【摘要】：隨著我國城鎮(zhèn)化建設的深入推進,涌現(xiàn)出大量高層與超高層建筑物,這些建筑物對地基基礎的要求也頗為嚴格。其中,地基基礎中的深基坑就是他們的典型代表。建筑物或構筑物在使用過程中會產(chǎn)生一定的變形與沉降,這對建筑物使用而言非常不利。因此,對深基坑進行科學的評估與形變預測對建筑物安全使用具有重要現(xiàn)實意義。本文采用基因表達編程算法作為研究方法,利用其超強的發(fā)現(xiàn)能力和獨特的算法優(yōu)勢,并對該算法進行進一步的改進,使其更加符合實際應用的要求,主要研究工作包括:首先,闡述了深基坑變形監(jiān)測和基因表達式編程的國內(nèi)外研究現(xiàn)狀及背景意義;其次,對傳統(tǒng)基因表達式編程算法的基本原理和實際應用中存在的缺陷進行分析;再次,利用云模型理論的基本思想并根據(jù)種群適應度的大小利用X正態(tài)云發(fā)生器選取遺傳控制參數(shù)來改進傳統(tǒng)的基因表達式模型;最后,以深基坑兩個監(jiān)測點的前20期觀測數(shù)據(jù)作為訓練樣本,利用改進前后的預測模型對深基坑后5期形變數(shù)據(jù)進行預測并對其數(shù)據(jù)精度及空間分布影響進行分析。通過實例分析與比較可得知改進后的模型較傳統(tǒng)的模型在預測精度上提高了一倍多,證明改進后的基因表達式模型在收斂的速度和預測精度方面都有所提高,從而體現(xiàn)該改進模型在深基坑變形預測領域的研究價值。
[Abstract]:With the further development of urbanization in China, a large number of high-rise and super-tall buildings have emerged, and the requirements of these buildings for foundation are quite strict. Among them, deep foundation pit in foundation is their typical representative. Buildings or structures in the process of use will produce certain deformation and settlement, which is very disadvantageous to the use of buildings. Therefore, the scientific evaluation and deformation prediction of deep foundation pit are of great practical significance to the safe use of buildings. In this paper, the gene expression programming algorithm is used as the research method, and its super discovery ability and unique algorithm advantages are utilized, and the algorithm is further improved to make it more suitable for practical application. The main research work includes: firstly, the research status and background significance of deep foundation pit deformation monitoring and gene expression programming at home and abroad are expounded. Secondly, the basic principle of the traditional gene expression programming algorithm and the defects in its practical application are analyzed. Thirdly, using the basic idea of cloud model theory and the size of population fitness, the genetic control parameters are selected by X normal cloud generator to improve the traditional gene expression model. Finally, using the first 20 observation data of two monitoring points of deep foundation pit as the training sample, the prediction model before and after the improvement is used to predict the deformation data of the later five periods of deep foundation pit, and the accuracy of the data and the influence of spatial distribution are analyzed. Through the analysis and comparison of examples, it can be found that the improved model is more than twice as accurate as the traditional one. It is proved that the improved gene expression model has improved both the convergence speed and the prediction accuracy. The research value of the improved model in the field of deep foundation pit deformation prediction is demonstrated.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TU433

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