Least‐square support vector machine applied to settlement of shallow foundations on cohesionless soils

This paper examines the potential of least‐square support vector machine (LSVVM) in the prediction of settlement of shallow foundation on cohesionless soil. In LSSVM, Vapnik's ε‐insensitive loss function has been replaced by a cost function that corresponds to a form of ridge regression. The LSSVM involves equality instead of inequality constraints and works with a least‐squares cost function. The five input variables used for the LSSVM for the prediction of settlement are footing width (B), footing length (L), footing net applied pressure (P), average standard penetration test value (N) and footing embedment depth (d). Comparison between LSSVM and some of the traditional interpretation methods are also presented. LSSVM has been used to compute error bar. The results presented in this paper clearly highlight that the LSSVM is a robust tool for prediction of settlement of shallow foundation on cohesionless soil. Copyright © 2008 John Wiley & Sons, Ltd.     

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.     

Application of statistical learning algorithms to ultimate bearing capacity of shallow foundation on cohesionless soil

This study employs two statistical learning algorithms (Support Vector Machine (SVM) and Relevance Vector Machine (RVM)) for the determination of ultimate bearing capacity (q_u) of shallow foundation on cohesionless soil. SVM is firmly based on the theory of statistical learning, uses regression technique by introducing varepsilon‐insensitive loss function. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. It also gives variance of predicted data. The inputs of models are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ) and angle of shearing resistance (?). Equations have been developed for the determination of q_u of shallow foundation on cohesionless soil based on the SVM and RVM models. Sensitivity analysis has also been carried out to determine the effect of each input parameter. This study shows that the developed SVM and RVM are robust models for the prediction of q_u of shallow foundation on cohesionless soil. Copyright © 2010 John Wiley & Sons, Ltd.     

Relevance vector machine applied to slope stability analysis

This paper examines the potential of relevance vector machine (RVM) in slope stability analysis. The nonlinear relationship between slope stability and its influence factors is presented by the relevance vector learning mechanism based on a kernel‐based Bayesian framework. The six input variables used for the RVM for the prediction of stability slope are density (γ), friction angle (C), friction coefficient (?), slope angle (?_r), slope height (H), and pore water pressure (r_u). Comparison of RVM with some other methods is also presented. RVM has been used to compute the error bar. The results presented in this paper clearly highlight that the RVM is a robust tool for the prediction of slope stability. The experimental results show the effectiveness of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd.     

公路软基沉降预测的支持向量机模型

提出了基于支持向量机（SVM）模型对公路软基沉降进行预测的一种新方法,工程实例预测结果表明,在同样的训练均方误差下,SVM模型预测能力要优于BP神经网络模型,同时该模型能够综合利用分级加载过程中的沉降观测数据作为训练样本集,比仅依靠预压期内部分实测沉降数据的双曲线法更能反映地基土的变形趋势。因此,将建立的SVM模型应用于公路软基沉降预测能够更准确地反映实际沉降过程     

Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine

Accurate prediction of ground surface settlement is necessary for effectively controlling the settlement that develops during tunneling. Many models have been established for this purpose by extracting the relationship between the settlement and the factors that influence it. However, most of the models focused on the maximum ground surface settlement and do not involve dynamic and real-time predictions. This paper investigated how tunneling-induced ground surface settlement developed using a smooth relevance vector machine with a wavelet kernel (wsRVM). Various factors that affect this settlement, including geometrical, geological and shield operational parameters were considered. The model was applied to earth pressure balance (EPB) shield-driven tunnels. The results indicate that the prediction model performs well and that the distribution of the predictions can provide a measure of the prediction uncertainty. Unlike conventional methods that requireadditional efforts to determine relevant model parameters, the proposed method can optimize the parameters in the training process. The results of the parametric study conducted show that the model performance can be improved by the optimization and that the method can serve as a simple tool for practitioners to use in estimating ground surface settlement development during tunneling.     

支持向量机方法在膨胀土分类中的应用

将支持向量机方法应用于膨胀土分类问题中,建立了膨胀土分类的支持向量机模型。以膨胀土实测数据为学习样本,经过训练,得到膨胀土的分类区间。应用该模型对剩余的膨胀土数据进行预测,预测结果表明支持向量机分类模型性能良好、预测精度高、简便易行,是膨胀土判别的一种有效方法,具有广阔的应用前景。     

浅土层工程钻孔机的研制

在城市采用钻机埋设通气装置,补充养分来挽救树木衰亡而研制了浅土层工程钻孔机。文章详细叙述了钻机的组成,工作原理及钻具结构参数,钻进工艺参数的选择。     

软土地基沉降速率的分析研究及其应用

本文利用Terzaghi一维固结理论推导出了在主固结阶段沉降速率与沉降、剩余沉降及固结时间的关系。结果表明，沉降速率与沉降、剩余沉降均呈线性关系，沉降速率的对数与固结时间成正比。根据这些函数关系，就可推算出最终主固结沉降、剩余沉降以及固结时间，为工程施工提供理论依据。本文还结合了工程实例，论证其可行性。     

浅层地基土应用的新途径

本文通过考察目前对浅层地基土的强度和变形研究现状及应用方法,提出了既能够利用浅部低强度地基土作为高层建筑基础的持力层,又能够反映土和工程结构之间相互作用,同时具备简单、方便、经济、安全等特点的土层围压应用.通过试验测试、工程实践效果证明了这种应用的合理性及有效性.     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.     

极限学习机模型在季冻区深基坑地表沉降预测中的应用

针对传统数据驱动模型存在收敛速度慢、过度拟合等问题,提出了基于极限学习机算法的基坑地表沉降预测方法。结合季冻区地铁车站基坑的特点,提取基坑开挖时间、开挖深度、围护桩顶位移、围护桩内力、支撑轴力及地表温度等特征信息,建立极限学习机回归预测模型,选用实例数据进行算例分析,并将其与传统回归预测模型进行对比,实验结果表明,极限学习机模型收敛速度快,泛化能力强,其预测精度优于传统预测模型,且在学习速度方面优势明显,对深基坑安全监控有一定的实用价值。     

FLAC有限差分在地基沉降计算中的应用

介绍了有限差分法计算软件FLAC以及该软件在计算地基沉降时的步骤。通过对一个实际工程的计算对比，发现FLAC和分层总和法这两种方法计算得到的地基沉降量结果之间的误差仅为1．2mm，满足地基设计的要求，且FLAC有限差分法计算地基沉降量具有较好的准确性、直观性和简便性。     

刚性桩复合地基沉降计算的简化方法

既简便又具有较好精度的刚性桩复合地基沉降计算方法仍是工程实践中迫切需要而又有待于解决的问题,文中提出了一个解决的简化方法。在刚性桩复合地基中,由于桩间土荷载水平不高,将桩间土荷载-沉降曲线近似为线性。当桩的荷载水平不高时,对桩的沉降可按线性考虑,线性关系可按线弹性方程计算得到。若桩可能进入非线性甚至塑性,则假设桩的荷载-沉降曲线满足双曲线规律,可较好地考虑桩的非线性沉降过程。通过计算单桩承载力特征值下的沉降,然后通过双曲线方程特点得到桩的非线性沉降方程。对于有单桩静载试验的情况,提出直接利用单桩试验曲线建立单桩的双曲线方程。最后依据共同作用时桩和桩间土的变形协调条件和静力平衡方程,即可计算其实际基础下复合地基的沉降,从而得到一个刚性桩复合地基沉降计算的简化方法。工程实例表明,简化是方法既简便又具有较好的精度,可为工程实践提供一个有效实用的计算方法。     

地基沉降大变形有限元分析的几何刚度效应

研究了基于完全拉格朗日（Total Lagrangian）描述的大变形有限元法分析地基沉降问题的几何刚度效应。在有限元列式的推导过程中严格考虑了土力学表述习惯的影响。通过算例分析,主要研究了几何刚度效应对荷载-沉降曲线的影响,并对比分析了不同率型大变形分析中的几何刚度效应问题。结果表明,几何刚度效应的存在减小了地基大变形有限元系统的刚度;忽略几何刚度效应将导致沉降计算结果偏小,在地基变形较大的情况下误差更明显,Truesdell率型大变形分析的最终沉降结果与小变形法的结果一致。几何刚度效应在地基大变形有限元分析中具有一定程度的影响,处理不当可能出现结构刚度增大的现象。大变形分析结果的性质偏于刚硬。     

软土深基坑开挖地表沉降估算方法的分析

将地表沉降曲线视为正态和偏态分布模式分别对地表沉降进行估算,是当前软土地区深基坑开挖地表沉降计算中两种较为有效的方法,但由于均需要计算地表沉降曲线的包络面积,从而使其计算过程较复杂,同时也增加了计算结果的不确定性。基于上述原因,在以上两种计算方法的基础上,利用经验关系式对现有的计算方法进行了改进,改进后的地表沉降计算方法的计算过程更加简便。利用改进后的地表沉降计算方法对4个工程实例进行了计算分析,并在此基础上对两种计算方法的适用性进行了比较。结果表明,改进后的基坑开挖地表沉降估算方法更加简便实用,地表沉降曲线为偏态分布模式时的计算结果更合理。     

半刚性复合地基沉降实用计算方法的探讨

本文对半刚性复合地基的沉降实用计算方法进行了分析,将临界桩长问题引入复合地基沉降计算方法,提出了复合地基沉降计算的若干建议,构成了更合理、更符合实际的计算方法.最后通过实例计算验证,文中建议方法的计算结果与实测值甚为吻合.     

基于沉降控制的软土路基粉喷桩加固设计方法探讨

结合典型的工程地质特点及工程实例,论述了粉喷桩处理软土路基沉降的计算方法,提出在进行沉降分析和沉降控制的基础上,对软土路基处理的设计方案进行分析和选择,特别是探讨了桩长和桩间距的选择等问题,对不同设计方案在沉降控制等方面进行分析和比较,得到了一些对工程实际十分有益的结论,对于用粉喷桩处理桥头路基防治跳车等问题有直接的指导意义。     

软土e-p曲线确定的简化方法及在非线性沉降计算中的应用

首先假设软土的压缩曲线符合双曲线方程,并通过某工程一系列的数据进行验证,验证结果表明双曲线假定比较符合软土压缩曲线;由于双曲线方程参数相对较少,数据拟合确定比较简单易行,可对实际工程软土地基沉降进行简化计算并能满足工程的精度要求;同时还针对目前地质报告一般不提供详细的孔隙比-压缩应力（简称e-p）试验数据的情况,对e-p双曲线方程提出了一个只采用初始孔隙比e0、压缩模量Es来确定其参数的简化方法。在此基础上,基于e-p曲线确定邓肯-张模型的切线模量Et,采用分层总和法应用于软土地基的非线性沉降计算。对计算结果与实测数据进行比较分析表明,所提出的方法是切实可行的。     

层状地基群桩沉降研究

考虑了桩的加筋与遮帘效应,通过剪切位移法依据力与位移的协调条件得出桩的柔度系数。在此基础上建立土的成层性模型,根据桩顶与桩底的物理关系推导了桩顶沉降与荷载关系的解析解。通过工程算例进行比较,结果表明, 以土层状模型建立的群桩沉降计算方法是可行的。分析表明, 加固区土层不同分布对桩沉降有所影响,浅部土模量的提高有利于减小沉降。