Experimental Load–Transfer Curves of Laterally Loaded Piles in Nak-Dong River Sand

Kim, Byung Tak; Kim, Nak Kyung; Lee, Woojin; Kim, Young Su

doi:10.1061/(asce)1090-0241(2004)130:4(416)

Cited by 111 publications

(12 citation statements)

References 12 publications

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“…The p ult is used in the concept of hyperbolic displacement boundary condition according to [23],[29],[30] and widely tested on cohesionless soils [32],[33] and adopted for cohesive soils in [34],[35],[36] and [37]: where: γ is the weight of a soil [FL -3 ], K max [FL -2 ] is an initial stiffness of the soil, R f [–] is a conformity factor assumed in the paper 0.85, s [L] is the wall deflection towards the soil (Fig 9B), s m [L] is a unit length. R f and K max can be derived from filed tests.…”

Section: Methodsmentioning

confidence: 99%

Finite element and analytical stochastic models for assessment of underground reinforced concrete water storage facilities and results of their application

Wróblewski

Kozubal

2019

PLoS ONE

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Typical underground water storage facilities consist of reinforced concrete tanks and pipes. Although methods of their analysis are well developed, the use of these methods does not always give unambiguous results, as presented in the paper. An example of underground tank is considered in which cylindrical roof collapsed during construction under soil and excavator loads. The causes of failure are investigated with deterministic and stochastic models. In the first step nonlinear finite element analysis including soil-structure interaction was performed to examine overall level of the structural safety, which was found satisfactory thus not explaining the collapse. In the second step an analytical stochastic model was developed and analysed with emphasis to sensitivity. The last analysis explained the collapse as a complex of unfavourable states for considered variables and the failure was recognised as a mixed construction-geotechnical-structural problem. The key role played backfill properties and its depth.

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Section: Methodsmentioning

confidence: 99%

Finite element and analytical stochastic models for assessment of underground reinforced concrete water storage facilities and results of their application

Wróblewski

Kozubal

2019

PLoS ONE

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“…The test were carried in IS grade-II dry Ennore sand having placement density of 13.35kN/m 3 It is observed that different criteria yield different ultimate load (vide Table- 1). For the present analysis, the average of first three criteria is taken as ultimate pile capacity.…”

Section: Experimental Set-up and Model Testsmentioning

confidence: 99%

Behavior of Lateral Resistance of Flexible Piles in Layered Soils

Chawhan¹

2012

IOSRJMCE

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“…Large deflection analysis for slender piles considering nonlinear Soil-Structure Interaction (SSI) is an essential yet challenging aspect of geotechnical engineering. Currently, this large deflection analysis commonly relies on the Winkler model 1,2 (Figure 1A), characterizing the pile as a beam surrounding by lateral soil springs defined by p-y curves. 3,4 A standard solution technique for this model is the Line Finite Element Method (LFEM) [5][6][7][8] (Figure 1B), which represents the pile with line elements and the surrounded soil with nonlinear springs at element nodes.…”

Section: Introductionmentioning

confidence: 99%

Physics‐informed neural networks for large deflection analysis of slender piles incorporating non‐differentiable soil‐structure interaction

Ouyang,

Li,

Chen

et al. 2024

Num Anal Meth Geomechanics

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Physics‐informed neural networks (PINN) is an emerging machine learning technique and has been applied in different areas successfully. To benefit pile analysis from this innovative technique, this paper addresses several problems that arise when extending PINN to the large deflection analysis of slender piles accounting for nonlinear Soil‐Structure Interaction (SSI). The governing equations for the structural behavior of piles, considering geometric nonlinearity, are elaborated at first, based on which a PINN framework is constructed correspondingly with a model training process. A series of normalization factors are introduced to the loss function to enhance model training stability. Additionally, a regression‐based soil resistance estimation is developed to prevent non‐convergence and instability that may occur during the model training when encountering non‐differentiable SSI. Extensive examples are provided to validate the robustness and accuracy of the proposed analysis method for piles under complex geological conditions. Furthermore, several case studies are conducted, revealing the necessity of appropriate loss normalization and the effectiveness of regression‐based estimation for reflecting non‐differentiable functions in the PINN study.

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Experimental Load–Transfer Curves of Laterally Loaded Piles in Nak-Dong River Sand

Cited by 111 publications

References 12 publications

Finite element and analytical stochastic models for assessment of underground reinforced concrete water storage facilities and results of their application

Finite element and analytical stochastic models for assessment of underground reinforced concrete water storage facilities and results of their application

Behavior of Lateral Resistance of Flexible Piles in Layered Soils

Physics‐informed neural networks for large deflection analysis of slender piles incorporating non‐differentiable soil‐structure interaction

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