岩石本构模型智能辨识方法研究

岩石本构模型是研究岩石力学特征和变形机制的基础,而本构模型或模型中相关参数的识别是本构模型研究中的热点和难点问题。本文基于红板岩室内力学实验数据,分别利用遗传算法、BP神经网络以及遗传规划对红板岩本构模型进行了模式识别,结果表明,遗传算法进行参数识别需要事先假定流变模型的形式,误差较大,而BP神经网络和遗传规划可以一次性同时确定流变模型的结构形式及参数,有效避免模型假定所带来的误差。而遗传规划与BP神经网络相比,具有精度高、收敛快,可视化程度高等特点,为岩石本构参数及模型的智能识别方法的选取提供参考。     

不同加载速率下岩石强度特性的扰动状态概念理论分析

岩石的强度特性受很多因素的影响,加载速率是其中很重要的影响因素之一.利用扰动状态概念理论,建立了非线弹性岩石材料的本构模型.采用RMT-150B岩石力学试验机对砂岩试件进行了四种不同加载速率下的单轴压缩试验,得到了不同加载速率下砂岩的应力-应变全过程曲线,并分析了加载速率对岩石强度特性的影响.通过建立的扰动状态本构模型对岩石的应力-应变关系进行了模拟.相关研究及分析比较表明,该模型与试验结果较为一致.     

基于Drucker-Prager准则的岩石弹塑性损伤本构模型研究

大多数岩石材料软化本构模型在硬化函数中引入塑性内变量来表示材料的硬化/软化性质,但并不能反映岩石微裂隙损伤对材料力学性能的影响及单轴拉伸和压缩所表现的初始屈服强度f0与屈服极限fu的差异。基于D-P准则同时考虑塑性软化及损伤软化,建立岩石类材料的弹塑性本构关系及其数值算法。塑性屈服函数采用Borja等的应力张量的硬化/软化函数,反映塑性内变量及应力状态对硬化函数的影响;由于岩石损伤软化是微裂隙扩展所导致的体积膨胀引起的,因此,提出用体积应变表征岩石损伤变量的演化,并用回映隐式积分算法编制了岩石的弹塑性损伤本构程序。对单轴压缩及拉伸荷载作用下的岩石材料试验进行数值模拟,结果表明,所提出的岩石弹塑性损伤本构模型可以较好地符合岩石材料的力学特性。     

压剪应力条件下板岩细观力学本构模型

为了研究板岩力学性质,采用细观力学方法对板岩建模进行了研究,并建立了板岩损伤本构模型。在模型中增加了对剪胀变形机制和渗流对变形影响的内容,使得模型能够更好地表述板岩的变形特性。所建立模型能够考虑微裂纹闭合、摩擦滑移、自相似扩展等过程。理论与试验对比结果表明本文模型是适用的。     

考虑空间相关尺度特征的细观力学模型及其应用

天然岩石材料内部存在各种缺陷,在微、细观尺度表现出高度的非均质特性。基于连续介质力学框架,采用非线性标量损伤本构关系描述岩石材料的变形与破坏行为,建立了岩石细观损伤本构模型,并在常规Weibull随机分布模型基础上,引入空间相关尺度因子以模拟实际岩石材料各项物理力学指标具有的空间相关特征。选取典型岩石单轴拉伸试验算例,分析随机场内空间相关尺度因子的变化对试样荷载-加口张开度关系曲线以及破坏行为的影响。结果表明,考虑岩石材料各项物理力学指标的空间相关尺度特征对评价其力学指标的离散性以及破坏形态特征有着较显著的影响。     

基于颗粒物质力学与连续介质热力学的岩土本构模型初探

首先简要介绍颗粒物质力学与模拟岩土材料本构特性的热力学方法,其次对力链及其对应的强弱网络的形成、力学特性与能量耗散特点与机制进行深入地分析,随后在Collins提出的土体热力学模型的基础上,考虑强弱网络结构的应力应变特征,引入合理的假设,探讨建立符合热力学原理的宏细观结合的岩土本构模型的思路与步骤     

南宁市防洪堤岸坡岩土材料的本构模型试验研究

根据南宁市防洪堤岸坡岩土材料的特性,对典型堤段的岸坡岩土材料进行了试验研究。根据试验数据拟合得到体积屈服面和剪切屈服面,基于广义塑性力学原理,建立了岸坡岩土材料的本构模型。使用MATLAB程序进行数值计算,通过对比试验曲线和理论曲线的拟合程度检验了模型的合理性。     

非饱和土化学-塑性耦合本构行为的数值模拟

基于Hueckel提出的饱和黏土化学-塑性本构模型和Gallipoli提出的非饱和土弹塑性本构模型,提出了一个新的非饱和多孔介质的化学-塑性本构模型,并建立了该模型的隐式积分算法,算法中考虑了化学软化和非饱和吸力的影响。在已有的非饱和多孔介质有限元分析程序平台上进行了程序研发,对孔隙水中化学污染物浓度变化对非饱和土力学行为的影响进行数值模拟,使所研制的程序能够进行岩土工程问题的化学-力学耦合非线性分析。     

高温岩石的扰动状态本构模型

诸多岩石工程都涉及高温岩石问题,开展高温下岩石的本构关系尤为必要.基于扰动状态概念理论,通过定义与温度相关的扰动函数,建立起高温岩石的扰动状态本构模型.利用高温下实施的花岗岩单轴压缩破坏试验,对建立的本构模型进行了检验.研究表明,所建立的本构模型能描述高温岩石的力学响应特性.     

岩石类材料的能量基率相关弹塑性损伤模型

岩石类材料的动态本构模型研究是岩石动力学理论研究的基础问题。相比于其他的非线性理论,损伤力学理论已证实可成功地模拟岩石类材料的应变软化和渐进破坏等特征,可用于解释其静、动态破坏机制。现有的通过理论推导得出的动态本构中大部分并未考虑损伤因素,而通过维象学试验方法建立起的动态本构则缺乏损伤力学理论基础。为此基于弹性余能等效原理和损伤力学的基本概念,并结合动态试验,推导建立了岩石类材料的率相关弹塑性损伤模型。并通过与文献中的试验资料对比,证明了模型的有效性,为岩石类材料组成的结构的动力响应研究奠定了基础。     

岩土工程可靠度分析的神经网络四阶矩法

针对岩土工程的功能函数强非线性且难以显式表达的特点,提出了基于人工神经网络的四阶矩法,充分利用了基本随机变量的统计信息。首先利用神经网络对结构的隐式功能函数进行拟合,求得基本随机变量在均值点处的功能函数值及其偏导数,然后利用泰勒级数展开的方法由基本随机变量的前四阶矩求得功能函数的前四阶矩,并借助于Pearson系统获得功能函数的更高阶矩。在此基础上,通过最大熵原理确定以功能函数各阶矩为约束的功能函数的概率密度函数,最后由一次积分得到结构的失效概率。通过数值算例和工程实例不同方法的对比分析,表明基于神经网络的结构可靠度分析四阶矩方法是可行的,有效的,能够满足岩土工程可靠度分析的要求。     

基于修正Mohr-Coulomb准则的弹塑性本构模型及其数值实施

针对Mohr-Coulomb准则高估岩土体抗拉性能的局限性,建立考虑最大拉应力准则的修正Mohr-Coulomb模型;系统地论述隐式本构积分算法的主要内容,推导相应的一致性刚度矩阵。以ABAQUS软件为平台,采用向后欧拉隐式应力积分算法编制了UMAT本构程序,对单轴拉伸试验和三轴压缩试验进行数值模拟,对比分析ABAQUS自带模型和自编模型的优劣,结果表明编写的修正Mohr-Coulomb模型能够有效地反映岩土介质的抗拉性能,弥补了ABAQUS自带模型的不足。     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

Classification of slopes and prediction of factor of safety using differential evolution neural networks

Slope stability analysis is one of the most important problems in geotechnical engineering. The development in slope stability analysis has followed the development in computational geotechnical engineering. This paper discusses the application of different recently developed artificial neural network models to slope stability analysis based on the actual slope failure database available in the literature. Different ANN models are developed to classify the slope as stable or unstable (failed) and to predict the factor of safety. The developed ANN model is found to be efficient compared with other methods like support vector machine and genetic programming available in literature. Prediction models are presented based on the developed ANN model parameters. Different sensitivity analyses are made to identify the important input parameters.     

岩土非线性流变本构模型的辨识算法研究

从岩土非线性流变本构模型通式——P.Perzyna方程出发,研制了岩土工程围岩流变本构模型辨识用有限元计算程序EVP2D,并将其和均匀设计(UD)法结合,获得了用于ANN辨识模型训练用的具有丰富本构信息的全局性输入输出有效数据。尔后,设计了基于实际监测位移数据辨识围岩流变本构模型参数的ANN模型,并在matlab软件平台中研制了辨识用相关程序CYJBS3.M。有关实例验证,从待辨识参数向量UD设计、训练数据的有限元计算获取、ANN模型的训练和辨识这一整体计算过程的实现,表明了UD-FEM-ANN本构模型辨识方法的可行性。      

Evaluation of the compression index of soils using an artificial neural network

The compression index is a one of the important soil parameters that is essential to geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming, empirical formulas based on soil parameters can be useful. Over the decades, a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, liquid limit, plasticity index, specific gravity, and others. Each of the existing empirical formulas yields good results for a particular test set, but cannot accurately or reliably predict the compression index from various test sets. In this study, an alternative approach, an artificial neural network (ANN) model, is proposed to estimate the compression index with numerous consolidation test sets. The compression index was modeled as a function of seven variables including the natural water content, liquid limit, plastic index, specific gravity, and soil types. Nine hundred and forty-seven consolidation tests for soils sampled at 67 construction sites in the Republic of Korea were used for the training and testing of the ANN model. The predicted results showed that the neural network could provide a better performance than the empirical formulas.     

广义回归神经网络预测加筋土支挡结构高度

土工合成材料加筋支挡结构（Geosythetics-Reinforced Retaining Wall, 简称GRW）设计方法主要是建立在似粘聚力理论基础之上的半经验设计法。由于土性及加筋机理的复杂性，常常要对它们进行人为假定，导致计算结果差强人意。神经网络方法与传统方法的不同之处在于不需要主观假定，而是模拟人脑思维，通过数据样本的学习来获得预测结果。引入神经网络技术来预测加筋土支挡结构的设计高度是一种新尝试。由于本问题具有样本容量非常有限、影响因素复杂多样的特点。因此，采用适用于稀土样本数据的广义回归网络（General Regression Neural Network)来预测加筋土支挡结构设计高度。基于MATLAB神经网络工具箱及文献[1]的挡墙离心模型试验结果，建立了一个可用于加筋支挡结构设计高度预测的GRNN网络。通过对足尺试验，实际工程及模型试验结果的检验，表明网络的学习是成功的，具有一定指导意义。     

Probabilistic stability analyses of slopes using the ANN-based response surface

Slope stability analysis is a geotechnical engineering problem characterized by many sources of uncertainty. Some of these sources are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for integrating a commercial finite difference method into a probabilistic analysis of slope stability is presented. Given that the limit state function cannot be expressed in an explicit form, an artificial neural network (ANN)-based response surface is adopted to approximate the limit state function, thereby reducing the number of stability analysis calculations. A trained ANN model is used to calculate the probability of failure through the first- and second-order reliability methods and a Monte Carlo simulation technique. Probabilistic stability assessments for a hypothetical two-layer slope as well as for the Cannon Dam in Missouri, USA are performed to verify the application potential of the proposed method.     

Integration of Information Technology into Geotechnical Engineering Practice: Concept and Development

Information Technology (IT) has been extensively used to predict, visualize, and analyze physical parameters in order to expedite routine geotechnical design procedures. This paper presents an example of the combined technique of IT and numerical analysis for routine geotechnical design projects. The proposed approach involves the development of ANN(s) using a calibrated finite element model(s) for use as a prediction tool and implementation of the developed ANN(s) into a GIS platform for visualization and analysis of spatial distribution of predicted results. A novel feature of the proposed approach is an ability to expedite a routine geotechnical design process that otherwise requires significant time and effort in performing numerical analyses for different design scenarios. A knowledge-based underground excavation design system that utilizes artificial neural networks (ANNs) as prediction tools is also introduced. Practical implications of the use of IT in geotechnical design are discussed in great detail.     

Neural network based prediction of ground surface settlements due to tunnelling

Ground surface settlement due to tunnel excavation varies in magnitude and trend depending on several factors such as tunnel geometry, ground conditions, etc. Although there are several empirical and semi-empirical formulae available for predicting ground surface settlement, most of these do not simultaneously take into consideration all the relevant factors, resulting in inaccurate predictions. In this study, an artificial neural network (ANN) is incorporated with '113' of monitored field results to predict surface settlement for a tunnel site with prescribed conditions. To achieve this, a standard format (a protocol) for a database of monitored field data is first proposed and then used for sorting out a variety of monitored data sets available in KICT. Using the capabilities of pattern recognition and memorization of the ANN, an attempt is made to capture the rich physical characteristics smeared in the database and at the same time filter inherent noise in the monitored data. Here, an optimal neural network model is suggested through preliminary parametric studies. It is shown that preliminary studies for generating an optimal ANN under given training data sets are necessary because no analytical method for this purpose is available to date. In addition, this study introduces a concept of relative strength of effects (RSE) [Yang Y, Zhang Q. A heirarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering 1997; 30(4): 207–22] in sensitivity analysis for various major factors affecting the surface settlement in tunnelling. It is seen in some examples that the RSE rationally enables us to recognize the most significant factors of all the contributing factors. Two verification examples are undertaken with the trained ANN using the database created in this study. It is shown from the examples that the ANN has adequately recognized the characteristics of the monitored data sets retaining a generality for further prediction. It is believed that an ANN based hierarchical prediction procedure shown in this paper can be further employed in many kinds of geotechnical engineering problems with inherent uncertainties and imperfections.