Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.     

基于遗传支持向量机的地下工程裂隙岩体注浆量预测

提出了地下工程裂隙岩体注浆量预测的遗传支持向量机方法,通过支持向量机对实际注浆数据样本进行学习,建立注浆量及其影响因素之间的非线性映射关系,基于这种关系实现注浆量的预测。模型建立过程中,考虑到支持向量机惩罚因子和核参数对预测精度的影响,以预测误差为适应度,采用遗传算法对最佳参数进行搜索。结果表明,本文方法计算快速,预测精度高,是一种注浆量预测的好方法。     

基于PSO-SVM的边坡稳定性组合预测方法研究

为利用不同边坡稳定预测方法的特征信息,改进预测质量,提出了一种基于微粒群优化--支持向量机(PSO-SVM)的边坡稳定性非线性组合预测模型。该模型能够利用边坡的特征参数快速预测出边坡的稳定性,且在建模过程中可对不同建模方法的特征信息进行整合,避免了单一方法的偶然性。为提高SVM的学习、泛化能力,采用混合核函数,并用具有并行性和分布式特点的PSO算法优化选择SVM模型参数。利用该非线性组合预测模型对73个边坡实例进行学习,对另外10个边坡实例进行推广预测,研究结果表明,该模型较好地整合了不同建模方法的特征信息,较单一模型、加权组合模型和BP网络组合模型具有更高的预测精度和更小的峰值误差,为边坡稳定性评价提供了一种新的途径。     

Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN)

The stability problem of natural slopes, filled slopes, and cut slopes are commonly encountered in Civil Engineering Projects. Predicting the slope stability is an everyday task for geotechnical engineers. In this paper, a study has been done to predict the factor of safety (FOS) of the slopes using multiple linear regression (MLR) and artificial neural network (ANN). A total of 200 cases with different geometric and shear strength parameters were analyzed by using the well-known slope stability methods like Fellenius method, Bishop’s method, Janbu method, and Morgenstern and Price method. The FOS values obtained by these slope stability methods were used to develop the prediction models using MLR and ANN. Further, a few case studies have been done along the Jorabat-Shillong Expressway (NH-40) in India, using the finite element method (FEM). The output values of FEM were compared with the developed prediction models to find the best prediction model and the results were discussed.     

土质边坡稳定性影响因素的研究

边坡稳定性涉及到诸多因素,引入人工神经网络预测边坡稳定性的方法--误差逆传播学习算法效果显著.边坡稳定性预测系统的输入信息包括岩土体参数、几何参数等,而输出信息则是网络预测的稳定系数和稳定状态.土质边坡主要以圆弧滑移破坏为主,通过人工神经网络预测的结果与实际监测结果的对比分析,证实了BP神经网络在评价土质边坡稳定性方面的效果显著;并在此基础上分析了土质边坡影响因素对边坡稳定性的影响程度.     

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.     

Slope reliability analysis by updated support vector machine and Monte Carlo simulation

This paper presents a new methodology for slope reliability analysis by integrating the technologies of updated support vector machine (SVM) and Monte Carlo simulation (MCS). MCS is a powerful tool that may be used to solve a broad range of reliability problems and has therefore become widely used in slope reliability analysis. However, MCS often involves a great number of slope stability analysis computations, a process that requires excessive time consumption. The updated SVM is introduced in order to build the relationship between factor of safety and random variables of slope, contributing to reducing a large number of normal computing tasks and enlarging the problem scale and sample size of MCS. In the algorithm of the updated SVM, the particle swarm optimization method is adopted in order to seek the optimal SVM parameters, enhancing the performance of SVM for solving complex problems in slope stability analysis. Finally, the integrating method is applied to a classic slope for addressing the problem of reliability analysis. The results of this study indicate that the new methodology is capable of obtaining positive results that are consistent with the results of classic solutions; therefore, the methodology is proven to be a powerful and effective tool in slope reliability analysis.     

Uncoupled analysis of stabilizing piles in weathered slopes

This paper describes a simplified numerical approach for analyzing the slope/pile system subjected to lateral soil movements. The lateral one-row pile response above and below the critical surface is computed by using load transfer approach. The response of groups was analyzed by developing interaction factors obtained from a three-dimensional nonlinear finite element study. An uncoupled analysis was performed for stabilizing piles in slope in which the pile response and slope stability are considered separately. The non-linear characteristics of the soil–pile interaction in the stabilizing piles are modeled by hyperbolic load transfer curves. The Bishop's simplified method of slope stability analysis is extended to incorporate the soil-pile interaction and evaluate the safety factor of the reinforced slope. Numerical study is performed to illustrate the major influencing parameters on the pile-slope stability problem. Through comparative studies, it has been found that the factor of safety in slope is much more conservative for an uncoupled analysis than for a coupled analysis based on three-dimensional finite element analysis.     

基于混合核函数PSO-LSSVM的边坡变形预测

支持向量机（SVM）的核函数类型和超参数对边坡位移时序预测的精度有重要影响。鉴于局部核函数学习能力强、泛化性能弱,而全局核函数泛化性能强、学习能力弱的矛盾,通过综合两类核函数各自优点构造了基于全局多项式核和高斯核的混合核函数,并引入粒子群算法（PSO）对最小二乘支持向量机（LSSVM）超参数进行全局寻优,提出了边坡位移时序预测的混合核函数PSO-LSSVM模型。将模型应用于锦屏一级水电站左岸岩石高边坡变形预测分析,并与传统核函数支持向量机预测结果进行对比分析。结果表明,该模型较传统方法在预测精度上有了明显提高,预测结果科学可靠,在边坡位移时序预测中具有良好的实际应用价值。     

基于边界元法的边坡矢量和稳定分析

矢量和法物理力学意义明确,计算简单,且能根据边坡当前的应力分布状态合理地评价其整体稳定性状态。其中边坡的应力状态通常是采用有限元法来求解。由于边界元法具有研究问题降阶、离散化带来的误差值仅产生在边界以及计算量小等优点,在工程中得到了广泛应用;对于平面问题,以源点作为原点,以所积分单元的切向和法向为坐标轴建立局部坐标系,对于线性单元可以得到所有积分的解析解。因此,可以得到计算区域内部任意点的场变量的解析解,这就保证了位于边界附近区域场变量的精度。利用边界元法得到二维边坡体内连续的应力分布状态,使用矢量和法对该边坡进行稳定性分析,并且与基于有限元的矢量和法、极限平衡法进行对比分析。边坡圆弧滑面和折线滑面的计算结果表明,基于边界元法得到的矢量和安全系数和基于有限元的矢量和法、极限平衡法基本一致;边界元法对应的矢量和安全系数对边界单元尺寸不敏感。     

遗传算法在土坡整体稳定性分析中的应用

边坡稳定性分析的关键是如何确定最危险滑动面的位置并计算与之相对应的安全系数。由于传统的极限平衡分析方法很容易陷入局部极小值而不能找到真正的最危险滑裂面,因此采用瑞典条分确立土坡分析模型,用遗传算法搜索土坡最危险滑动面,进而求得土坡最小安全系数。该方法模拟了生物遗传进化的过程,克服了传统方法的局限性。通过和面积细分法所搜索的最危险滑动面和计算得到的土质边坡安全系数作对比,可得遗传算法在土质边坡稳定分析中具有较高的精度与可靠性。遗传算法可以很好地解决如何寻找土质边坡整体极值的问题。工程应用实例表明,遗传算法分析土质边坡的稳定性效果良好,具有很好的应用前景。     

Least Square Support Vector Machine Applied to Slope Reliability Analysis

This paper investigates the feasibility of Least square support vector machine (LSSVM) model to cope the problem of implicit performance function during first order second moment (FOSM) method based slope reliability analysis. LSSVM is firmly based on the theory of statistical learning. In LSSVM, Vapnik’s ε -insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. Here, LSSVM has been used as a regression technique to approximate implicit performance functions. A slope example has been presented for illustrating the applicability of LSSVM based FOSM method. The developed LSSVM based FOSM has been compared with the artificial neural network (ANN) and least square method. The result shows that the approximation of LSSVM can be used in the FOSM method for slope reliability analysis.     

Development of a model for analysis of slope stability for circular mode failure using genetic algorithm

Slope stability estimation is an engineering problem that involves several parameters. The interactions between factors that affect slope instability are complex and multi-factorial, so often it is difficult to describe the slope stability mathematically. This paper, proposes the use of a genetic algorithm (GA) as a heuristic search method to find a regression model for analyzing the slope stability. For this purpose, an evolutionary algorithm based on GA was used to develop a regression model for prediction of factor of safety (FS) for circular mode failure. The proposed GA uses the root mean squared error as the fitness function and searches among a large number of possible regression models to choose the best for estimation of FS from six geotechnical and geometrical parameters. For validation of the model and checking its efficiency, a validation dataset was used to evaluate FS using the proposed model and a previously developed mathematical GA based model in the literature. Results have shown that the presented model in this study was capable of evaluating FS at a higher level of confidence regarding the other model (R = 0.89 for presented model in this study comparing R = 0.78 for the other model) and can be efficient enough to be used as a simple mathematical tool for evaluation of factor of safety for circular mode failure especially in preliminary stages of the designing phase.     

岩质高边坡稳定性支持向量机预测方法研究

提出一种基于支持向量机的岩质边坡稳定性预测方法。该方法地很好的表达了岩质边坡稳定性与其影响因素之间的非线性映射关系,并应用该方法建立了相应的模型。预测结果表明,利用该方法进行岩质边坡稳定性预测是可行的、有效的。     

强度折减有限元法中的单元阶次影响分析

强度折减有限元法是当前较为有效的边坡稳定性评价方法,且应用越来越广泛。但影响强度折减有限元法的因素有很多,单元阶次是其中比较重要的一个。通过3个经典算例,这些算例分别是二维地基承载力问题、二维边坡和三维边坡问题,分析了单元阶次的选择对强度折减法的影响。计算结果表明：随着单元的增多,线性单元和二次单元都从大于真实解的一侧来逼近真解;相对于二次单元,由于线性单元过“刚”,因此,会过高地估计安全系数,对于实际工程会偏于危险,且误差大,二次单元的误差是线性单元误差的1/8左右。在采用系统最大位移收敛与否的评判标准的基础上,利用二次单元来进行强度折减分析,则可以弥补这种线性单元的不足,得到更加合理的安全系数。二次单元比线性单元更适合于强度折减有限元法。     

空间三维滑坡敏感性分区工具及其应用

对于滑坡敏感性分区目前有三种方法:定性法、统计法和基于岩土定量模型的确定性方法。定性法基于对滑坡敏感性或灾害评估的人为判断;统计法用一个来源于结合了权重因子的预测函数或指标;而确定性法,或者说是物理定量模型法以质量、能量和动量守恒定律为基础。二维确定性模型广泛用于土木工程设计,而无限边坡模型(一维)也用于滑坡灾害分区的确定性模型。文中提出了一个新的基于GIS(地理信息系统)的滑坡敏感性分区系统,这个系统可用于从复杂地形中确认可能的危险三维(3-D)滑坡体。所有与滑坡相关的空间数据(矢量或栅格数据)都被集成到这个系统中。通过把研究区域划分为边坡单元并假定初始滑动面是椭球的下半部分,并使用Monte Carlo随机搜索法,三维滑坡稳定性分析中的三维最危险滑面是三维安全系数最小的地方。使用近似方法假定有效凝聚力、有效摩擦角和三维安全系数服从正态分布,可以计算出滑坡失稳概率。3DSlopeGIS是一个计算机程序,它内嵌了GIS Developer kit(ArcObjects of ESRI)来实现GIS空间分析功能和有效的数据管理。应用此工具可以解决所有的三维边坡空间数据解问题。通过使用空间分析、数据管理和GIS的可视化功能来处理复杂的边坡数据,三维边坡稳定性问题很容易用一个友好的可视化图形界面来解决。将3DSlopeGIS系统应用到3个滑坡敏感性分区的实例中:第一个是一个城市规划项目,第二个是预测以往滑坡灾害对临近区域可能的影响,第三个则是沿着国家主干道的滑坡分区。基于足够次数的Monte Carlo模拟法,可以确认可能的最危险滑坡体。这在以往的传统边坡稳定性分析中是不可能的。     

Modeling of tensile strength of rocks materials based on support vector machines approaches

In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS‐SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS‐SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS‐SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS‐SVM model resulted in error reductions relative to that of the other models. Copyright © 2012 John Wiley & Sons, Ltd.     

基于点稳定系数法的斜坡稳定性分析

基于莫尔-库伦强度理论构架,界定了点稳定系数的概念,并推导其计算公式。利用Geostudio软件建立了均质斜坡模型及计算其应力分布,并在此基础上结合MATLAB软件计算斜坡模型中各点的点稳定系数,勾绘出斜坡体内不同稳定度区域,探析了斜坡稳定性,并与传统极限平衡法进行了对比。对比结果表明:对直立斜坡,两种方法的计算结果均为不稳定,但点稳定性系数法勾绘出坡脚及坡脚底部存在两处不稳定区域;对60°斜坡,点稳定系数法的计算结果表明坡脚处存在潜在不稳定区域,而极限平衡法的计算结果表明坡体处于稳定状态;对45°斜坡,两种方法的计算结果均为稳定,计算结果一致。进一步分析得到结论:点稳定系数法不需要假设或指定某一形状滑面进行斜坡稳定性评价,且可考虑应力集中对坡体稳定性的影响;极限平衡法以稳定系数表达计算结果,而点稳定系数法以不稳定区域表达计算结果。在分析了应力和岩土体力学参数因素对点稳定系数法计算结果的敏感性后发现:相对于极限平衡法,岩土体力学参数对点稳定系数法影响更为敏感,存在黏聚力界限点和内摩擦角界限点,且对均质斜坡破坏形式(局部滑动变形破坏或整体压缩变形破坏)起着非常重要的作用。     

基于PCA-GEP算法的边坡稳定性预测

提出一种基于主成分分析的基因表达式程序设计算法,并将其用于边坡稳定性预测。该算法先采用主成分分析法对样本数据进行预处理,有效地减少预测模型的输入量,消除输入数据间的相关性,再将得到的新样本数据输入基因表达式,构建边坡稳定性的预测模型。利用该预测模型对82个危险圆弧破坏边坡实例中的71个实例进行学习,对另外11个实例进行预测,取得了较好的效果。在保留传统的以误差值作为评判模型优劣标准的同时,引入AIC信息准则法,分别对v-SVR算法和GA-BP网络算法和PCA-GEP算法三种预测模型进行比较分析,结果表明,运用该算法可以获得更优的预测模型,其预测结果比v-SVR算法和GA-BP网络等其他算法得到的结果具有更高的预测精度。工程实例计算表明,该方法是合理、可行的。     

国家天文台500米口径球面射电望远镜台址球冠型边坡稳定性分析

中国科学院国家天文台500米口径球面射电望远镜（Five-hundred-meter Aperture Spherical radio Telescope, FAST）台址位于岩溶洼地内,开挖完成后的洼地边坡外形接近球冠型边坡,属于轴对称圆形凹坡,坡体由胶结的土石混合体和石灰岩组成,局部稳定性良好,整体稳定性有待评价。目前边坡整体稳定性评价通常采用基于平面应变假定的极限平衡法,由于未考虑边坡外形,该方法用于轴对称圆形凹坡稳定性的评价结果有偏差。为消除上述偏差,将轴对称圆形凹坡滑体划分为多个环形条块,考虑环形条块轴力的抗滑作用,提出了针对轴对称圆形凹坡的极限平衡法改进方法;并采用数值分析方法对改进后的简化Bishop法进行验证,结果表明改进后的简化Bishop法安全系数计算结果较未改进方法略有提高,与数值分析法结果基本一致。同时给出轴对称圆形凸坡的安全系数计算公式,计算结果表明圆形凸坡稳定性与直线形边坡较为接近。上述两种计算方法为类似外形的边坡稳定性评价提供了新的途径。