Introduction to Soil Mechanics and Foundations. Physical Characteristics of Soils and Soil Investigations. Stresses, Strains and Elastic Deformations of Soils. One--Dimensional Consolidation Settlement of Fine--Grained Soils. Shear Strength of Soils. A Critical State Model to Interpret Soil Behavior. Bearing of Capacity of Soils and Settlement of Shallow Foundations. Pile Foundations. Two--Dimensional Flow of Water Through Soils. Stability of Earth Retaining Structures. Slope Stability. Appendices. Answers to Selected Problems. References. Index.

Soil Mechanics and Foundations

This paper describes the results of numerical analysis of the effects of installing a driven pile. The geometry of the problem has been simplified by the assumption of plane strain conditions in addition to axial symmetry. Pile installation has been modelled as the undrained expansion of a cylindrical cavity. The excess pore pressures generated in this process have subsequently been assumed to dissipate by means of outward radial flow of pore water. The consolidation of the soil has been studied using a work-hardening elasto–plastic soil model which has the unique feature of allowing the strength of the soil to change as the water content changes. Thus it is possible to calculate the new intrinsic soil strength at any stage during consolidation. In particular the long-term shaft capacity of a driven pile may be estimated from the final effective stress state and intrinsic strength of the soil adjacent to the pile. A parametric study has been made of the effect of the past consolidation history of the soil...

Driven piles in clay - the effects of installation and subsequent consolidation

Six methods to determine axial pile capacity directly from cone penetration test (CPT) data are presented, discussed, and compared. Five of the methods are CPT methods that apply total stress and a filtered arithmetic average of cone resistance. One is a recently developed method, CPTu, that considers pore-water pressure and applies an unfiltered geometric average of cone resistance. To determine unit shaft resistance, the new method uses a new soil profiling chart based on CPTu data. The six methods are applied to 102 case histories combining CPTu data and capacities obtained in static loading tests in compression and tension. The pile capacities range from 80 to 8000 kN. The soil profiles range from soft to stiff clay, medium to dense sand, and mixtures of clay, silt, and sand. The pile embedment lengths range from 5 to 67 m and the pile diameters range from 200 to 900 mm. The new CPTu method for determining pile capacity demonstrates better agreement with the capacity determined in a static loading test and less scatter than by CPT methods.

Pile capacity by direct CPT and CPTu methods applied to 102 case histories

Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of geotechnical engineering problems. It is not intended to describe the ANNs modelling issues in geotechnical engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches. The engineering properties of soil and rock exhibit varied and uncertain behaviour due to the complex and imprecise physical processes associated with the formation of these materials (Jaksa 1995). This is in contrast to most other civil engineering materials, such as steel, concrete and timber, which exhibit far greater homogeneity and isotropy. In order to cope with the complexity of geotechnical behaviour, and the spatial variability of these materials, traditional forms of engineering design models are justifiably simplified. An alternative approach, which has been shown to have some degree of success, is based on the data alone to determine the structure and parameters of the model . The technique is known as artificial neural networks ( ANNs) and is well suited to model complex problems where the relationship between the model variables is unknown (Hubick 1992). This paper is intended to be for readers in the field of geotechnical engineering who are not familiar with artificial neural networks. The paper aims to detail some features associated with ANNs through a review for some of their applications to-date in geotechnical engineering. It is hoped that this review may attract more geotechnical engineers to pay better attention to this promising tool. The paper starts with a brief overview of the structure and operation of the ANNs and gives a general overview of most ANN applications that have appeared in the geotechncial engineering literature. Finally, the paper discusses the relative success of ANNs in predicting various geotechnical engineering properties and behaviour.

Artificial neural network applications in geotechnical engineering

This report contains the findings of a study to develop resistance factors for driven pile and drilled shaft foundations These factors are recommended for inclusion in Section 10 of the AASHTO Load and Resistance Factor Design (LRFD) Bridge Design Specifications to reflect current best practice in geotechnical design and construction The report also provides a detailed procedure for calibrating deep foundation resistance The material in this report will be of immediate interest to bridge engineers and geotechnical engineers involved in the design of pile and drilled shaft foundations

Load and resistance factor design (lrfd) for deep foundations

The discussers point out further aspects of limit point bearing capacity of piles in sand as well as the overlayered stratum and critical depth. It is noted that if phi (angle of friction) is the parameter that can describe the part in p sub o N sub q, it cannot alone explain the part in q sub 1, which essentially depends upon the compressibility phenomena. Experiments on various sands have shown that the relationship between q sub 1 and phi is valid only for sands, the physical and mechanical properties of which are not very different. For homogeneous medium, A relationship is proposed between the critical depth D sub c and p sub c, or 1 sub 1. For overlayered stratum, experimental results have shown that the value of D sub c decreases rapidly with the value of the overload applied to the bearing stratum. It is shown that for long poles, the current rule of 3-5 diameters of penetration in the bearing stratum allows a mobilization of the full point resistance of the pile.

J Biarez

Papers

Bearing capacity and settlement of pile foundations