Search results

The purpose of this paper is to present an investigation of the effect of different gravity conditions on the penetration mechanism using the two-dimensional Distinct Element Method (DEM), which ranges from high gravity used in centrifuge model tests to low gravity incurred by serial parabolic flight, with the aim of efficiently analyzing cone penetration tests on the lunar surface.

Seven penetration tests were numerically simulated on loose granular ground under different gravity conditions, i.e. one-sixth, one-half, one, five, ten, 15 and 20 terrestrial gravities. The effect of gravity on the mechanisms is examined with aspect to the tip resistance, deformation pattern, displacement paths, stress fields, stress paths, strain and rotation paths, and velocity fields during the penetration process.

First, under both low and high gravities, the penetration leads to high gradients of the value and direction of stresses in addition to high gradients in the velocity field near the penetrometer. In addition, the soil near the penetrometer undergoes large rotations of the principal stresses. Second, high gravity leads to a larger rotation of principal stresses and more downward particle motions than low gravity. Third, the tip resistance increases with penetration depth and gravity. Both the maximum (steady) normalized cone tip resistance and the maximum normalized mean (deviatoric) stress can be uniquely expressed by a linear equation in terms of the reciprocal of gravity.

This study investigates the effect of different gravity conditions on penetration mechanisms by using DEM.

In the literature, several empirical methods can be found to predict the occurrence of nonlinear soil liquefaction in soil layers. These methods are limited to the seismic conditions and the parameters used in developing the model. This paper seeks to present General Regression Neural Network (GRNN) model that addresses the collective knowledge built in simplified procedure.

The GRNN model incorporates the soil and seismic parameters of the region. It was developed in four phases; identification, collection, implementation, and verification. The data used consisted of 3,895 case records, mostly from the cone penetration test (CPT) results produced from the two major earthquakes that took place in Turkey and Taiwan in 1999. The case records were divided randomly into training, testing and validation datasets. Soil liquefaction decision in terms of seismic demand and seismic capacity is determined by the stress‐based method and strain‐based method, and further tested with the well‐known Chinese criteria.

The results produced by the proposed GRNN model explore effectively the complex relationship between the soil and seismic input parameters and further forecast the liquefaction potential with an overall success ratio of 94 percent. Liquefaction decisions were further validated by the SPT, confirming the viability of the SPT‐to‐CPT data conversion, which is the main limitation of most of the simplified methods.

The proposed GRNN model provides a viable tool to geotechnical engineers to predict seismic condition in sites susceptible to liquefaction. The model can be constantly updated when new data are available, which will improve its predictability.

Large deformation problems are frequently encountered in various fields of geotechnical engineering. The particle finite element method (PFEM) has been proven to be a promising method to solve large deformation problems. This study aims to develop a computational framework for modelling the hydro-mechanical coupled porous media at large deformation based on the PFEM.

The PFEM is extended by adopting the linear and quadratic triangular elements for pore water pressure and displacements. A six-node triangular element is used for modelling two-dimensional problems instead of the low-order three-node triangular element. Thus, the numerical instability induced by volumetric locking is avoided. The Modified Cam Clay (MCC) model is used to describe the elasto-plastic soil behaviour.

The proposed approach is used for analysing several consolidation problems. The numerical results have demonstrated that large deformation consolidation problems with the proposed approach can be accomplished without numerical difficulties and loss of accuracy. The coupled PFEM provides a stable and robust numerical tool in solving large deformation consolidation problems. It is demonstrated that the proposed approach is intrinsically stable.

The PFEM is extended to consider large deformation-coupled hydro-mechanical problem. PFEM is enhanced by using a six-node quadratic triangular element for displacement and this is coupled with a four-node quadrilateral element for modelling excess pore pressure.

The purpose of this paper is to develop new constitutive models to predict the soil deformation moduli using multi expression programming (MEP). The soil deformation parameters formulated are secant (Es) and reloading (Er) moduli.

MEP is a new branch of classical genetic programming. The models obtained using this method are developed upon a series of plate load tests conducted on different soil types. The best models are selected after developing and controlling several models with different combinations of the influencing parameters. The validation of the models is verified using several statistical criteria. For more verification, sensitivity and parametric analyses are carried out.

The results indicate that the proposed models give precise estimations of the soil deformation moduli. The Es prediction model provides considerably better results than the model developed for Er. The Es formulation outperforms several empirical models found in the literature. The validation phases confirm the efficiency of the models for their general application to the soil moduli estimation. In general, the derived models are suitable for fine‐grained soils.

These equations may be used by designers to check the general validity of the laboratory and field test results or to control the solutions developed by more in‐depth deterministic analyses.

The complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead to very large errors. The purpose of this paper is to illustrate capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression programming (GEP), and multi‐expression programming (MEP) by applying them to the formulation of several complex geotechnical engineering problems.

LGP, GEP, and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP, and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one of their major advantages over most of the traditional constitutive modeling methods.

In order to demonstrate the simulation capabilities of LGP, GEP, and MEP, they were applied to the prediction of: relative crest settlement of concrete‐faced rockfill dams; slope stability; settlement around tunnels; and soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behavior for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practicing engineers.

The LGP, GEP, and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP, and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in‐depth deterministic analyses.

This paper reports on an integrated prototype expert system, which has been developed to support geotechnical engineers during the initial foundation design phase. Foundation design based on Dutch practice and geotechnical codes as well as construction and project management aspects is programmed. The paper discusses an integrated approach to foundation design of buildings using a case‐demonstration to show the proposed system and its basis. The modelling techniques used offer a sound basis for including these aspects in routine geotechnical design. The example has indicated that the system is capable of integrating geotechnical design aspects with construction related aspects in order to produce better foundation design solutions.

Transportation of the measurement samples from their original place to the measurement site causes significant changes in their mechanical properties. The possibility of making in situ measurements helps to create more precise discrete element models.

The possibility of using in situ modified vane shear test based measurement for the calibration of discrete element models is demonstrated in this work.

The advantage of employing the adjusted vane test is that the values of in situ measurements can be used for the calibration.

The procedure we present allows us to perform accurate discrete element calibration using data from on-site measurements that can be performed quickly and easily.

The purpose of this paper is to examine the feasibility of committee neural network (CNN) theory for the improvement of accuracy and consistency of the neural network model on the estimation of preconsolidation pressure from the field piezocone measurements.

In this study, CNN theory is introduced to improve the initial weight dependency of the neural network model on the prediction of preconsolidation pressure of soft clay from a piezocone test result. It was found that the proposed CNN model can improve the initial weight dependency of the NN model and provide a more consistent and precise inference result than existing NN models, as well as empirical and theoretical models.

It was found that the CNN overcomes the initial weight dependency of the single neural network model. Various committees of the single multilayer perceptrons (MLPs) were tested. It was found that if eight single MLPs, which have the same structure but have been trained with a different initial weight and bias, are accumulated in the committee with the same weighting factor, any variation on the prediction of the preconsolidation pressure from the piezocone test result can be simply and successfully eliminated.

In recent years, ANN has been found to be a powerful theory for analyzing complex relationships involving a multitude of variables, on many geotechnical applications. However, single MLP, when repeatedly trained on the same patterns, tends to reach different minima of the objective function each time and hence give a different set of neuron weights, because the solution is not unique for noisy data, as in most geotechnical problems. The authors observed that a committee neural network system is able to provide improved performance compared with a single optimal neural network.