【摘要】：21世紀(jì),我國城市路面資源的利用趨近于飽和,各城市大量修建地鐵以減緩路面交通的運輸量。而由于地鐵的埋深較淺,對土體的開挖擾動較敏感,在開挖過程中造成地表沉降,更嚴(yán)重可能會形成地面塌陷,周邊原有建筑物也會受到地鐵近接施工的影響從而發(fā)生沉降,在新建地鐵隧道的同時又要保證原有建筑物的穩(wěn)定性是實際施工時不可忽視的。而實際工程中,通常根據(jù)隧道圍巖的力學(xué)參數(shù)確定地鐵施工的開挖方式以及支護結(jié)構(gòu)的材料參數(shù),但是巖體是一種非均質(zhì)、非線性、不連續(xù)的材料,如何能夠利用簡單、高效的方式得到準(zhǔn)確的力學(xué)參數(shù)尤為重要,通過獲得圍巖的力學(xué)參數(shù),能準(zhǔn)確預(yù)測并采取相應(yīng)措施控制施工中引起的沉降值,對實際施工具有指導(dǎo)意義。本文結(jié)合大連地鐵凌水一號橋區(qū)間的實際工程,提出基于粒子群的BP神經(jīng)網(wǎng)絡(luò)優(yōu)化算法,利用現(xiàn)場監(jiān)測的數(shù)據(jù)反演得到圍巖的力學(xué)參數(shù),對實際的工程應(yīng)用有一定意義。本文的主要內(nèi)容如下:(1)利用有限元分析軟件ABAQUS分別對采用全斷面開挖法、正臺階法、CD法和CRD法的地鐵隧道施工進行數(shù)值模擬,分析得到四種不同施工方式下地表沉降值、圍巖應(yīng)力場和位移場、錨桿以及襯砌的內(nèi)力變化情況。通過對比分析,結(jié)果表明:采用CRD法施工,隧道的控制效果較好,而全斷面開挖法對隧道穩(wěn)定性的控制相對較差。(2)根據(jù)正交試驗設(shè)計原理,利用極差法和方差法分析各試驗因素對圍巖位移沉降值的影響情況,結(jié)果表明彈性模量相對于其他因素為主要的影響因素。選取巖體物理力學(xué)參數(shù)的取值范圍,構(gòu)造位移反分析學(xué)習(xí)樣本,分別采用BP神經(jīng)網(wǎng)絡(luò)算法和PSO—BP算法進行計算,對比分析試驗值和學(xué)習(xí)值,采用自驗證的方式,得到PSO—BP算法的誤差較小,有較好的預(yù)測能力。(3)結(jié)合凌水一號橋區(qū)間實際的監(jiān)測數(shù)據(jù),采用正交試驗設(shè)計構(gòu)造了PSO—BP算法的學(xué)習(xí)樣本,將反演圍巖的力學(xué)參數(shù)帶入ABAQUS中,考慮近接施工,采用正臺階法模擬地鐵動態(tài)開挖,分析近接施工對圍巖及橋樁穩(wěn)定性的影響,并將地表沉降、拱頂沉降、邊墻凈空收斂值、各樁基的沉降值與模擬值對比,得到最大的誤差為18.4%,說明數(shù)值模擬分析的可靠性。
[Abstract]:In the 21st century, the utilization of urban pavement resources in China is approaching saturation, and a large number of subways are built in various cities to slow down the traffic volume of pavement traffic. However, due to the shallow buried depth of the subway and the sensitivity to the excavation disturbance of the soil, the surface subsidence may be caused by the excavation process, and the ground collapse may be formed, and the original buildings around the subway will also be affected by the adjacent construction of the subway, resulting in settlement. At the same time, the stability of the original building should be guaranteed when the subway tunnel is built, which can not be ignored in the actual construction. In practical engineering, the excavation mode of subway construction and the material parameters of supporting structure are usually determined according to the mechanical parameters of tunnel surrounding rock, but the rock mass is a kind of heterogeneous, nonlinear and discontinuous material, so how to make use of it is simple. It is particularly important to obtain accurate mechanical parameters in an efficient way. By obtaining the mechanical parameters of surrounding rock, we can accurately predict and take corresponding measures to control the settlement value caused by construction, which is of guiding significance to the actual construction. In this paper, based on the actual project of Lingshui No. 1 Bridge in Dalian Metro, a BP neural network optimization algorithm based on particle swarm optimization is proposed. The mechanical parameters of surrounding rock are obtained by inversion of field monitoring data, which is of certain significance for practical engineering application. The main contents of this paper are as follows: (1) the finite element analysis software ABAQUS is used to simulate the subway tunnel construction using full section excavation method, positive step method, CD method and CRD method, respectively. The changes of surface settlement, surrounding rock stress field and displacement field, anchor rod and lining internal force under four different construction modes are analyzed. Through comparative analysis, the results show that the control effect of CRD method is better, while the control effect of full section excavation method on tunnel stability is relatively poor. (2) according to the principle of orthogonal test design, The influence of each test factor on the displacement and settlement value of surrounding rock is analyzed by means of extreme difference method and variance method. The results show that the elastic modulus is the main influencing factor compared with other factors. The range of physical and mechanical parameters of rock mass is selected, and the learning samples of displacement back analysis are constructed. BP neural network algorithm and PSO-BP algorithm are used to calculate the values, and the experimental values and learning values are compared and analyzed, and the self-verification method is adopted. The error of PSO-BP algorithm is small and the prediction ability is good. (3) combined with the actual monitoring data of Lingshui No. 1 bridge interval, the learning sample of PSO-BP algorithm is constructed by orthogonal experiment design. The mechanical parameters of surrounding rock are inversed into ABAQUS, and the dynamic excavation of subway is simulated by forward step method. The influence of adjacent construction on the stability of surrounding rock and bridge pile is analyzed, and the surface settlement, arch roof settlement and side wall clearance convergence value are analyzed. By comparing the settlement value of each pile foundation with the simulated value, the maximum error is 18.4%, which shows the reliability of numerical simulation analysis.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U455;U451.2

【相似文獻】

相關(guān)期刊論文 前10條

1 謝新民;既有橋高錐體近接工程支護型式研究[J];鐵道學(xué)報;2003年04期

2 廖惠生,処文，

本文編號：2484907