本文選題：邊坡 + 破壞機理��； 參考：《中國礦業(yè)大學》2015年碩士論文

【摘要】：邊坡穩(wěn)定性問題作為巖土工程研究領域中的重要課題,廣泛涉及到公路、鐵路、水利工程、建筑基坑、煤礦等基礎建設中。邊坡穩(wěn)定性問題主要研究:邊坡穩(wěn)定性的預測與加固效果預測。通過對邊坡穩(wěn)定性的預測與加固預測,可以快速、直觀地判斷出邊坡的狀態(tài)。邊坡加固效果模擬可以近似仿真現場加固效果,低成本獲得較優(yōu)的邊坡加固方案,從而為邊坡加固方案的現場實施提供一定的科學參考。本文基于理論分析和數值模擬,系統(tǒng)分析了考慮滲流場作用下高邊坡的穩(wěn)定性與加固效果,主要完成了以下幾個方面的研究工作:(1)系統(tǒng)概述了巖土屈服準則和巖土微觀破壞機理、邊坡破壞的判別準則以及邊坡強度折減法等基本理論。(2)研究了邊坡在自重作用下的變形、塑性區(qū)、安全系數等特征,并且進一步研究了邊坡在滲流場、應力場耦合作用情況下的穩(wěn)定性和破壞規(guī)律。為了簡化預測系統(tǒng),詳細對比了滲流場作用下和代替重度法的邊坡穩(wěn)定性差異,為建立有效的邊坡穩(wěn)定性預測系統(tǒng)提供了理論基礎。(3)基于神經網絡理論,對比了幾個訓練函數的準確性,并且確定了幾個影響因素(重度、內聚力、內摩擦角、坡角、坡高等)作為輸入層,建立了5*12*1的神經網絡預測高邊坡穩(wěn)定性的診斷平臺。通過大量的邊坡數值模擬結果作為訓練樣本,并且驗證了神經網絡的準確性;并在以上基礎上,對診斷平臺進行深入研究,建立了邊坡加固效果神經網絡預測系統(tǒng),確定了多個影響因素(重度、內聚力、內摩擦角、坡角、坡高、加固前安全系數、土釘長度、土釘間隔、土釘角度、網噴厚度等)作為輸入層,建立10*16*1的神經網絡診斷平臺,將大量規(guī)范中的加固方案作為訓練樣本,并且驗證其準確性。(4)結合上述神經網絡診斷平臺,先對徐濟高速公路邊坡進行穩(wěn)定性預測,然后通過邊坡加固神經網絡預測制定加固方案,最后通過Flac3D模擬加固方案,詳細了解邊坡受力狀態(tài)并對預測結果驗證。
[Abstract]:As an important subject in the field of geotechnical engineering, slope stability is widely involved in the infrastructure construction of highway, railway, water conservancy engineering, building foundation pit, coal mine and so on. Slope stability is mainly studied: slope stability prediction and reinforcement effect prediction. Through the prediction of slope stability and reinforcement prediction, the state of slope can be judged quickly and intuitively. The simulation of slope reinforcement effect can approximate the field reinforcement effect, and obtain the better slope reinforcement scheme at low cost, thus providing a certain scientific reference for the field implementation of the slope reinforcement scheme. Based on theoretical analysis and numerical simulation, the stability and reinforcement effect of high slope considering seepage field are systematically analyzed in this paper. The main work of this paper is as follows: (1) A systematic overview of the yield criterion of rock and soil and the mechanism of microcosmic failure of rock and soil, the criterion of slope failure and the basic theory of slope strength reduction. (2) the deformation of slope under the action of self-gravity is studied. The characteristics of plastic zone and safety factor, and the stability and failure law of slope under the coupling of seepage field and stress field are further studied. In order to simplify the prediction system, the difference of slope stability under seepage field and in place of severe method is compared in detail, which provides a theoretical basis for the establishment of an effective slope stability prediction system based on neural network theory. The accuracy of several training functions is compared, and several influencing factors (heavy, cohesion, angle of internal friction, angle of slope, height of slope, etc.) are determined as input layers, and a diagnostic platform for predicting the stability of high slope by neural network is established. Through a large number of slope numerical simulation results as training samples, and verify the accuracy of neural networks, and on the basis of the above, the diagnosis platform is studied, and the slope reinforcement effect neural network prediction system is established. Several factors (heavy, cohesive, internal friction angle, slope angle, slope height, safety factor before reinforcement, soil nail length, soil nailing interval, soil nail angle, net spray thickness, etc.) were determined as the input layer to establish a neural network diagnostic platform of 10 / 16 / 1. Taking a large number of reinforcement schemes in the code as training samples and verifying its accuracy, combined with the above neural network diagnostic platform, the slope stability of Xuji Expressway is forecasted. Then the reinforcement scheme is established through the prediction of the slope reinforcement neural network. Finally, the stress state of the slope is understood in detail and the prediction results are verified by the Flac3D simulation reinforcement scheme.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TU43;TP183

【參考文獻】

相關期刊論文 前8條

1 胡伍生;;變形分析的神經網絡技術應用實例[J];測繪工程;2008年03期

2 吳洪詞;回采巷道圍巖穩(wěn)定性的神經網絡控制[J];煤炭工程師;1998年01期

3 王媛;多孔介質滲流與應力的耦合計算方法[J];工程勘察;1995年02期

4 丁向東,吳繼敏,李健,劉成君;巖爆分類的人工神經網絡預測方法[J];河海大學學報(自然科學版);2003年04期

5 張玉祥;巖土工程時間序列預報問題初探[J];巖石力學與工程學報;1998年05期

6 田東方;劉德富;王世梅;陳勇;肖詩榮;;土質邊坡非飽和滲流場與應力場耦合數值分析[J];巖土力學;2009年03期

7 胡輝;;基于FLAC~(3D)強度折減理論的邊坡穩(wěn)定性分析[J];公路與汽運;2012年04期

8 夏明;趙崇斌;;簇平行黏結模型中微觀參數對宏觀參數影響的量綱研究[J];巖石力學與工程學報;2014年02期

，

本文編號：1795609