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.     

Uplift Capacity of Horizontal Strip Anchors in Cohesionless Soil

This paper presents the details of the theoretical analysis of net uplift capacity of horizontal strip anchor in cohesionless soil using Kötter’s equation. A plane failure surface inclined at a characteristic angle with the ground surface is assumed. Results obtained using the proposed method are compared with the available experimental results of 30 cases for dense to loose cohesionless soil, with the maximum embedment ratio of 8. It is observed that the proposed method leads to the predictions of net uplift capacity of horizontal strip anchor that are very close to the experimental results in 93% cases. The comparison of results with available theoretical solutions shows that, proposed method makes better predictions for anchor embedment ratio less than 8 in dense cohesionless soils.     

Prediction of Uplift Pile Displacement Based on Cone Penetration Tests (CPT)

Accurate prediction of uplift pile displacement is necessary to ensure appropriate structural and serviceability performance of civil projects. On the other hand, in recent years, machine-learning models have been applied to many geotechnical-engineering problems, with some degrees of success. The scope of this research includes three main stages: (1) the compilation of load–displacement data sets, obtained from the published literature, (2) analysis of machine learning models that predict the uplift pile displacement based on the cone penetration test data, and the relative importance of input parameters that have been evaluated using senility analysis by the artificial neural network, In addition, this paper also examines the different selection of input parameters and internal network parameters to obtain the optimum model, (3) A parametric study has also been performed for the input parameters to study the consistency of the suggested model. The statistical parameters and parametric study obtained in this research show the superiority of the current model. It is demonstrated that machine learning models such as ANN and GP models outperform the traditional methods, and provide accurate uplift pile displacement predictions.     

Effect of Arching on Uplift Capacity of Single Piles

An analytical method has been proposed to predict the ultimate uplift capacity of single vertical piles embedded in sand considering arching effect. The present analysis takes into consideration of various pile and soil parameters such as length (L), diameter (d) of the pile, angle of internal friction of soil (ϕ), soil pile friction angle (δ) and unit weight of soil (γ). A modified value of coefficient of lateral earth pressure in uplift has been developed considering the arching effect of soil. A comparative assessment of the uplift capacity of piles predicted by using proposed theory and the existing available theories is made with the existing field and model test results. It has been observed that the present model considering the arching effect predicts the results closer.     

Effect of Compressive Load on Uplift Capacity of Cast-Insitu Bored Piles

Field tests were conducted to study the effect of compressive loading on the uplift capacity of single piles embedded in silty sand. The test program consists of four instrumented cast in situ axial pile load tests in compression, pure tension and tension with 25 and 50% of compressive load of ultimate capacity in compression. The experimental results indicate that the net ultimate uplift capacity of single pile decreases with increase in compressive load. The shaft friction is non linear in nature. It observed that as the compressive load increases the shaft friction along the length of pile decreases.     

Estimation of Pullout Capacity of Vertical Plate Anchors in Cohesionless Soil Using MARS

Vertical plate anchors provide an economical solution to safely resist the large horizontal forces experienced by the foundation of different structures such as bulkheads, sheet piles, retaining walls and so forth. This paper develops a multivariate adaptive regression spline (MARS) model-based approach for the determination of horizontal pullout capacity (P _u ) of vertical plate anchors buried in cohesionless soil by utilizing experimental results reported by different researchers. Based on the collection of forty different pullout experimental test results reported in the literature for anchors buried in loose to dense cohesionless soil with an embedment ratio ranges from 1 to 5, a predictive approach for P _u of vertical plate anchors has been developed in terms of non-dimensional pullout coefficient (M _γq ). The capability of the proposed MARS model for estimating the values of M _γq is examined by comparing the results obtained in the present study with those methods available in the literature. Using different statistical error measure criteria, this study indicates that the present approach is efficient in estimating the horizontal pullout capacity of vertical plate anchors as compared to other methods. The sensitivity analysis indicates that the embedment ratio (H/h, where H = embedment depth of anchor, and h = height of anchor) and internal friction angle (?) of soil mass are the two most important parameters for the evaluation of non-dimensional pullout coefficient (M _γq ) using the proposed MARS model.     

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。     

Settlement of Shallow Foundations on Cohesionless Soils Based on SPT Value Using Multi-Objective Feature Selection

Accurate prediction of settlement for shallow footings on cohesionless soil is a complex geotechnical problem due to large uncertainties associated with soil. Prediction of the settlement of shallow footings on cohesionless soil is based on in situ tests as it is difficult to find out the properties of soil in the laboratory and standard penetration test (SPT) is the most often used in situ test. In data driven modelling, it is very difficult to choose the optimal input parameters, which will govern the model efficiency along with a better generalization. Feature subset selection involves minimization of both prediction error and the number of features, which are in general mutual conflicting objectives. In this study, a multi-objective optimization technique is used, where a non-dominated sorting genetic algorithm (NSGA II) is combined with a learning algorithm (neural network) to develop a prediction model based on SPT data based on the Pareto optimal front. Pareto optimal front gives the user freedom to choose a model in terms of accuracy and model complexity. It is also shown how NSGA II can be effectively applied to select the optimal parameters and besides minimizing the error rate. The developed model is compared with existing models in terms of different statistical criteria and found to be more efficient.     

Cone Penetration Test Based Direct Methods for Evaluating Static Axial Capacity of Single Piles

The direct cone penetration test (CPT) based pile design methods use the measured penetrometer readings by scaling relationships or algorithms in a single-step process to enable the assessment of pile capacity components of shaft and base resistance (f _p and q _b, respectively) for evaluation of full-size pilings. This paper presents a state-of-the-art review of published works that focus on direct CPT evaluation of static axial pile capacity. The review is presented in a chronological order to explicate the evolution over the past six decades of an in situ test based solution for this soil-structure interaction problem. The objective of this study is an attempt to assemble maximum published methods proposed as a result of past investigations in one resource to afford researchers and practitioners with convenient access to the respective design equations and charts. In addition to an all-inclusive summary table and the design charts, a compilation of significant findings and discussions thereof are presented. Furthermore, potential future research directions are indicated, with special emphasis on the optimal use of the modern multi-channel hybrid geophysical-geotechnical seismic CPT to evaluate the complete axial pile load–displacement response.     

后压浆提高钻孔灌注桩承载力的试验研究

后压浆技术能改善桩端沉渣和桩周泥皮。通过某工程压浆试验,表明后压浆工艺能大幅度提高基桩承载能力,减小沉降量,并结合分布式光纤检测得到了桩身侧摩阻分布图,对压浆效果及作用机理进行了直观地分析。     

Soil Deformations During Casing Jacking and Extraction of Expanded-Shoe Piles,Using Model Tests

A small-scale 1-g transparent soil model was employed to investigate soil deformations caused by casing-assisted pile jacking of plastic tube cast-in-place concrete piles. The transparent soil was made of fused quartz and a refractive index matched blended oil. Models were sliced with a laser light sheet and digital images were employed to record jacking and extraction of the casing, allowing the use of digital image correlation to derive the generated displacement fields, non-intrusively. The effects of the shoe shape as well as use of a casing to jack the pile were investigated. It was found that under unconfined conditions, soil response to jacking is significantly affected by the pile shoe shape, with displacements adjacent to a conical shoe markedly smaller compared to a flat shoe. Moreover, casing extraction results in recovery of some of the soil deformation that takes place during pile jacking.     

软土地区超长抗拔桩成桩选型研究

对软土地区长抗拔桩中常用的常规直桩、扩底桩、注浆直桩的承载力性状进行了比较分析,研究了不同桩型的荷载传递规律和承载力提高机理,并引出其荷载一位移的理论计算公式,认为抗拔桩存在“有效桩长”。通过对某沿海城市不同超长抗拔桩的现场抗拔试验,分析比较了各种桩型在30mm上拔位移控制值下的单桩承载力和桩侧摩阻力分布规律,认为超过“有效桩长”的设计是不经济的,注浆直桩可以提高安全系数,宜优先选用注浆直桩。     

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.

     

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.     

Vertical Uplift Capacity of Equally Spaced Multiple Strip Anchors in Sand

The ultimate uplift resistance of a group of multiple strip anchors placed in sand and subjected to equal magnitudes of vertical upward pullout loads has been determined by means of model experiments. Instead of using a number of anchor plates in the experiments, a single anchor plate was used by simulating the boundary conditions along the planes of symmetry on both the sides of the anchor plate. The effect of clear spacing (s) between the anchors, for different combinations of embedment ratio (λ) of anchors and friction angle (ϕ) of soil mass, was examined in detail. The results were presented in terms of a non-dimensional efficiency factor (ξ_γ), which was defined as the ratio of the failure load for an intervening strip anchor of a given width (B) to that of a single strip anchor plate having the same width. It was clearly noted that the magnitude of ξ_γ reduces quite extensively with a decrease in the spacing between the anchors. The magnitude of ξ_γ for a given s/B was found to vary only marginally with respect to changes in λ and ϕ. The experimental results presented in this study compare reasonably well with the theoretical and experimental data available in literature.     

郑州某长螺旋成孔CFG桩质量事故原因分析

结合郑州某长螺旋成孔CFG桩复合地基处理出现基坑周围土体变形过大及断桩的工程问题，对工程质量事故原因进行了分析，并根据施工经验提出了相关的预防措施。     

沿海地区复杂地层抗拔桩在深基坑中的试桩实例

通过沿海地区复杂地层中抗拔桩试桩实例,介绍了抗拔试验桩试验参数与方法以及深基坑中的抗拔桩施工技术：填石夹填土层成孔技术、桩端岩心取样技术与桩基清孔技术。试验表明,抗拔试验参数较为可靠且能满足结构设计的抗拔要求,为抗拔桩在深基坑中的应用研究提供了数据支撑。     

Effect of Geotextile Ties on Uplift Capacity of Anchors Embedded in Sand

This paper presents the results of experimental investigation on the effect of geotextile ties on uplift capacity of anchors embedded in sand. Uplift capacity of anchor increases with increase in embedment depth to base diameter (H/D) ratio irrespective of type of anchor. With the introduction of tie to anchors, uplift capacity of anchors increases and optimum number of layers of ties is found to be 2. A non linear power model has been developed to predict the uplift capacity at any settlement (Q _R) of anchors with tie in terms of uplift capacity at any settlement (Q _URs) of anchor without tie, H/D ratio, number of layers of tie and displacement to base diameter ratio (Δ/D). The model is applicable for predicting Q _R having the values of Q _RS, H/D, N and Δ/D in the range of 0.257 ≤ Q _URs ≤ 1.420, 1.5 ≤ H/D ≤ 3.0, 1 ≤ N ≤ 4, 0.8 ≤ Δ/D ≤ 8.     

Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models

This work proposes a methodology to obtain from the soils properties the best prediction model for the California bearing ratio index. The methodology proposes three different prediction techniques: (1) the multiple linear regression, a classical parametric technique; and two non-parametric techniques: (2) the local polynomial regression (LPR) and (3) the radial basis network. The LPR is a known statistical method, but in the geotechnical engineering field is not in common use. Besides, although several research works have been published in this field, they do not include a robust procedure for making good comparison between different models. Here, a cross validation method is proposed with this aim. A data set of 96 samples from Peruvian soils is used to illustrate the methodology. To validate the proposed methodology, a data set from the literature is also analyzed.