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.

     

Ultimate Bearing Capacity Prediction of Eccentrically Inclined Loaded Strip Footings

Extensive laboratory model tests have been carried out on a strip footing resting over dry sand bed subjected to eccentrically inclined load to determine the ultimate bearing capacity (Patra et al. in Int J Geotech Eng 6(3):343–352, 2012a.  https://doi.org/10.3328/IJGE.2012.06.03.343-352, Int J Geotech Eng 6(4):507–514, b.  https://doi.org/10.3328/IJGE.2012.06.04.507-514). Similarly, lower bound calculations based on finite element method were performed to compute the bearing capacity of a strip footing subjected to an eccentric and inclined load lying over a cohesionless soil with varying embedment depth and relative density (Krabbenhoft et al. in Int J Geomech ASCE, 2014.  https://doi.org/10.1061/(ASCE)GM.1943-5622.0000332). The load may be applied in two ways namely, towards the center line and away from the center line of the footing. Based on the results (both experimental and numerical analyses), a neural network model is developed to predict the reduction factor that will be used in computing the ultimate bearing capacity of an eccentrically inclined loaded strip footing. This reduction factor (RF) is the ratio of the ultimate bearing capacity of the footing subjected to an eccentrically inclined load to the ultimate bearing capacity of the footing subjected to a centric vertical load. A thorough sensitivity analysis is carried out to evaluate the parameters affecting the reduction factor. Based on the weights of the developed neural network model, a neural interpretation diagram is developed to find out whether the input parameters have direct or inverse effect on the output. A prediction model equation is framed with the trained weights of the neural network as the model parameters. The predictions from ANN, and those from other approaches, are compared with the results computed from both experimentation and FEM analyses. The ANN model results are found to be more accurate and well matched with other results.     

A New Computer Program for Pile Capacity Prediction using CPT Data

An interactive computer program “GLAMCPT” is developed for application in soil profiling and prediction of pile load capacity using cone penetration test (CPT) and laboratory soil test results. GLAMCPT calculates pile capacity according to 10 selected methods from European design codes, refereed international publications and recommendations of professional institutions. To demonstrate the capabilities of the program, a database of comprehensive ground investigation and full-scale pile tests in sand, at a Belgian site, is analysed using GLAMCPT. The database comprises 11 static tests and 12 dynamic tests on piles of different construction techniques, including driven pre-cast concrete piles and screwed cast in-situ piles, installed using 5 different procedures. Prior to pile installation, CPTs were carried out at each proposed pile location. Comparison of GLAMCPT predictions with the observed pile capacities reveals that the most accurate of the existing methods yields an average, μ, of predicted to observed pile head capacity [P_uh(p)/P_uh(m)] equal to 0.94. The most consistent method produces a coeffcient of variation (COV) of [P_uh(p)/P_uh(m)] equal to 0.1 and ranking index (RI) of 0.08. Parametric studies have been carried out using GLAMCPT to formulate an improved predictive method, which yielded: μ = 0.99, COV = 0.07 and RI = 0.04.     

Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming

Numerous methods have been proposed to assess the axial capacity of pile foundations. Most of the methods have limitations and therefore cannot provide consistent and accurate evaluation of pile capacity. However, in many situations, the methods that correlate cone penetration test (CPT) data and pile capacity have shown to provide better results, because the CPT results provide more reliable soil properties. In an attempt to obtain more accurate correlation of CPT data with axial pile capacity, gene expression programming (GEP) technique is used in this study. The GEP is a relatively new artificial intelligent computational technique that has been recently used with success in the field of engineering. Three GEP models have been developed, one for bored piles and two other models for driven piles (a model for each of concrete and steel piles). The data used for developing the GEP models are collected from the literature and comprise a total of 50 bored pile load tests and 58 driven pile load tests (28 concrete pile load tests and 30 steel pile load tests) as well as CPT data. For each GEP model, the data are divided into a training set for model calibration and an independent validation set for model verification. The performances of the GEP models are evaluated by comparing their results with experimental data and the robustness of each model is investigated via sensitivity analyses. The performances of the GEP models are evaluated further by comparing their results with the results of number of currently used CPT-based methods. Statistical analyses are used for the comparison. The results indicate that the GEP models are robust and perform well.     

摩擦桩的荷载传递及承载力的一些问题

本文首先讨论桩承载力的主要影响因素,分析桩的荷载传递机制,描述各类荷载传递函数的型式,介绍桩侧摩阻力和端阻力的测定方法及实测结果。接着,分析成桩方法对承载力的影响。最后,对钻孔灌注桩的下沉,失稳和承载力变化进行讨论。     

Application of Relevance Vector Machine for Prediction of Ultimate Capacity of Driven Piles in Cohesionless Soils

This paper examines the potential of relevance vector machine (RVM) in prediction of ultimate capacity of driven piles in cohesionless soils. RVM is a Bayesian framework for regression and classification with analogous sparsity properties to the support vector machine (SVM). In this study, RVM has been used as a regression tool. It can be seen as a probabilistic version of SVM. In this study, RVM model outperforms the artificial neural network (ANN) model based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also estimates the prediction variance. An equation has been developed for the prediction of ultimate capacity of driven piles in cohesionless soils based on the RVM model. The results show that the RVM model has the potential to be a practical tool for the prediction of ultimate capacity of driven piles in cohesionless soils.     

Determination of Bearing Capacity for Piles with CASE Method

A method named CASE method is presented for determining the statically axial bearing capacity of piles from dynamic measurements of force and acceleration performed under the impact of a large hammer. The basic equation for calculating resistance against pile penetration is derived. The examples of field measurements are given. In order to use CASE method effectively, it is necessary to determine “damping ”constant by comparison the pile bearing capacity from static method with that from CASE method, or by changing hammer weight or dropping height to cause the Vb to close on zero and to obtain the static bearing capacity of pile.     

确定摩擦桩承载力的一种动测法

本文在分析单桩的承载能力取决于桩身条件、桩周岩土介质的力学参数和受载的作用时间的基础上,探讨了单桩的动力和静力的承载机制。提出一种考虑桩周岩土介质动、静力学强度指标和加载速率的动测法,用它来确定摩擦桩的极限承载力。动测与静压试桩对比和工程应用表明,该法简便有效。     

从静载荷试验分析深层搅拌桩承载力的影响因素

论述了深导搅拌桩成桩，加固机理，着重分析桩体强度的影响因素，指出由于在成桩过程中断喷或勘察不规范等因素导致局部地段承载力达不到要求，也提也了施工中应重视的几个问题。     

A Dynamic Measurement of Bearing Capacity for Frictional Piles

Based on the fact that the bearing capacity of pile depends of the condition of pile body, mechanical parameters of the media around it, and loading period, the mechanism of dynamic and static bearing capacity for the single-pile is studied. Considering loading rate and difference between dynamic and static mechanical strengths of rock and soil media, a dynamic measurement is proposed to determine critical bearing capacity of frictional piles, which has been proved to be simple and effective with engineering applications and comparison between dynamic and static tests.     

凯司(CASE)法确定桩的承载力

本文介绍了用测量大锤撞击作用下的力和加速度以确定桩的静态轴向承载力的凯司(CASE)法。介绍了推导计算桩贯入阻抗力的基本方程,并给出了现场测量的例子。要有效地使用这种方法,就要将其测得的承载力与静力法测得的桩承载力对比来确定“阻尼”常数,或通过改变锤重和落距使V_b。趋近零,获得桩的静承载力,并求得“阻尼”常数。     

超长PHC管桩轴向受力试验研究

通过高应变动测试验和静载荷试验,对深厚软土地基中PHC管桩的受力特性进行了研究,探讨了深厚软土地基中超长PHC管桩的竖向承载特性和荷载传递机理.结果表明,高应变动测试验和静载荷试验得出的极限承载力结果很接近.在深度小于30 m时,动静试验所得的侧摩阻力比较接近,但是随着深度的加深,动测试验所得的侧摩阻力比静载试验要大.     

确定桩基极限承载力的随机-模糊方法

简述了岩土工程中确定桩基极限承载力的一般方法，讨论了影响桩基极限承载力的主要因素，在此基础上，运用随机-模糊方法确定桩基极限承载力并给出算例。初步研究表明，该方法确定桩基极限承载力比一般的统计方法要客观和全面。     

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。     

挤扩支盘桩极限承载力的预测

用双曲线方法对110根挤扩支盘桩的极限承载力进行预测,并与直杆桩预测结果进行对比。结果表明由于支盘桩受力性状复杂,加载前期和中期预测精度较高,后期较低,误差超过15%;支盘桩的沉降曲线出现台阶时预测值与实测值误差较大;支盘桩极限承载力的预测精度与工程地质条件和桩本身的参数密切相关,桩身参数和地质条件相同条件下各桩预测精度比较接近。总体上直杆桩的预测结果优于支盘桩。     

桩底注浆对钻孔灌注桩竖向承载性能的影响

依据某工程中采用桩底注浆和没有注浆的2根试桩的单桩竖向抗压静荷载试验结果,通过对基桩的竖向极限承载力性状、桩身轴力传递特性以及桩侧阻力发挥特性和桩端阻力发挥特性的综合分析研究,揭示了桩底注浆对基桩承载性状的影响情况。     

成都粘土中载体桩承载力分析

通过成都粘土中载体桩工程实例的载荷试验资料,探讨成都粘土中载体桩承载特征,以及JGJ135-2007规程承载力估算问题。     

郑州某大厦试验桩承载力初探

通过某工程的实测数据,运用最小二乘法原理,采用试算法对桩侧阻力进行分析。     

静压桩极限承载力的时效分析

通过隔时复压等试验表明,静压桩的极限承载力随时间呈双曲线增大.静压桩时效性机理主要是触变恢复效应和固结效应.引入时间参数分析在粘土中沉桩时所引起的超静孔隙水压力,给出了考虑固结效应的超静孔隙水压力的解.考虑不确定性和模糊性的特点,根据静压桩的压桩力,推算桩的极限承载力,并给出了一种计算方法.     

试论水泥喷射搅拌桩的单桩承载力测试

水泥喷射搅拌桩（以下简称粉喷桩）是软土地基加固的一种方法。按规范，一般测试复合地基承载力作为加固效果的评判标准。粉喷桩是复合地基的基础，很多设计单位和建设单位要求对单桩进行承载力检测，验证桩的设计参数，检查施工质量。由于粉喷桩承载力能否测试，如何取值等，没有规范依据，给检测工作带来困难。本文就此问题谈一点看法。