本文選題：巖體工程 + 輸水隧洞��； 參考：《東華理工大學》2017年碩士論文

【摘要】：隨著工程建設的發(fā)展,越來越多的重大工程在深部和強烈構造活動區(qū)興建,給工程的安全建設提出了重大挑戰(zhàn)。由于初始地應力是分析圍巖的穩(wěn)定性并實現工程開挖設計和科學決策的重要條件。因此,準確測量和反演初始地應力是進行工程災害風險評估、災害預警以及可靠防控的前提和關鍵。針對目前地應力反演方法中的反演精度問題,本文以蘭州水源地建設工程為工程背景,基于工程現場初始地應力實測資料,提出了一種結合多元線性回歸方法和PSO-BP神經網絡方法的巖體初始地應力場反演的新方法。本文的主要研究工作如下:(1)對初始地應力的研究背景進行了介紹,并歸納總結了國內外地應力測量方法和技術的適用性及發(fā)展趨勢;闡述了目前各種地應力場反演分析方法及各自的優(yōu)缺點,同時并介紹了初始地應力場反演的基本原理。(2)介紹了蘭州水源地工程區(qū)域的工程概況與地質條件,并基于現場地應力的實測數據,使用多元線性回歸、非線性回歸等反分析方法對蘭州水源地工程區(qū)域的初始地應力場進行反演分析。(3)提出了基于線性回歸的PSO-BP神經網絡反演新方法,即基于多元線性回歸地應力反演結果重新構造樣本數據,以此開展基于PSO-BP神經網絡模型的地應力反演,使用該方法對蘭州市水源地建設工程區(qū)域的初始地應力場進行反演分析。同時,為驗證新方法的合理性,采用工程經驗類比法構造了經驗樣本數據,并進行了初始地應力的神經網絡反演。(4)將上述四種地應力場反演方法得到的工程區(qū)域初始地應力計算值分別與實測值對比分析可得到:相對其他方法,本文所提新方法不論在地應力量值還是方向性方面均能與實測地應力數據很好吻合,尤其在地應力方向性方面具備更好的準確性。本文提出的基于線性回歸方法的PSO-BP神經網絡新方法在初始地應力場反演中能夠考慮多種地質構造作用的荷載或位移作用模式和自重的影響,避免現有方法中構造樣本時因為經驗不足而導致的錯誤,此外,通過回歸與回代過程,避免了試算法受人為因素影響和隨意性較大的缺點并有效地保證工程區(qū)域初始地應力場的準確反演。本文研究成果可以為相似工程的初始地應力場反演提供重要參考。
[Abstract]:With the development of engineering construction, more and more important projects are built in deep and strong tectonic active areas, which brings great challenges to the safety construction of engineering. The initial in-situ stress is an important condition for analyzing the stability of surrounding rock and realizing engineering excavation design and scientific decision. Therefore, accurate measurement and inversion of initial in-situ stress is the prerequisite and key for engineering disaster risk assessment, disaster early warning and reliable prevention and control. Aiming at the problem of inversion accuracy in the present inversion method of in-situ stress, this paper takes Lanzhou water source construction project as the engineering background, and based on the measured data of the initial in-situ stress in the engineering site. A new method for inversion of initial geostress field of rock mass by combining multiple linear regression method and PSO-BP neural network method is proposed. The main research work of this paper is as follows: (1) the research background of initial in-situ stress is introduced, and the applicability and development trend of in-situ stress measurement methods and techniques at home and abroad are summarized. This paper expounds various methods of inversion and analysis of in-situ stress field and their advantages and disadvantages, and introduces the basic principle of inversion of initial in-situ stress field.) it also introduces the engineering situation and geological conditions of Lanzhou water source engineering area. Based on the measured data of in-situ stress, multivariate linear regression is used. A new inversion method of PSO-BP neural network based on linear regression is proposed for inversion analysis of initial in-situ stress field in Lanzhou water source engineering area by nonlinear regression and other inverse analysis methods. In other words, the sample data are reconstructed based on the results of multiple linear regression geostress inversion, and the in-situ stress inversion based on PSO-BP neural network model is carried out, and the initial in-situ stress field of the water source construction project in Lanzhou City is analyzed by using this method. At the same time, in order to verify the rationality of the new method, the empirical sample data are constructed by using the engineering empirical analogy method. The neural network inversion of initial in-situ stress is also carried out. (4) the calculated values of the initial in-situ stress obtained by the four methods mentioned above are compared with the measured values, and the results are as follows: compared with other methods, The new method proposed in this paper is in good agreement with the measured in-situ stress data in terms of both the in-situ stress value and the directionality, especially in the directionality of the in-situ stress. In this paper, a new PSO-BP neural network method based on linear regression method is proposed, which can take into account the influence of load or displacement modes and deadweight of various geological structures in the inversion of initial geostress field. To avoid errors caused by inexperience in constructing samples in existing methods, in addition, through regression and retrogeneration processes, The disadvantages of artificial factors and randomness of the trial algorithm are avoided and the accurate inversion of the initial geostress field in the engineering area is effectively ensured. The results of this paper can provide an important reference for the inversion of initial geostress field of similar engineering.
【學位授予單位】：東華理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TV223

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