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.     

基于静力触探试验的液化判别新方法

近来地震液化灾害频发,再次成为研究重点,发展具有良好应用前景的基于静力触探试验（CPT）的液化判别方法对预防液化灾害具有重要意义。以Boulanger数据库171组数据为回归样本,分析既有方法存在的问题,提出了基于CPT液化判别的双曲线模型和计算公式,并通过提取2011年新西兰地震147组液化新数据,对该方法进行对比检验。研究表明,我国岩土工程勘察规范的CPT液化判别方法对浅埋砂层偏于保守,对深层土又明显偏于危险,而国际上具有代表性的Robertson方法,其液化临界线存在低烈度区不合理回弯、高烈度区又偏于保守的问题。提出的新公式在不同地震动强度和砂层埋深下均可给出合理判别结果,克服了国内外既有方法的缺点,并纳入到具有样板规范性质的《建筑工程抗震性态设计通则》修订稿中,可为我国相关规范修订和工程应用提供支持。     

Simplified DMT-based methods for evaluating liquefaction resistance of soils

This paper presents simplified dilatometer test (DMT)-based methods for evaluation of liquefaction resistance of soils, which is expressed in terms of cyclic resistance ratio (CRR). Two DMT parameters, horizontal stress index (K_D) and dilatometer modulus (E_D), are used as an index for assessing liquefaction resistance of soils. Specifically, CRR–K_D and CRR–E_D boundary curves are established based on the existing boundary curves that have already been developed based on standard penetration test (SPT) and cone penetration test (CPT). One key element in the development of CRR–K_D and CRR–E_D boundary curves is the correlations between K_D (or E_D) and the blow count (N) in the SPT or cone tip resistance (q_c) from the CPT. In this study, these correlations are established through regression analysis of the test results of SPT, CPT, and DMT conducted side-by-side at each of five sites selected. The validity of the developed CRR–K_D and CRR–E_D curves for evaluating liquefaction resistance is examined with published liquefaction case histories. The results of the study show that the developed DMT-based models are quite promising as a tool for evaluating liquefaction resistance of soils.     

基于Logistic回归模型的砂土液化概率评价

以国内外23次地震中200组场地液化实测数据为基础,通过Logistic回归分析,建立关联修正标准贯入击数N160cs与循环应力比CSR的液化概率模型。以50 %液化概率水平为液化与非液化的临界点,建立了指数形式的抗液化应力比CRR计算式,新建概率模型预测饱和砂土液化与非液化的成功率分别为85.71 %和76.14 %,具有较高的可靠性。与已有模型比较,使用了新的数据和修正系数,消除了一些不合理的偏差,总体判别结果偏于安全。为了将确定性分析方法与概率分析方法联系起来,建立了抗液化安全系数FS与液化概率PL的关系式。算例结果表明,新建概率模型简单、实用、可靠。     

Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT

The determination of liquefaction potential of soil is an imperative task in earthquake geotechnical engineering. The current research aims at proposing least square support vector machine (LSSVM) and relevance vector machine (RVM) as novel classification techniques for the determination of liquefaction potential of soil from actual standard penetration test (SPT) data. The LSSVM is a statistical learning method that has a self-contained basis of statistical learning theory and excellent learning performance. RVM is based on a Bayesian formulation. It can generalize well and provide inferences at low computational cost. Both models give probabilistic output. A comparative study has been also done between developed two models and artificial neural network model. The study shows that RVM is the best model for the prediction of liquefaction potential of soil is based on SPT data.     

Soil liquefaction evaluation using shear wave velocity

A reasonably good relationship between shear wave velocity (SWV) and standard penetration resistance (SPT) of granular soils in agreement with previous studies was obtained from field tests. A similar correlation between SWV and cone penetration resistance of granular soils was also obtained. Using Seed's Standard Penetration Test (SPT)-based soil liquefaction charts, new charts of soil liquefaction evaluation based on SWV data were developed for various magnitude earthquakes.     

CPT-based probabilistic evaluation of seismic soil liquefaction potential using multi-gene genetic programming

In this paper, liquefaction potential of soil is evaluated within a probabilistic framework based on the post-liquefaction cone penetration test (CPT) data using an evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). Based on the developed limit state function using MGGP, a relationship is given between probability of liquefaction (P_L) and factor of safety against liquefaction using Bayesian theory. This Bayesian mapping function is further used to develop a P_L-based design chart for evaluation of liquefaction potential of soil. Using an independent database of 200 cases, the efficacy of the present MGGP-based probabilistic method is compared with that of the available probabilistic methods based on artificial neural network (ANN) and statistical methods. The proposed method is found to be more efficient in terms of rate of successful prediction of liquefaction and non-liquefaction cases, in three different ranges of P_L values compared to ANN and statistical methods.     

A Novel Methodology to Classify Soil Liquefaction Using Deep Learning

In this research, deep learning (DL) model is proposed to classify the soil reliability for liquefaction. The applicability of the DL model is tested in comparison with emotional backpropagation neural network (EmBP). The database encompassing cone penetration test of Chi–Chi earthquake. This study uses cone resistance (q_c) and peck ground acceleration as inputs for prediction of liquefaction susceptibility of soil. The performance of developed models has been assessed by using various parameters (receiver operating characteristic, sensitivity, specificity, Phi correlation coefficient, Precision–Recall F measure). The performance of DL is excellent. Consistent results obtained from the proposed deep learning model, compared to the EmBP, indicate the robustness of the methodology used in this study. In addition, both the developed model was also tested on global earthquake data. During validation on global data, both the models shows good results based on fitness parameters. The developed classification models a simple, but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction potential. The finding of this paper can be further used to capture the relationship between soil and earthquake parameters.

     

Probabilistic evaluation of seismic soil liquefaction potential based on SPT data

The performance-based liquefaction potential analysis was carried out in the present study to estimate the liquefaction return period for Bangalore, India, through a probabilistic approach. In this approach, the entire range of peak ground acceleration (PGA) and earthquake magnitudes was used in the evaluation of liquefaction return period. The seismic hazard analysis for the study area was done using probabilistic approach to evaluate the peak horizontal acceleration at bed rock level. Based on the results of the multichannel analysis of surface wave, it was found that the study area belonged to site class D. The PGA values for the study area were evaluated for site class D by considering the local site effects. The soil resistance for the study area was characterized using the standard penetration test (SPT) values obtained from 450 boreholes. These SPT data along with the PGA values obtained from the probabilistic seismic hazard analysis were used to evaluate the liquefaction return period for the study area. The contour plot showing the spatial variation of factor of safety against liquefaction and the corrected SPT values required for preventing liquefaction for a return period of 475 years at depths of 3 and 6 m are presented in this paper. The entire process of liquefaction potential evaluation, starting from collection of earthquake data, identifying the seismic sources, evaluation of seismic hazard and the assessment of liquefaction return period were carried out, and the entire analysis was done based on the probabilistic approach.     

Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction

The determination of liquefaction potential of soils induced by earthquake is a major concern and an essential criterion in the design process of the civil engineering structures. A purely empirical interpretation of the filed case histories relating to liquefaction potential is often not well constrained due to the complication associated with this problem. In this study, an integrated fuzzy neural network model, called Adaptive Neuro-Fuzzy Inference System (ANFIS), is developed for the assessment of liquefaction potential. The model is trained with large databases of liquefaction case histories. Nine parameters such as earthquake magnitude, the water table, the total vertical stress, the effective vertical stress, the depth, the peak acceleration at the ground surface, the cyclic stress ratio, the mean grain size, and the measured cone penetration test tip resistance were used as input parameters. The results revealed that the ANFIS model is a fairly promising approach for the prediction of the soil liquefaction potential and capable of representing the complex relationship between seismic properties of soils and their liquefaction potential.     

以标贯试验为依据的砂土液化确定性及概率判别法

核电厂址非基岩场地的地基液化问题是核电厂选址的关键问题,亟需建立核电厂址地基液化判别方法。回顾了以标贯试验和地表峰值加速度为依据的砂土液化判别方法的演化历史,依据Idriss-Boulanger确定液化临界曲线的基本方法,提出了确定液化临界曲线的基本原则,分别依据美国液化数据库、中国抗震规范液化判别式所用的液化数据及综合两者的液化数据资料,给出了相应的液化临界曲线,验证了液化临界曲线的位置对不同的细粒含量、有效上覆压力、现场试验方法的液化数据的合理性,分析了测量或估计土层循环应力比和修正标贯击数各种因素的不确定性对液化临界曲线的敏感性,结果表明：所提的液化临界曲线不易受各种因素的影响。利用Monte Carlo模拟、加权最大似然法和加权经验概率法,给出了液化临界曲线的名义抗液化安全系数与液化概率的经验关系式及概率等值线,并对核电厂Ⅰ类、Ⅱ类和Ⅲ类抗震物项地基,给出了相应的液化临界曲线。     

基于MPL神经网络的地震作用下砂土液化评估及预测

砂土在地震的作用下会产生剧烈的液化现象,液化引发的地基失稳会对道路、建筑物、堤坝等各类设施造成严重危害。因此,地震作用下的砂土液化判别预测一直是地质灾害领域研究的热点问题。本文使用过去几十年发生在世界各地的166组地震作用下砂土液化实例数据,通过大量数据训练和参数分析建立了基于机器学习的地震作用下砂土液化判别模型。结果表明,当网络结构为6(输入层)-15(隐藏层)-1(输出层)、训练函数为Levenberg-Marquardt时,对地震液化预测效果较好,最大准确率可达96%。参数分析结果表明不同参数对网络预测准确率影响程度不一:锥端阻力、地表归一化峰值水平加速度影响相对较大;地震震级、总垂向应力、有效垂向应力影响中等;贯入深度对其影响较小。因此在不同网络预测精度要求的条件下,可考虑适当简化输入参数。     

Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data

This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (P_u) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of P_u based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is P_u for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of P_u with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done.     

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.     

基于双桥静力触探的饱和砂土液化判别方法刍议

饱和砂土液化是地震引发地基变形失稳、建筑受损破坏的主因之一,合理判定液化可能性是勘察实践和理论研究的重大课题。国内规范要求砂土液化判别仍以标准贯入试验为主,静力触探等原位测试方法为辅,静力触探较其他原位测试手段,在操作便利性、数据采集连续性、测试成本和抗干扰能力等方面更具优势。针对国内规范常用静力触探砂土液化判别方法进行了分析研究,并结合勘察实例进行了计算和对比,探讨了静力触探判别液化方法的土层适用性,并提出有关改进建议。     

Statistical Evaluation of CPT and CPTu Based Methods for Prediction of Axial Bearing Capacity of Piles

Piles are structural members made of steel, concrete, or wood installed into the ground to transfer superstructure loads to the soil. Nowadays, many structures are built on poor lands, and therefore piles have crucial roles in such structures. Performing in-situ tests such as cone penetration (CPT) and piezocone penetration tests (CPTu) have always been of great importance in designing piles. These tests have a brilliant consistency with reality, and as a result, the outcome data can be used in order to achieve reliable pile designing models and reduce uncertainty in this regard. In this paper, the capability of various CPT and CPTu based methods developed from 1961 to 2016 has been investigated using four statistical methods. Such CPT and CPTu based methods are adopted for direct prediction of axial bearing capacity of piles using CPT and CPTu field data. For this purpose, 61 sets of field data prepared from CPT and CPTu have been collected. The data sets were utilized in order to calculate the axial bearing capacity of piles (Q_E) through 25 different methods. In addition, the measured axial pile capacities (Q_M) have been collected, recorded and prepared from field static load tests, respectively. Then, four different statistical approaches have been applied to assess the accuracy of these methods. Finally, the most reliable and accurate methods are presented.

     

上海地区扁铲侧胀试验与其他原位试验结果的相关性分析

由于扁铲侧胀试验（DMT）在国内应用时间不长,如何合理地解释和应用DMT试验成果尚待进一步探讨。与其他原位试验结果进行对比分析是研究方法之一。介绍了多元回归在岩土工程相关分析中的应用。根据上海地区多个工程的扁铲侧胀试验、十字板剪切试验、标贯试验和静力触探试验的数据,通过选元分析和多元回归,得出扁铲侧胀试验与其它原位试验的相关关系,可供勘察设计相关人员参考使用。     

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.     

Geotechnical reconnaissance and liquefaction analyses of a liquefaction site with silty fine sand in Southern Taiwan

Geotechnical reconnaissance of a recurrent liquefaction site at a Quaternary alluvial deposit in southern Taiwan was conducted to establish a comprehensive case history for liquefaction on silty fine sand with high fines content. The liquefaction occurred at a silty fine sand layer with D₅₀ = 0.09 mm and fines content greater than 35% and was triggered by a M_w = 6.4 earthquake on March 4, 2010, which induced maximum horizontal acceleration up to 0.189 g at the site. In situ subsurface characterizations, including standard penetration test, cone penetration test, and shear wave velocity measurement, were performed as well as cyclic simple shear tests on undisturbed specimens retrieved by a modified hydraulic piston sampler. Comparisons of cyclic resistance ratios (CRRs) indicate that CPT sounding with standard penetration rate could overestimate the resistance ratio and drainage conditions during penetration should be considered for high fines content soil in the liquefaction analysis. Additionally, variations of CRRs from different in situ tests indicate that correlations among in situ tests and CRR could be soil specific and precautions should be taken when using these curves on silty fine sands.