Relevance vector machine applied to settlement of shallow foundation on cohesionless soils

The settlement of shallow foundation on cohesionless soil is a key parameter in the design of shallow foundation. The recently introduced relevance vector machine (RVM) technique is applied to predict the settlement of shallow foundation on cohesionless soils. RVM allows computation of the prediction intervals, taking into account the uncertainties of both the parameters and the data. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. It also estimates the prediction variance. This study shows that compared to the available methods, RVM is better at determining the settlement of shallow foundation on cohesionless soil.     

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.     

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.     

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.     

进化-最小二乘支持向量机的边坡稳定性估计

针对最小二乘支持向量机的参数选择问题,用遗传算法来搜索最小二乘支持向量机的相关参数,避免了人工搜索参数的盲目性,提高了模型的推广性能。根据大量的实际边坡工程数据,建立了基于进化-最小二乘支持向量机的边坡稳定性模型,并将其应用于估计丁家河磷矿自然边坡稳定状况。计算结果与工程实际情况一致,表明了该方法的有效性和合理性。     

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。     

Determination of liquefaction susceptibility of soil: a least square support vector machine approach

This article adopts least square support vector machine (LSSVM) for determination of liquefactions susceptibility of soil based on standard penetration test data. Two models (Models I and II) have been developed. For Model I, input variables are cyclic stress ratio and standard penetration test value (N). Model II employs peak ground acceleration and N as input variables. The developed LSSVM models (Models I and II) give equations for determination of liquefaction susceptibility of soil. The performances of Models I and II are the same. The developed LSSVM gives probabilistic output. The results of LSSVM have been compared with the artificial neural network model. This article shows that N and the peak ground acceleration are sufficient input parameters for determination of liquefaction susceptibility of soil. Copyright © 2012 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     

基于小波变换和GALSSVM的边坡位移预测

边坡变形是一个受多种因素综合作用的复杂非线性动力学演化过程,用现有的物理模型来解决边坡变形的预测问题有很大难度。大量的研究工作表明,用实测的边坡位移时间序列来预测边坡未来变形更为准确,而将多种方法组合起来进行预测成为研究的主要趋势。在此基础上,建立了一种基于小波变换和进化最小二乘支持向量机(GALSSVM)的边坡位移预测模型。首先利用小波变换将边坡时间序列分解为低频分量和高频分量,然后利用互信息法和伪近邻法得到各分量的时间延迟和嵌入维数并进行相空间重构,再根据各个相空间的特点建立相应的GALSSVM预测模型,最后把各分量的预测结果进行小波重构,重构后的结果即为最终的边坡位移预测结果。对丹巴滑坡预测研究表明,这种新的预测模型具有较高的预测精度,可以应用于实际工程     

基于BA-LSSVM模型的黄土滑坡致灾范围预测

滑坡致灾范围的预测研究一直是滑坡研究的重点难点之一。以陕西泾阳南塬滑坡为研究对象,选取滑坡高度、体积、滑源区长度以及宽度为影响因子,采用蝙蝠算法对最小二乘支持向量机中的正则化参数γ和σ₂进行寻优计算,建立BA-LSSVM滑坡致灾范围预测模型,并于多元线性回归模型进行对比。结果表明,该模型具有较高的预测精度和效果,可作为该地区防灾减灾依据。     

基于灰色最小二乘支持向量机的边坡位移预测

利用边坡实测位移序列预测边坡未来时间的位移,可以有效地判断边坡的稳定性。在分析了灰色预测方法和最小二乘支持向量机各自的优缺点的基础上,提出了将二者相结合的一种新的预测模型--灰色最小二乘支持向量机预测模型。新模型既发挥了灰色预测方法中“累加生成”的优点,弱化了原始序列中随机扰动因素的影响,增强了数据的规律性,又充分利用了最小二乘支持向量机求解速度快、易于描述非线性关系的优良特性,避免了灰色预测方法及模型存在的理论缺陷。同时,采用遗传算法进行了模型的参数优化,通过2个工程实例说明灰色最小二乘支持向量机模型预测边坡位移的有效性,具有较高的精度。     

岩爆分类的支持向量机方法

针对岩爆分类问题,提出了基于支持向量机的分类方法。通过对影响岩爆因素的分析,运用支持向量机理论建立岩爆类别的支持向量机模型。结果表明,基于支持向量机的岩爆分类方法具有较高的准确率,该方法是科学可行的,具有广泛的应用前景。     

Predicted ultimate capacity of laterally loaded piles in clay using support vector machine

The determination of ultimate capacity of laterally loaded pile in clay is a key parameter for designing the laterally loaded pile. The available methods for determination of ultimate resistance of pile in clay are not reliable. This study investigates the potential of a support vector machine (SVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. The SVM, which is firmly based on statistical learning theory, uses a regression technique by introducing an ?-insensitive loss function. A sensitivity analysis has been carried out to determine the relative importance of the factors affecting ultimate capacity. The results show that SVM has the potential to be a practical tool for prediction of the ultimate capacity of pile in clay.     

支持向量机在地球化学勘查中的应用

将K均值聚类和v -SVM结合, 推出一种改进的分类方法。该方法根据指标的相关性, 构建了具有强分辨能力的分类模型, 并将模型应用于内蒙某地勘查地球化学研究, 查明了所研究元素之间的相似性, 建立了v - SVM分类机, 实现了组合异常的圈定。     

基于支持向量机的岩体工程分级

提出了岩体工程分级的一种新方法,即支持向量机方法。该方法可以根据有限的学习样本,建立影响岩体级别的条件的因素和级别之间的一种非线性映射,可以对未知的岩体进行工程分级。结果表明,这种方法具有较高的准确率。     

滑坡位移的多模态支持向量机模型预测

将支持向量机（support vector machine,SVM）方法与信号分析中的经验模态分解（empirical mode decomposition, EMD）方法相匹配,提出了一种通过多模态支持向量机函数回归分析建模预测滑坡位移的理论方法。以边坡位移历史观测数据为基础,应用EMD方法获得滑坡形成过程中位移演化的几个特征时间模态,构成了多模态信息统计学习样本,确定了边坡位移演化的自适应多尺度变化信息。对应于每个经验模态的位移变化信息,引入了多模态SVM建模方法,然后合成不同经验模态下边坡位移的计算结果,得到滑坡位移的预测值。以卧龙寺新滑坡和新滩滑坡的监测数据为基础的理论预测结果表明,与采用遗传算法的神经网络方法的预测结果相比,支持向量机经验模态方法具有更强的预测能力,理论预测结果与实际监测值具有很好的一致性     

SVM在地下工程可靠性分析中的应用

将支持向量机应用到地下工程可靠性分析中,通过将支持向量机分别与一阶二次矩和蒙特卡洛结合,提出了基于支持向量机的可靠性分析方法,利用数值模拟构造学习样本,通过支持向量机学习,建立变形与随机变量之间映射关系的支持向量机表达,进而实现隧道极限状态函数及其偏导数的显式表达,从而计算隧道的可靠性指标。该方法避免了传统可靠性分析的缺点。算例分析结果表明,该方法计算效率高、结果可靠,对含有大量随机变量的复杂岩土工程可靠性分析具有很大的潜力,具有广泛的应用前景和工程价值。     

基于支持向量机的地震属性优选及煤层气预测

为了充分发挥地震属性分析技术的优势,针对地震勘探中的小样本事件,阐述了支持向量机原理,开展了基于支持向量机的煤田地震属性非线性优选的方法研究,并在煤层气含量预测中取得了良好的效果。结果表明:基于支持向量机(SVM)属性优选的煤层气预测效果比运用钻孔插值的效果更精确,较好地解决了小样本的学习问题,可作为煤层气预测的一种有效方法。     

围岩破坏模式识别的支持向量机研究

围岩的破坏受到多种因素的影响,并且各破坏模式之间没有明显的界限,因此其破坏模式的识别是一种模糊、非线性、小样本、高维数的模式识别问题。支持向量机（SVM）是最近发展起来的一种新机器学习技术,已在模式识别领域有很多成功地应用。基于支持向量机的思想,提出了围岩破坏模式识别的支持向量机方法,很好地表达了围岩破坏模式与其影响因素之间的复杂非线性关系。具体算例表明,该方法是可行的,具有一定的准确性。     

基于数学形态学多尺度分析的顶板下沉量预测

利用实测顶板沉降序列预测顶板下沉量是分析顶板稳定性的有效手段。提出基于数学形态学（MM）的预测方法,将沉降序列视为一组信号,用多尺度形态学算法将其解耦为若干分量,用最小二乘支持向量机（LSSVM）算法对各分量进行预测,叠加还原得到最终预测结果。根据某金矿762采场预控顶板AIII测点实测沉降时序,建立MM-LSSVM模型进行预测研究。研究结果表明,MM-LSSVM的解耦处理强化了各分量内部规律、弱化了相互干扰。与其他传统预测算法相比,MM-LSSVM的平均绝对百分误差和均方根误差大幅下降,预测精度显著提高。