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Foundation analysis and design
01 Jan 1968-
TL;DR: In this paper, Fondation de soutenagement et al. presented a reference record for Dimensionnement Reference Record created on 2004-09-07, modified on 2016-08-08.
Abstract: Keywords: Fondation ; Mur de soutenement ; Pieux ; Capacite portante ; Ancrage ; Dimensionnement Reference Record created on 2004-09-07, modified on 2016-08-08
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TL;DR: In this article, the authors present the observations of cone penetration testing, in situ vane shear testing and undrained triaxial testing of underconsolidated marine clay in the Craney Island Dredged Material Management Area (CIDMMA), Norfolk, VA.
16 citations
TL;DR: In this paper, the authors proposed the use of artificial neural network (ANN) to predict the safe bearing capacity (SBC) of noncohesive soil as a function of coefficient of curvature, coefficient of uniformity, and design value of soil density along with footing dimensions such as depth, width and diameter.
Abstract: Estimation of safe bearing capacity (SBC) of noncohesive soil based on Indian Standard Code requires a lot of field work, viz, conducting direct shear tests to determine cohesion and angle of internal friction, performing the standard penetration test to determine the N-value of soil, and finding the relative density and dry density of soil. The present study does away with these soil parameters except for the design value of density and uses the results of sieve analysis to determine the SBC of soil. This research proposes the use of artificial neural network (ANN) to predict the SBC of noncohesive soil as a function of coefficient of curvature, coefficient of uniformity, and design value of soil density along with footing dimensions such as depth, width and diameter (in case of circular footing), and the desired settlement of the footing. The results show that ANN is a useful technique in estimating SBC of noncohesive soil using parameters derived from sieve analysis results and match closely from the results derived from the traditional methods based on Terzaghi’s theories.
16 citations
TL;DR: In this article, an attempt has been made to model and analyze the footings, having finite flexural rigidity, lying on granular bed-stone column-reinforced poor soil system.
Abstract: In this paper, an attempt has been made to model and analyze the footings, having finite flexural rigidity, lying on granular bed-stone column-reinforced poor soil system. The granular bed placed over stone column reinforced earth beds has been idealized by the Pasternak shear layer. The natural occurring poor soil has been idealized as Winkler springs and stone columns have been idealized as stiffer Winkler springs. Nonlinear behavior of granular bed, natural occurring soil and the stone columns has been considered in the analysis and has been incorporated by means of hyperbolic constitutive relationships. Governing differential equations for the soil-foundation system have been obtained and finite difference method has been adopted for solving these by means of Gauss-Elimination iterative scheme. A detailed parametric study for a combined footing subjected to concentrated column loads at its edges has been carried out to study the influence of various parameters on the flexural response of the f...
16 citations
TL;DR: In this article, the capabilities of artificial neural networks (ANNs) are assessed as a computational method for predicting standard penetration test (SPT) results at any point (x, y, z) in a field where a set of SPTs is performed.
Abstract: Geotechnical engineers recognize the variability of the geological materials they work with, including uncertainties associated with subsurface characterization tasks. These uncertainties include data scattering, such as real spatial variation in soil properties, or random testing errors. Systematic errors, as can occur in bias measurement procedures, are also common. In almost all construction projects, penetration tests play a major role in subsoil characterization. Interpretation of test results is mostly empirical, and it is therefore prudent to find a suitable computational method to minimize the error in predicting values at points away from actual test locations. In this research, the capabilities of artificial neural networks (ANNs) are assessed as a computational method for predicting standard penetration test (SPT) results at any point (x, y, z) in a field where a set of SPTs is performed. SPT and moisture content data for five bore holes are used to train and test the developed three-dimensiona...
16 citations
TL;DR: In this article, an approach is presented to determine the reactive loads in individual reinforcements, T max, using limit analysis (LA) considering a log-spiral mechanism and the effects of facing elements for segmental block reinforced soil walls.
16 citations