation pit engineering. Accurate prediction of the future deformation of deep foundation pit is the ultimate purpose of deep foundation pit deformation monitoring. In view of the limitations of traditional prediction methods and the current research situation of support vector machine, it is proposed that it will be able to solve the problem of small sample, nonlinear and high dimension effectively. The support vector machine (SVM) model for local minima is applied to deep foundation pit deformation prediction. First of all, the significance of deep foundation pit deformation prediction is expounded, the research status of deep foundation pit deformation prediction is comprehensively expounded, and the deformation in deep foundation pit construction process is analyzed. The minimum prediction error method is proposed to determine the embedded dimension and time delay of the sample set, and the construction of the sample data is realized. Aiming at the difficulty of determining the parameters of the traditional SVM prediction model, the particle swarm optimization (PSO) algorithm is proposed. Through population random initialization, fitness function setting, particle update, termination condition setting, the parameters of support vector machine are optimized, and an improved support vector machine prediction model based on particle swarm optimization algorithm is obtained. Secondly, the embedding dimension and time delay of the sample set are calculated according to the prediction error minimization method, and the coordinate delay phase space reconstruction of the deformation data sequence is carried out. Using the phase points of the phase space domain, the learning samples are obtained through the phase space structure established, and then, the Microsoft Visual C 6.0 compiler is combined with the Matlab 7.14 platform. The traditional support vector machine model and the improved support vector machine model are trained and predicted by extended programming with libsvm toolbox. Finally, according to the Matlab program, the prediction results of the improved support vector machine model, the traditional support vector machine model and the Elman dynamic neural network model are presented, and the mean square error and square sum error are adopted. The mean relative error of the improved support vector machine prediction model is estimated. The mean square error and square sum error are 0.0155 and 0.01640.1550 and 0.16391.2511% and 4.2205 respectively. The experimental results show that the mean square error, square sum error and average relative error of the prediction results based on the improved support vector machine prediction model are better than those of the traditional support vector machine model and the Elman network model. The particle swarm optimization algorithm is used to optimize the parameters of the prediction model of support vector machine (SVM), and the prediction model of SVM can be improved, and the fitting effect, generalization performance and stability performance are better, and the prediction accuracy is higher. It is proved that the prediction model based on improved support vector machine can better reflect the dynamic nonlinear characteristics of deep foundation pit system, and has certain superiority and application value in engineering application.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TU196.1;TU753

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本文編號：2015182