极限分析有限元法讲座—— Ⅰ岩土工程极限分析有限元法

经典岩土工程极限分析方法一般采用解析方法，有些还要对滑动面作假设，且不适用于非均质材料，尤其是强度不均的岩石工程，从而使极限分析法的应用受到限制。随着计算技术的发展，极限分析有限元法应运而生，它能通过强度降低或者荷载增加直接算得岩土工程的安全系数和滑动面，十分贴近工程设计。为此，探讨了极限分析有限元法及其在边坡、地基、隧道稳定性计算中的应用，算例表明了此法的可行性，拓宽了该方法的应用范围。随着计算机技术与计算力学的发展，岩土工程极限分析有限元法正在成为一门新的学问，而且有着良好的发展前景。     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression

In this paper a new approach is presented, based on evolutionary polynomial regression (EPR), for determination of liquefaction potential of sands. EPR models are developed and validated using a database of 170 liquefaction and non-liquefaction field case histories for sandy soils based on CPT results. Three models are presented to relate liquefaction potential to soil geometric and geotechnical parameters as well as earthquake characteristics. It is shown that the EPR model is able to learn, with a very high accuracy, the complex relationship between liquefaction and its contributing factors in the form of a function. The attained function can then be used to generalize the learning to predict liquefaction potential for new cases not used in the construction of the model. The results of the developed EPR models are compared with a conventional model as well as a number of neural network-based models. It is shown that the proposed EPR model provides more accurate results than the conventional model and the accuracy of the EPR results is better than or at least comparable to that of the neural network-based models proposed in the literature. The advantages of the proposed EPR model over the conventional and neural network-based models are highlighted.     

A Simple Limit Equilibrium Approach for Calculation of Ultimate Bearing Capacity of Shallow Foundations on Two-Layered Granular Soils

The bearing capacity of shallow foundations in a non-homogeneous soil profile has been a challenging task in geotechnical engineering. In this paper, a limit equilibrium method is used for calculating bearing capacity factors of shallow foundations constructed on a two-layered granular soil profile. The main objective has been to determine the ultimate bearing capacity computed from equivalent bearing capacity factors N_q and N_γ and comparing that with numerical analysis using finite element methods. It will be shown that the data obtained form the developed method are well comparable with those obtained from FE approach, specially when the difference between shear strength parameters of layers is low which is a practical case for sedimentary soil profiles and also for artificially compacted soils. A computer program has been developed to investigate the influence of various parameters on bearing capacity factors.     

State-of-the-art review of soft computing applications in underground excavations

Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity,compared to the traditional methods.This paper presents an overview of some soft computing techniques as well as their applications in underground excavations.A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting(XGBoost),Multivariate Adaptive Regression Splines(MARS),Artificial Neural Networks(ANN),and Support Vector Machine(SVM) in estimating the maximum lateral wall deflection induced by braced excavation.This study also discusses the merits and the limitations of some soft computing techniques,compared with the conventional approaches available.     

液化地基中桩的破坏机理研究进展

地震诱发的地基液化对桩基础的破坏极大,液化地基中桩的破坏机理是岩土地震工程中的一个重要研究课题。目前地基液化时桩土结构系统的地震性态尚没有认识充分,已有的研究内容较多局限于桩身材料的强度破坏方面,难以考虑液化土体侧向流动、基桩屈曲失稳、以及土与结构动力相互作用等复杂因素的影响。本文重点加强以下3个方面的深入探讨和研究:(1)液化地基中桩的屈曲失稳;(2)液化地基中桩基破坏的数值模拟新方法;(3)液化地基中桩-土-结构的动力相互作用分析。     

Classification of slopes and prediction of factor of safety using differential evolution neural networks

Slope stability analysis is one of the most important problems in geotechnical engineering. The development in slope stability analysis has followed the development in computational geotechnical engineering. This paper discusses the application of different recently developed artificial neural network models to slope stability analysis based on the actual slope failure database available in the literature. Different ANN models are developed to classify the slope as stable or unstable (failed) and to predict the factor of safety. The developed ANN model is found to be efficient compared with other methods like support vector machine and genetic programming available in literature. Prediction models are presented based on the developed ANN model parameters. Different sensitivity analyses are made to identify the important input parameters.     

岩基载荷试验影响因素分析

在大型工程的岩土工程勘察中,如何确定岩石地基的承载力,直接影响到建筑的质量、造价和进度。载荷试验作为模拟建筑物基础受力条件的试验,科学、直观地提供了地基的承载力和变形模量,给设计部门提供了不可或缺的设计依据。然而,岩基载荷试验结果受基岩埋深及孔径的影响尚不清楚。利用ABAQUS有限元分析软件,对深层岩基载荷试验影响因素进行数值模拟分析,得出的结论可为今后岩土工程勘察提供借鉴。     

港口工程中非均质层状地基承载力计算方法

在深水码头、防浪堤等大型港口工程建设中常常会遇到构筑物下覆地基由非均质的层状土构成的情况。对于这类非均质层状地基,我国港口工程地基规范推荐采用Hansen 61公式、Hansen 69公式和Sokolovskii数值解法3种方法计算地基的极限承载能力。然而,工程实践中由这3种方法计算得出的地基极限承载力数值往往有较大的差别。考虑到规范的继承性,基于牛顿迭代法,针对3个理论计算公式编制了计算程序,根据长江口深水治理二期工程地质实测资料,运用该计算程序对该地区地基的极限承载能力进行了计算比较。计算结果表明,对于非均质层状地基,在相同地质条件、相同荷载条件下Sokolovskii数值解法得到的承载力数值最大,Hansen 69公式得到的承载力数值最小。     

New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization

In this study, two different approaches are proposed to determine the ultimate bearing capacity of shallow foundations on granular soil. Firstly, an artificial neural network (ANN) model is proposed to predict the ultimate bearing capacity. The performance of the proposed neural model is compared with results of the Adaptive Neuro Fuzzy Inference System, Fuzzy Inference System and ANN, which are taken in literature. It is clearly seen that the performance of the ANN model in our study is better than that of the other prediction methods. Secondly, an improved Meyerhof formula is proposed for the computation of the ultimate bearing capacity by using a parallel ant colony optimization algorithm. The results achieved from the proposed formula are compared with those obtained from the Meyerhof, Hansen and Vesic computation formulas. Simulation results showed that the improved Meyerhof formula gave more accurate results than the other theoretical computation formulas. In conclusion, the improved Meyerhof formula could be successfully used for calculating the ultimate bearing capacity of shallow foundations.     

Progressive failure analysis of shallow foundations on soils with strain-softening behaviour

This paper presents a finite element approach to analyse the response of shallow foundations on soils with strain-softening behaviour. In these soils, a progressive failure can occur owing to a reduction of strength with increasing the plastic strains induced by loading. The present approach allows this failure process to be properly simulated by using a non-local elasto-viscoplastic constitutive model in conjunction with a Mohr–Coulomb yield function in which the shear strength parameters are reduced with the accumulated deviatoric plastic strain. Another significant advantage of the method is that it requires few material parameters as input data, with most of these parameters that can be readily obtained from conventional geotechnical tests. To assess the reliability of the proposed approach, some comparisons with experimental results from physical model tests are shown. A fairly good agreement is found between simulated and observed results. Finally, the progressive failure process that occurs in a dense sand layer owing to loading is analysed in details, and the main aspects concerning the associated failure mechanism are highlighted.     

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.     

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.     

Undrained capacity of a surface circular foundation under fully three-dimensional loading

Circular foundations are widely employed in offshore engineering to support facilities and are generally subjected to fully three-dimensional loading due to the harsh offshore environmental load and complex operational loads. The undrained capacity of surface circular foundations on soil with varying strength profiles and under fully three-dimensional loading is investigated and presented in the form of failure envelopes that obtained from finite element analyses. The combined ultimate limit state of circular foundations is defined as the two-dimensional failure envelopes in resultant H-M loading space accounting for the vertical load and torsion mobilisation. The effects of vertical load and torsion mobilisation, soil shear strength heterogeneity and loading angle from moment to horizontal load on the shape of normalised H-M failure envelopes are explored. A series of expressions are proposed to describe the shape of failure envelopes obtained numerically, enabling essentially instantaneous generation of failure envelopes and optimisation of a circular foundation design based on constraint of any input variable through implementation in an automated calculation tool. An example application is ultimately provided to illustrate how the proposed expressions may be used in practice.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Probabilistic stability analyses of slopes using the ANN-based response surface

Slope stability analysis is a geotechnical engineering problem characterized by many sources of uncertainty. Some of these sources are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for integrating a commercial finite difference method into a probabilistic analysis of slope stability is presented. Given that the limit state function cannot be expressed in an explicit form, an artificial neural network (ANN)-based response surface is adopted to approximate the limit state function, thereby reducing the number of stability analysis calculations. A trained ANN model is used to calculate the probability of failure through the first- and second-order reliability methods and a Monte Carlo simulation technique. Probabilistic stability assessments for a hypothetical two-layer slope as well as for the Cannon Dam in Missouri, USA are performed to verify the application potential of the proposed method.     

An intelligent multi-objective EPR technique with multi-step model selection for correlations of soil properties

Current multi-objective evolutionary polynomial regression (EPR) methodology has difficulties on decision-making of optimal EPR model. This paper proposes an intelligent multi-objective optimization-based EPR technique with multi-step automatic model selection procedure. A newly developed multi-objective differential evolution algorithm (MODE) is adopted to improve the optimization performance. The proposed EPR process is composed of two stages: (1) intelligent roughing model selection and (2) model delicacy identification. In the first stage, besides two objectives (model accuracy and model complexity), the model robustness measured by robustness ratio is considered as an additional objective in the multi-objective optimization. In the second stage, a new indicator named selection index is proposed and incorporated to find the optimal model. After intelligent roughing selection and delicacy identification, the optimal EPR model is obtained considering the combined effects of correlation coefficient, size of polynomial terms, number of involved variables, robustness ratio and monotonicity. To show the practicality of the proposed EPR technique, three illustrative cases helpful for geotechnical design are presented: (a) modelling of compressibility, (b) modelling of undrained shear strength and (c) modelling of hydraulic conductivity. For each case, a practical formula with better performance in comparison with various existing empirical equations is finally provided. All results demonstrate that the proposed intelligent MODE-based EPR technique is efficient and effective.

     

Undrained capacity of surface foundations with zero-tension interface under planar V-H-M loading

Undrained capacity of strip and circular surface foundations with a zero-tension interface on a deposit with varying degrees of strength heterogeneity is investigated by finite element analyses. The method for simulating the zero-tension interface numerically is validated. Failure envelopes for strip and circular surface foundations under undrained planar V-H-M loading are presented and compared with predictions from traditional bearing capacity theory. Similar capacity is predicted with both methods in V-H and V-M loading space while the traditional bearing capacity approach under-estimates the V-H-M capacity derived from the numerical analyses due to superposition of solutions for load inclination and eccentricity not adequately capturing the true soil response. An approximating expression is proposed to describe the shape of normalised V-H-M failure envelopes for strip and circular foundations with a zero-tension interface. The unifying expression enables implementation in an automated calculation tool resulting in essentially instantaneous generation of combined loading failure envelopes and optimisation of a foundation design as a function of foundation size or material factor. In contrast, the traditional bearing capacity theory approach or direct numerical analyses for a given scenario requires ad-hoc analyses covering a range of input variables in order to obtain the ‘best’ design.     

Integration of Information Technology into Geotechnical Engineering Practice: Concept and Development

Information Technology (IT) has been extensively used to predict, visualize, and analyze physical parameters in order to expedite routine geotechnical design procedures. This paper presents an example of the combined technique of IT and numerical analysis for routine geotechnical design projects. The proposed approach involves the development of ANN(s) using a calibrated finite element model(s) for use as a prediction tool and implementation of the developed ANN(s) into a GIS platform for visualization and analysis of spatial distribution of predicted results. A novel feature of the proposed approach is an ability to expedite a routine geotechnical design process that otherwise requires significant time and effort in performing numerical analyses for different design scenarios. A knowledge-based underground excavation design system that utilizes artificial neural networks (ANNs) as prediction tools is also introduced. Practical implications of the use of IT in geotechnical design are discussed in great detail.     

Experimental and numerical study of soil-reinforcement effects on the low-strain stiffness and bearing capacity of shallow foundations

This paper presents results of meticulous laboratory testing and numerical simulations on the effect of reinforcement on the low-strain stiffness and bearing capacity of shallow foundations on dry sand. The effect of the location and the number of reinforcement layers is studied in the laboratory, whereas numerical simulations are used to study the reinforcement-foundation interaction. Laboratory tests show an increase of 100, 200, and 275% not only in bearing capacity but also in low-strain stiffness (linear load–displacement behaviour) of a square foundation when one, two, and three layers of reinforcement are used, respectively. The specimen preparation technique is found to be crucial for the repeatability and reliability of the laboratory results (less than 5% variability). Numerical simulations demonstrate that if reinforcements are placed up to a depth of one footing width (B) below the foundation, better re-distribution of the load to deeper layers is achieved, thus reducing the stresses and strains underneath the foundation. Numerical simulations and experimental results clearly identify a critical zone between 0.3 and 0.5B, where maximum benefits not only on the bearing capacity but also on the low-strain stiffness of the foundation are obtained. Therefore, soil reinforcement can also be used to reduce low-strain vibrations of foundations.