本文關鍵詞： 盾構 土壓平衡 LS-SVM 預測 PSO　出處：《大連理工大學》2015年碩士論文　論文類型：學位論文

【摘要】：世界經(jīng)濟的快速發(fā)展促進了城市化建設的大規(guī)模進行,然而隨著建筑的日益密集以及地面空間的愈加有限,人們不得不將目光轉(zhuǎn)向地下空間,因此,如今對地下空間的開發(fā)進入了一個前所未有的階段。土壓平衡式盾構機是一種挖掘地下隧道的專用大型施工設備,現(xiàn)已廣泛應用于城市地鐵隧道、資源開發(fā)、市政建設、海底隧道等地下工程建設,它以安全可靠、掘進速度快、勞動強度低以及對地層影響小等顯著優(yōu)點,逐漸成為大型地下工程施工的重要手段。盾構施工時開挖面不穩(wěn)定易引起地表塌陷或隆起的災難性事故。由于密封艙土壓的變化情況與開挖面壓力密切相關,因此精確預測及控制密封艙土壓是有效預防開挖面壓力失衡的關鍵技術之一。由于盾構施工是一個極其復雜的過程,工況突變和地質(zhì)狀況異常時有發(fā)生,而各掘進參數(shù)之間存在著強耦合性,難以建立精確的數(shù)學模型,目前的盾構掘進施工主要依靠人工經(jīng)驗操作完成,土壓控制精度難以保證,災難性事故很難從根本上避免。所以采用智能建模方法建立土壓預測控制模型,對于實現(xiàn)掘進過程的智能控制,保證掘進施工安全,有著重要的理論和現(xiàn)實意義。為了建立土壓預測模型,首先從機理上對土壓與掘進參數(shù)的關系,特別是刀盤扭矩與土壓的關系,做了詳細分析,以此為基礎確定了密封艙土壓模型新的輸入輸出參數(shù)。然后,以密封艙承壓隔板上有四個土壓傳感器的典型土壓平衡盾構機為例,提出了基于最小二乘徑向基核函數(shù)支持向量機的密封艙土壓預測模型的智能建模方法,并采用粒子群優(yōu)化算法對最小二乘徑向基核函數(shù)支持向量機的兩個參數(shù)尋優(yōu)。最后,利用所提出的預測模型,以密封艙內(nèi)土壓預測值與設定值總體絕對偏差最小為優(yōu)化指標,采用粒子群算法對影響密封艙土壓變化的兩個控制參數(shù)——螺旋輸送機轉(zhuǎn)速和推進速度進行在線優(yōu)化,實現(xiàn)密封艙土壓平衡的最優(yōu)控制。結合現(xiàn)場施工數(shù)據(jù)對所提出的建模和優(yōu)化控制方法進行了仿真對比分析,驗證了模型的有效性和準確性,為實現(xiàn)密封艙土壓的準確預測和自動控制,保證掘進施工過程安全提供了技術支持。
[Abstract]:The rapid development of the world economy has promoted the large-scale construction of urbanization. However, with the increasing density of buildings and the increasingly limited space on the ground, people have to focus on underground space, so. At present, the development of underground space has entered an unprecedented stage. Earth pressure balance shield machine is a kind of special underground tunnel construction equipment, which has been widely used in urban subway tunnel, resource development. Municipal construction, subsea tunnel and other underground engineering construction, it is safe and reliable, fast tunneling speed, low labor intensity and small impact on the formation and other prominent advantages. It has gradually become an important means of large underground engineering construction. The unstable excavation surface during shield construction is liable to cause catastrophic accidents of surface collapse or uplift. The variation of soil pressure in sealed chamber is closely related to excavating surface pressure. Therefore, accurate prediction and control of sealed chamber earth pressure is one of the key technologies to effectively prevent excavating surface pressure imbalance. Because shield construction is an extremely complex process, sudden changes in working conditions and abnormal geological conditions occur from time to time. However, there is strong coupling among the tunneling parameters, so it is difficult to establish accurate mathematical model. The current shield tunneling construction mainly depends on manual experience operation, and the precision of soil pressure control is difficult to guarantee. It is difficult to avoid the catastrophic accident fundamentally, so the intelligent modeling method is used to establish the predictive control model of earth pressure, which can realize the intelligent control of the tunneling process and ensure the safety of the tunneling construction. It has important theoretical and practical significance. In order to establish the prediction model of soil pressure, the relationship between soil pressure and tunneling parameters, especially the relationship between cutter head torque and soil pressure, is analyzed in detail. On this basis, the new input and output parameters of the earth pressure model of the sealed tank are determined. Then, the typical earth pressure balance shield machine with four earth pressure sensors on the pressure partition board of the sealed chamber is taken as an example. An intelligent modeling method for soil pressure prediction model of sealed cabin based on least square radial basis function kernel support vector machine is presented. Particle swarm optimization algorithm is used to optimize the two parameters of least squares radial basis function support vector machine. Finally, the proposed prediction model is used. The minimum absolute deviation between the soil pressure prediction value and the set value in the sealed cabin is taken as the optimization index. The particle swarm optimization (PSO) algorithm is used to optimize the speed and propulsion speed of the spiral conveyor, which affects the change of soil pressure of the sealed cabin. The optimal control of soil pressure balance in sealed cabin is realized. The simulation and contrast analysis of the proposed modeling and optimization control methods are carried out in combination with the field construction data, and the validity and accuracy of the model are verified. It provides technical support for accurate prediction and automatic control of soil pressure in sealed cabin and for the safety of tunneling construction process.
【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U455.39

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相關期刊論文 前1條

1 何川;封坤;方勇;;盾構法修建地鐵隧道的技術現(xiàn)狀與展望[J];西南交通大學學報;2015年01期

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本文編號：1459785