Non-stationary realisation of CPT data: considering lithological and inherent heterogeneity

ABSTRACT

The main sources of uncertainty in the soil specification and mechanical behaviour consist of the lithological and heterogeneous randomness of soil deposits. It is quite obvious that the cone penetration testing (CPT) data and the variation of soil characteristics are not stationary. Hence, this paper investigates a new approach to realise a CPT data, taking both sources of uncertainty into consideration. In this regard, the first part of this study illustrates a simple approach to stratify the CPT data, using the Eslami–Fellenius chart of classification. In the second part, the non-stationary algorithm of generating random field is introduced to generate a multi-layer random field. This algorithm takes account of each layer’s statistical properties (i.e. standard deviation, mean, and the trend value), separately. To validate the proposed approach, 41 case histories from different worldwide sites, have been regenerated by considering both the stationary and non-stationary algorithms. The correlation coefficient between real and realised CPT data has been employed to show that the proposed non-stationary algorithm can simulate the CPT data more accurately in comparison with the stationary algorithm.     

Bayesian approach for calibrating transformation model from spatially varied CPT data to regular geotechnical parameter

Cone Penetration Test (CPT) is widely utilized to gain regular geotechnical parameters such as compression modulus, cohesion coefficient and internal friction angle by transformation model in the site investigation. However, it is challenging to obtain simultaneously the unknown coefficients and error of a transformation model, given the intrinsic uncertainty (i.e., spatial variability) of geomaterial and the epistemic uncertainty of geotechnical investigation. A Bayesian approach is therefore proposed calibrating the transformation model based on spatial random field theory. The approach consists of three key elements: (1) three-dimensional anisotropic spatial random field theory; (2) classifications of measurement and error, and the uncertainty propagation diagram of geotechnical investigation; and (3) the unknown coefficients and error calibration of the transformation model given Bayesian inverse modeling method. The massive penetration resistance data from CPT, which is denoted as a spatial random field variable to account for the spatial variability of soil, are classified as type A data. Meanwhile, a few laboratory test data such as the compression modulus are defined as type B data. Based on the above two types of data, the unknown coefficients and error of the transformation model are inversely calibrated with consideration of intrinsic uncertainty of geomaterial, epistemic uncertainties such as measurement errors, prior knowledge uncertainty of transformation model itself, and computing uncertainties of statistical parameters as well as Bayesian method. Baseline studying indicates the proposed approach is applicable to calibrate the transformation model between CPT data and regular geotechnical parameter within spatial random field theory. Next, the calibrated transformation model was compared with classical linear regression in cross-validation, and then it was implemented at three-dimensional site characterization of the background project.     

A performance-based framework for assessing liquefaction potential based on CPT data

This article describes a new performance-based approach for evaluating the return period of seismic soil liquefaction based on standard penetration test (SPT) and cone penetration test (CPT) data. The conventional liquefaction evaluation methods consider a single acceleration level and magnitude and these approaches fail to take into account the uncertainty in earthquake loading. The seismic hazard analysis based on the probabilistic method clearly shows that a particular acceleration value is being contributed by different magnitudes with varying probability. In the new method presented in this article, the entire range of ground shaking and the entire range of earthquake magnitude are considered and the liquefaction return period is evaluated based on the SPT and CPT data. This article explains the performance-based methodology for the liquefaction analysis – starting from probabilistic seismic hazard analysis (PSHA) for the evaluation of seismic hazard and the performance-based method to evaluate the liquefaction return period. A case study has been done for Bangalore, India, based on SPT data and converted CPT values. The comparison of results obtained from both the methods have been presented. In an area of 220 km² in Bangalore city, the site class was assessed based on large number of borehole data and 58 Multi-channel analysis of surface wave survey. Using the site class and peak acceleration at rock depth from PSHA, the peak ground acceleration at the ground surface was estimated using probabilistic approach. The liquefaction analysis was done based on 450 borehole data obtained in the study area. The results of CPT match well with the results obtained from similar analysis with SPT data.     

Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data

The support vector machine (SVM) is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. In this paper SVM models are developed for predicting the ultimate axial load-carrying capacity of piles based on cone penetration test (CPT) data. A data set of 108 samples is used to develop the SVM models. These data were obtained from the literature containing pile load tests and each sample contains information regarding pile geometry, full-scale static pile load tests and CPT results. Moreover, a sensitivity analysis is carried out to examine the relative significance of each input variable with respect to ultimate strength prediction. Finally, a statistical analysis is conducted to make comparisons between predictions obtained from the SVM models and three traditional CPT-based methods for determining pile capacity. The comparison confirms that the SVM models developed in this paper outperform the traditional methods.     

基于CPT资料的土性参数随机场特性研究

通过CPT资料研究土性参数随机场特性是一种比较理想的方法。土性参数随机场的平稳性是后续研究的先决条件之一,可以通过Bartlett统计量进行检验,但采用不同的模型拒绝域相差比较大,这样给实际应用造成困难,新模型LGS由于适应范围广可以解决这一问题。从随机场的基本理论出发,介绍了新相关模型LGS的数值模拟方法。基于LGS相关模型,采用修正的Bartlett统计量对随机场平稳性进行检验,统一了拒绝标准。用两组数据进行了实例分析,通过编制的程序进行计算,结果表明,这种相关模型对不同形式的相关函数有较好的适应性,可以解决拒绝域不统一的问题。     

由CPT确定灌注桩极限承载力的可靠性分析

利用随机场理论,研究了灌注桩的Ｐｓ,ｆｉ和Ｐｓｂ统计特性的统计原则,对相关距离、桩截面尺寸、端阻力计算范围等在程序编制中的协调问题进行了探讨;对上海地区１７个场地的４６条ＣＰＴ曲线进行了电算,进而求出了由ＣＰＴ确定灌注桩极限承载力的可靠度指标的统计特性。     

Load-settlement behavior modeling of single piles using artificial neural networks and CPT data

Pile foundations are usually used when the conditions of the upper soil layers are weak and unable to support the super-structural loads. Piles carry these super-structural loads deep into the ground. Therefore, the safety and stability of pile-supported structures depends largely on the behavior of the piles. In addition, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile behavior based on the results of cone penetration test (CPT) data. Approximately 500 data sets, obtained from the published literature, are used to develop the ANN model. The paper compares the predictions obtained by the ANN with those given by a number of traditional methods and it is observed that the ANN model significantly outperforms the traditional methods. An important advantage of the ANN model is that the complete load-settlement relationship is captured. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design.     

Generating non-stationary random fields of auto-correlated,normally distributed CPT profile by matrix decomposition method

This paper emphasises the true realisation of Cone Penetration Test (CPT) profiles considering non-stationary nature of the data. Formulation of stationary random field theory has been modified and adapted to non-stationary state in order to take into account the mean and variance variability for soil properties. Multi-variance correlation matrix along with the Cholesky decomposition technique was employed to produce realisations of non-homogenous and non-stationary random fields of CPT profiles. A piecewise and segmental data realisation according to the lithology and site class specifications acquired directly from CPT data is adopted in this study so as to render an accurate data simulation. For validation of proposed method 8 CPT test profiles collected from Urmia Lake site have been introduced and simulated by the stationary and non-stationary algorithms. The mean correlation coefficient between the actual CPT data profiles and related realisations along with some other important statistical parameters and their coefficients of variation strongly demonstrate that non-stationary random field generation technique gives quite better accuracy, by comparison to the conventional stationary random field generation scheme.     

A back analysis scheme for refined soil stratification based on integrating borehole and CPT data

Utilizing both borehole and Cone Penetration Testing (CPT) data in soil stratification helps to get more convincing soil stratification results. However, the soil classification results revealed by borehole (Unified Soil Classification System, USCS) and CPT tests (soil behavior type, SBT) are commonly not consistent. This study proposes a feasible solution to integrate the borehole and CPT data with the tree-based method. The tree-based method is naturally suitable for soil stratification tasks as it aims to divide the subsurface space into several clusters based on the similarities of the soil types. A novel boundary dictionary method is proposed to enhance the model performance on complex soil layer conditions. A probabilistic mapping matrix between the USCS-SBT system is built based on a collected municipal database with collocated borehole and CPT data. The optimal soil stratification results can be selected based on considering multiple borehole information and pruning the structure of trees. The structure of the trees can be optimized in a back analysis perspective with the Sequential Model-Based Global Optimization (SMBO) algorithm which aims to maximize the possibility of observing the borehole information based on the USCS-SBT probabilistic mapping matrix. The uncertainties of the optimal soil stratification results can be estimated based on a weighted Gini index method. The performance of the proposed method is validated based on a real case in New Zealand with a cross-validation method. The results indicate that the proposed method is robust and effective.     

关于土层相关距离计算方法的研究

根据随机场理论,对求解土层相关距离的递推空间法和相关函数法进行比较和探讨,指出两种方法所求得的相关距离值存在偏差的原因,并从理论上证明当样本容量足够大时两种方法的计算结果可以得到统一。对相关函数法作出改进,使其更易于曲线的拟合和参数的确定,最后采用实际工程的勘察测试资料进行了分析计算,验证了由计算公式推导所得的结论在实际工程中也是成立的。     

基于窗口数据的重力相关成像

位场相关成像可以快速估计场源的空间位置。为了减小叠加异常对相关成像的影响,现有的成像方法通常是采用剩余异常或异常垂向导数成像。笔者通过加入一个数据截取窗口,计算窗口内数据的相关系数,以减少相邻场源间的影响,从而达到提高成像分辨率的目的;并通过相关系数极值的大小选取窗口,从理论上使成像结果更加可靠。通过重力模型试验,验证本方法能够提高成像分辨率。将该法应用于葫芦岛市黑鱼沟的某矿洞的深度计算中,取得较好效果。     

Probabilistic assessment of soil liquefaction considering spatial variability of CPT measurements

The conventional liquefaction potential assessment methods (also known as simplified methods) profoundly rely on empirical correlations based on observations from case histories. A probabilistic framework is developed to incorporate uncertainties in the earthquake ground motion prediction, the cyclic resistance prediction, and the cyclic demand prediction. The results of a probabilistic seismic hazard assessment, site response analyses, and liquefaction potential analyses are convolved to derive a relationship for the annual probability and return period of liquefaction. The random field spatial model is employed to quantify the spatial uncertainty associated with the in-situ measurements of geotechnical material.     

基于小波变换的地震信号分时分频相关去噪

介绍了Donoho的小波域阈值去噪处理方法,提出了对小波变换尺度上小波系数进行分时分频相关处理去噪后,再重构小波系数的方法,以去除大部分随机噪声。然后,再对重构后地震剖面进行小波域阈值去噪处理。结果表明,使用上述新方法可以有效改善地震剖面处理效果,提高信噪比。     

Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms

Cone penetration test (CPT) is one of the most common in situ tests which is used for pile design because it can be realized as a model pile. The measured cone resistance (q_c) and sleeve friction (f_s) usually are employed for estimation of pile unit toe and shaft resistances, respectively. Thirty three pile case histories have been compiled including static loading tests performed in uplift, or in push with separation of shaft and toe resistances at sites which comprise CPT or CPTu sounding. Group method of data handling (GMDH) type neural networks optimized using genetic algorithms (GAs) are used to model the effects of effective cone point resistance (q_E) and cone sleeve friction (f_s) as input parameters on pile unit shaft resistance, applying some experimentally obtained training and test data. Sensitivity analysis of the obtained model has been carried out to study the influence of input parameters on model output. Some graphs have been derived from sensitivity analysis to estimate pile unit shaft resistance based on q_E and f_s. The performance of the proposed method has been compared with the other CPT and CPTu direct methods and referenced to measured piles shaft capacity. The results demonstrate that appreciable improvement in prediction of pile shaft capacity has been achieved.     

基于关联维数的地质灾害空间分布特征分析

通过分形理论中关联维数及其动态变化来揭示黑水河流域地质灾害的时空分布特征,并探讨了影响区内地质灾害空间分布的相关因素。研究表明:区内不同规模地质灾害点系统空间分布具有分形特征,崩塌灾害点的分布最为集中,滑坡次之,泥石流则最为分散;近十年来区内每年所发生灾害的空间分布集中程度变化剧烈,呈现强集中、弱分散的特点;近五年来每年所发生的灾害呈逐渐分散趋势,但灾害的累积却有逐渐集中的趋势;影响区内地质灾害空间分布异同的主要因素为地形坡度、地层岩性、河流、降雨、人类工程活动、高程、断裂。     

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GIS空间数据的滑坡SPH粒子模型研究

针对光滑粒子流体动力学方法（SPH）在滑坡模拟中建立粒子模型的难题,提出了基于地理信息系统（GIS）栅格数据的粒子排列与插入方法。根据该方法,建立了滑坡SPH粒子模型及相关粒子生成程序,进一步以结合摩尔-库仑破坏准则的SPH宾汉流体模型为核心,实现了运用SPH方法模拟滑坡破坏后三维运动的过程。该SPH模型在对唐家山滑坡的模拟中得到了验证,并预测了金坪子滑坡破坏后的影响范围。结果表明：基于GIS空间数据的滑坡SPH粒子模型具有可行性与良好的适用性。以GIS数据库为基础,开展滑坡灾害的模拟研究,将大大提高对滑坡等地质灾害的仿真分析,为滑坡灾害的预测与防治提供参考。     

Effect of spatial correlation structure and transformation model on the design of shallow strip footing

Directional variability of spatial correlation is observed in natural soils due to their depositional characteristics and it influences the response of structures founded on these deposits. Nonetheless, the results presented in most of the available literature are based on the assumption of either isotropic spatial correlation or perfect spatial correlation of soil properties in horizontal and vertical directions. It is also observed from past studies that the effect of transformation model on the total uncertainty is quite significant. Hence, an effort has been made in this paper to study the effect of anisotropy of autocorrelation characteristics of cone tip resistance (q_c) and the transformation model on the bearing capacity of a shallow strip footing, founding on the surface of a spatially varying soil mass. The statistics in the vertical direction of the soil mass are taken from 8 Cone Penetration Test (CPT) records and statistics in the horizontal direction are assumed. For the case considered, it is observed that the transformation model significantly influences the degree of variability of design parameter. The results also show that isotropic correlation structure based on the vertical autocorrelation distance underestimates the variability of design parameter. On the other hand, perfect correlation in horizontal or vertical, or both directions, overestimates the variability of design parameters, and produces conservative estimates of allowable bearing capacity.     

用地球物理测井资料预测煤层气含量——基于斜率关联度—随机森林方法的工作案例

煤层气含量是煤层勘探开发研究的重点参数之一,由于煤层气含量受多因素影响,能有效预测其含量至关重要.本文将斜率关联度法与随机森林算法相结合,以地球物理测井资料为基础进行煤层气含量预测.首先利用改进的斜率关联度法,计算得到对煤层气含量敏感的测井曲线,再利用交叉验证法探究合适的随机森林决策树个数,并结合选出的超参数利用随机森...