双连拱隧道围岩变形有限元与BP神经网络耦合分析

以浙江金(华)-丽(水)-温(州)高速公路永嘉鹿城段的红枫双连拱隧道为工程背景,建立了基于神经网络的连拱隧道围岩变形预测模型.利用施工过程中左洞量测的围岩变形进行反分析,确定出围岩的力学特性参数,再通过数值分析方法对隧道右洞围岩变形和稳定性做出合理的评价和符合实际的预测,从而更有效地对支护参数和施工方法进行反馈设计.通过预测值与实际监测值的比较可以看出,其预测精度满足岩土工程的要求,具有一定的工程意义.     

Support system suggestion based on back-analysis results case study: Babolak water conveyance tunnel

Determining in-situ soil or rock geotechnical properties is a difficult task for a design engineer. Back analysis is a helpful technique for evaluating soil property by considering and measuring the convergence of an underground opening. Back analysis was performed by matching numerical modeling results with the measured tunnel convergence. The main purpose of this study was to determine optimum rock mass properties using back analysis in order to suggest the best and most economical support system. Accordingly, the difference between measured and calculated convergence values was minimized by using an error function (objective function). In this paper, through the parameters obtained from back analysis, a support system based on a set consisting of shotcrete, wire mesh, and lattice girder was suggested for the Babolak water conveyance tunnel in Mazandaran, Iran. Therefore, the suggested design was based on decreasing shotcrete thickness from 25 to 20?cm and eliminating of rock bolts.     

隧洞工程弹性参数反演的可辨识性及量测优化布置探讨

探讨依据完备的量测信息开展弹性参数反演的可辨识性及量测的优化布置,对于指导岩土工程反演实践具有重要的实用价值。利用平面弹性复变函数方法和一般弹性理论,推导出任意形状隧洞的位移、应力解析表达式,基于隧洞围岩位移、应力的解析解,依据参数可辨识条件和量测优化布置的最灵敏原则,探讨了地下隧洞工程弹性反演参数的可辨识性及测点优化布置,并通过算例进行了验证。研究表明,依据完备的量测信息开展弹性反分析,只要测点布置方式适当、反演参数满足相应的限定条件,可同时惟一辨识出5个参数P、Q、M、E、μ。参数反演的可辨识性受隧洞形状复杂性的影响,随着隧洞复杂性的提高,参数可同时惟一辨识需满足的限制条件大为减少,对测点布置方式的要求也会降低。工程实践中,为避免量测数据的少量误差导致反演计算结果的大幅度波动,测点(线)应根据最灵敏原则优化选择各反演参数的灵敏系数最大的测点(线)组合进行量测布置。     

Inverse Analysis of Deep Excavation Using Differential Evolution Algorithm

This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer code, named Python‐based DE, is developed and incorporated into the commercial finite element software ABAQUS, with a parallel computing technique to run an FE analysis for all trail vectors of one generation in DE in multiple cores of a cluster, which dramatically reduces the computational time. A synthetic case and a well‐instrumented real case, that is, the Taipei National Enterprise Center (TNEC) project, are used to demonstrate the capability of the proposed back‐analysis procedure. Results show that multiple soil parameters are well identified by back analysis using a DE optimization algorithm for highly nonlinear problems. For the synthetic excavation case, the back‐analyzed parameters are basically identical to the input parameters that are used to generate synthetic response of wall deflection. For the TNEC case with a total of nine parameters to be back analyzed, the relative errors of wall deflection for the last three stages are 2.2, 1.1, and 1.0%, respectively. Robustness of the back‐estimated parameters is further illustrated by a forward prediction. The wall deflection in the subsequent stages can be satisfactorily predicted using the back‐analyzed soil parameters at early stages. Copyright © 2014 John Wiley & Sons, Ltd.     

位移反分析的粒子群优化-高斯过程协同优化方法

针对采用随机全局优化技术进行岩土工程位移反分析存在数值计算量大、效率低的问题,将粒子群优化算法与高斯过程机器学习技术相结合,提出了位移反分析的粒子群优化-高斯过程协同优化方法。该方法利用全局寻优性能优异的粒子群优化算法进行寻优的基础上,采用高斯过程机器学习模型不断地总结历史经验,预测包含全局最优解的最有前景区域,通过提高粒子群搜索效率并降低适应度评价次数,进而有效地降低位移反分析过程中的数值计算工作量。多种测试函数的数学验证和工程算例的研究结果表明该方法是可行的,与传统方法相比较,可显著地降低位移反分析的计算耗时。     

双向八车道连拱隧道围岩力学参数反演分析

在采用双侧壁导坑法施工的双向八车道特大断面连拱隧道中,施工步繁多,临时支护设置的时间长,隧道全断面的变形量测只能在临时支护拆除后进行。由于全断面变形数据获取得较晚,故较难将其用于围岩力学参数的反演。将有限元计算和BP神经网络技术相结合,并在有限元计算过程中考虑实际的施工步,建立起所有临时支护拆除之前这一施工状态下导坑的变形量与围岩力学参数之间的非线性映射关系,并通过对应状态下实测的导坑变形值反演了围岩的力学参数。将反演的结果用于正分析验算,验证了该方法是可行的。     

基于Excel数据表和极限分析法的滑坡抗剪强度参数反演分析

针对采用传统的确定性边坡稳定性分析方法进行滑裂面抗剪强度参数反演分析所需滑坡断面不少于两个的不足,基于概率可靠度理论,提出了一种结合Excel数据表和极限分析上限法的滑坡强度参数反演新方法。依托湘西朱雀洞特大滑坡进行算例分析,研究结果表明：结合Excel数据表法和极限分析上限法反演得到的滑裂面抗剪强度参数与工程现场实测数据吻合良好,验证了新分析方法的正确性和有效性;运用该法反演分析只需一个滑坡断面,可有效解决单一滑裂面抗剪强度参数反演问题,减少了计算工作量,具有广泛的适用性和良好的工程应用前景。     

A new displacement back analysis to identify mechanical geo‐material parameters based on hybrid intelligent methodology

Displacement back analysis is a common method to identify mechanical geo‐material parameters using the monitored displacement. How to obtain a global optimum solution in large space search of highly non‐linear multimodal is a key point of optimum back analysis. The paper presents a new back analysis that is an integration of evolutionary support vector machines (SVMs), numerical analysis and genetic algorithm. The non‐linear relationship between the mechanical geo‐material parameters to be identified and the corresponding displacement values of key points is learned and represented by evolutionary SVMs in global optimum. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, performing a global optimum search on the obtained SVMs using genetic algorithm can identify the mechanical geo‐material parameters. The proposed algorithm is tested by back analysis of an elastic plate and an elastic–plastic plate and used to recognize mechanical parameters of subclay, strongly weathered tuff and weakly weathered tuff of Bachimen slope, Funing expressway, Fujian, China. The results indicate that applicability of the proposed algorithm with enough accuracy. Copyright © 2004 John Wiley & Sons, Ltd.     

Application of extended Kalman filter to back analysis of the natural stress state accounting for measuring uncertainties

In this work, we present a numerical procedure for determining the nature stress state in the rock mass around a tunnel. A finite element method is applied for analyzing the direct problems of tunneling during the back analysis of parameter estimation, in which a no‐tension elastic–plastic model is used to simulate the elastic–tensile and elastic–plastic‐tensile failure states which often occur in the cases of underground excavation in heavily jointed rock masses. By considering the natural stress state as random parameters of the tunneling system, the Kalman filter method is employed for feedback analysis to modify the parameter values in a statistical context, which uses the prior information in the process of estimation and employs a set of displacements obtained from field measurements. To verify the effectiveness of the proposed method of inverse analysis, the developed numerical procedure is applied to a synthetic example of deep tunnels in yielding rock masses. The relative importance of the a priori and updating information is investigated, as is the importance of their uncertainty. The results show great potential of the proposed approach. Copyright © 2010 John Wiley & Sons, Ltd.     

隧道围岩稳定性正算反演分析研究——以厦门海底隧道穿越风化深槽施工安全监控为例介绍

为了降低海底隧道施工风险,确保隧道施工顺利穿越海底几处风化深槽和风化囊区域,解决难以获得隧道围岩力学参数的技术难题,采用位移反分析方法建立了动态反演预测模型;作为比较,还简单介绍了弹塑性反演的一种全局优化方法。根据隧道典型断面实际监控量测的围岩拱顶沉降量和周边收敛位移量,结合先行服务隧道揭露的水文地质情况,进行优化反演分析,得到该类围岩初期支护后的等效弹性模量和等效侧压力系数。在相应的同类地质条件下,对后续将开挖的左、右主洞围岩采用边界元法进行正演数值计算,使之能为主洞施工方案比选以及支护设计参数调整与修正提供定量依据,做到信息化动态设计与施工。工程实例分析表明,利用正算反演分析法得出的围岩等效力学参数是可靠的,可据此对类似地质条件下主隧道围岩进行正演计算分析,预测主洞围岩的变形破坏模式,判断其围岩稳定性。位移反分析法是隧道施工变形理论预测分析与工程实际相联系的有效平台,为工程设计施工技术决策提供了一种切实有效的途径。     

PSO-LSSVM模型在位移反分析中的应用

提出了一种基于均匀设计原理、最小二乘支持向量机(LSSVM)和粒子群优化算法(PSO)的快速位移反分析方法。该方法利用均匀设计和有限差分法获得学习样本,再用粒子群算法搜索最优的最小二乘支持向量机模型参数。并用最小二乘支持向量机回归模型建立反演参数与监测点位移值之间的非线性映射关系,最后用粒子群算法从全局空间上搜索与实测位移最吻合的反演参数。该反演模型利用了粒子群算法高效简单、均匀设计构造高质量小样本以及最小二乘支持向量机的小样本、泛化性能好的特点。将该模型应用于龙滩水电站左岸地下厂房区岩体地应力场的反演分析中,计算结果与实测的位移值和地应力值均吻合较好,说明了该模型在岩土工程快速反演分析中具有良好的应用价值。     

隧洞围岩收敛损失位移的求取方法及应用

隧洞围岩变形监测断面设置与开挖施工工序上的先后导致了损失位移问题,它对于判断围岩的变形状态、评价其稳定性具有重要的意义。复杂多变的围岩条件使基于收敛曲线的累积位移和增量位移参数反演方法的应用变得非常谨慎。针对这些问题,通过对一致性问题的讨论后认为,虽然变形演化规律上的一致性尚存在问题,但三维计算和现场围岩的总位移之间的一致性是可以保证的。以此问题为前提,以Hoek经验公式的通式为基础,依据规范规定,基于现场监测收敛位移曲线,建立了损失位移的求取步骤,并将其在锦屏II级水电站辅助洞A、B围岩变形规律分析中进行了应用,得出了两个监测断面各测线的最终监测位移收敛值、损失位移、总收敛位移和相对收敛值等,拟合而得的全收敛曲线与监测曲线相吻合,能够很好地描述现场围岩随空间效应变化的演化规律,为围岩稳定性评价和基于全位移的参数反演分析奠定了基础。     

Updating the models and uncertainty of mechanical parameters for rock tunnels using Bayesian inference

Rock mechanical parameters and their uncertainties are critical to rock stability analysis, engineering design, and safe construction in rock mechanics and engineering. The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass, but this does not consider the uncertainty. This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data, then integrating the monitored data, prior knowledge of geotechnical parameters,and a mechanical model of a rock tunnel using Markov chain Monte Carlo(MCMC) simulation. The proposed approach is illustrated by a circular tunnel with an analytical solution, which was then applied to an experimental tunnel in Goupitan Hydropower Station, China. The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables. The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements. It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically. Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data. Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.     

基于遗传－广义回归神经元算法的坞石隧道三维弹塑性位移反分析研究

广义回归神经元网络在逼近能力、学习速度和网络稳定性方面均优于BP神经元网络,且具有网络人为调节参数少的优点。本文将广义回归神经元网络引入坞石隧道工程的三维弹塑性位移反分析。为了在网络训练过程中快速搜索到最优的网络阈值,采用十进制遗传算法对网络阈值进行优化。在确定最优的网络结构后,采用遗传算法在每个待反演参数的搜索范围内搜索出与实测位移最接近的围岩力学与初始应力场参数组合。用反分析得来的参数进行下步开挖位移预测,预测值与实测值吻合较好,表明所提出的这种反分析方法在工程上是可行的,可以推广使用。     

利用旁压试验的砂土初始状态反演分析

讨论了砂土旁压试验的反分析问题。建议利用旁压试验实测数据反演确定土体的初始状态而非材料参数,材料参数可以通过实验室常规试验确定。主要利用了新型的亚塑性本构模型参数不受土体状态影响的特点。主要内容包括：建立有限元数值模型,利用文献中的试验数据确定了特定砂土的材料参数;通过模拟砂土的实验室旁压试验,验证了数值模型;通过改变土体初始应力状态和相对密实度进行数值计算,并利用数值计算成果,建立了可适用于砂土初始状态反演分析的实测旁压荷载和土体中应力与相对密度的数学关系。     

A hybrid Bayesian back‐propagation neural network approach to multivariate modelling

There is growing interest in the use of back‐propagation neural networks to model non‐linear multivariate problems in geotehnical engineering. To overcome the shortcomings of the conventional back‐propagation neural network, such as overfitting, where the neural network learns the spurious details and noise in the training examples, a hybrid back‐propagation algorithm has been developed. The method utilizes the genetic algorithms search technique and the Bayesian neural network methodology. The genetic algorithms enhance the stochastic search to locate the global minima for the neural network model. The Bayesian inference procedures essentially provide better generalization and a statistical approach to deal with data uncertainty in comparison with the conventional back‐propagation. The uncertainty of data can be indicated using error bars. Two examples are presented to demonstrate the convergence and generalization capabilities of this hybrid algorithm. Copyright © 2003 John Wiley & Sons, Ltd.     

基于速度分析的探地雷达阻抗介电常数反演

阻抗反演是利用波阻抗与介电常数关系开展地下介质参数估计的重要技术,在探地雷达以及叠后地震资料解释中具有广泛的应用。常规阻抗反演需要钻孔或测井曲线作为约束项,约束项信息直接影响阻抗反演的估计精度。在缺少钻孔数据的实际应用中,如何开展探地雷达阻抗反演是该方法研究的重要内容之一。基于上述问题,本文提出了基于速度分析的探地雷达阻抗反演方法。其基本思想是基于多偏移距雷达数据开展速度谱分析和Dix反演,以获得不同深度的速度信息作为阻抗反演的约束项;同时,采用K-means方法自动拾取速度谱信息,大大降低了常规人工拾取误差,提高了计算效率。通过典型随机土壤介质模型,验证了本文方法在无钻孔条件下仍然可以获得较好的介电常数估计结果,并测试噪声适应能力强。最后通过美国密歇根州Wurtsmith AFB,in Oscoda区域的探地雷达数据测试了本文提出方法在探地雷达实测数据参数估计中具有较好的应用效果。     

现场量测位移反分析法及在地下工程中的应用

现场量测位移的分析法是通过测量开挖硐室、隧道、井巷等地下工程的周边和围岩内的相对位移和绝对位移值来反推岩体的原始地应力场及岩体参力参数，利用线弹性有限元位移反析程序计算出某石棉矿区的初始地应力场值，并得出圈套的水平应力，这正是该矿地压严重显现原因之一。     

Statistical inverse analysis based on genetic algorithm and principal component analysis: Applications to excavation problems and pressuremeter tests

This study concerns the identification of constitutive models from geotechnical measurements by inverse analysis. Soil parameters are identified from measured horizontal displacements of sheet pile walls and from a measured pressuremeter curve. An optimization method based on a genetic algorithm (GA) and a principal component analysis (PCA), developed and tested on synthetic data in a previous paper, is applied. These applications show that the conclusions deduced from synthetic problems can be extrapolated to real problems. The GA is a robust optimization method that is able to deal with the non‐uniqueness of the solution in identifying a set of solutions for a given uncertainty on the measurements. This set is then characterized by a PCA that gives a first‐order approximation of the solution as an ellipsoid. When the solution set is not too curved in the research space, this ellipsoid characterizes the soil properties considering the measured data and the tolerate margins for the response of the numerical model. Besides, optimizations from different measurements provide solution sets with a common area in the research space. This intersection gives a more relevant and accurate identification of parameters. Finally, we show that these identified parameters permit to reproduce geotechnical measurements not used in the identification process. Copyright © 2009 John Wiley & Sons, Ltd.     

Homogenization framework for three‐dimensional elastoplastic finite element analysis of a grouted pipe‐roofing reinforcement method for tunnelling

This paper deals with the grouted pipe‐roofing reinforcement method that is used in the construction of tunnels through weak grounds. This system consists on installing, prior to the excavation of a length of tunnel, an array of pipes forming a kind of ‘umbrella’ above the area to be excavated. In some cases, these pipes are later used to inject grout to strengthen the ground and ‘connect’ the pipes. This system has proven to be very efficient in reducing tunnel convergence and water inflow when tunnelling through weak grounds. However, due to the geometrical and mechanical complexity of the problem, existing finite element frameworks are inappropriate to simulate tunnelling using this method. In this paper, a mathematical framework based on a homogenization technique to simulate ‘grouted pipe‐roofing reinforced ground’ and its implementation into a 3‐D finite element programme that can consider stage construction situations are presented. The constitutive model developed allows considering the main design parameters of the problem and only requires geometrical and mechanical properties of the constituents. Additionally, the use of a homogenization approach implies that the generation of the finite element mesh can be easily produced and that re‐meshing is not required as basic geometrical parameters such as the orientation of the pipes are changed. The model developed is used to simulate tunnelling with the grouted pipe‐roofing reinforcement method. From the analyses, the effects of the main design parameters on the elastic and the elastoplastic analyses are considered. Copyright © 2004 John Wiley & Sons, Ltd.