Field Tests on Suction Pile Installation in Sand

Cho, Y.; Lee, T. H.; Park, J. B.; Kwag, Daejin; Chung, Eun-Mok; Bang, S.

doi:10.1115/omae2002-28179

Cited by 13 publications

(8 citation statements)

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“…Suction caissons are one of the most effective and widely used anchoring systems for deep-water offshore structures [5][6][7]. For decades, suction caisson anchors have been investigated through analytical studies [8][9][10][11][12][13][14][15][16], numerical analyses [17][18][19][20], laboratory tests [16,[21][22][23][24][25][26][27][28][29][30][31], and prototype model tests [32][33][34][35][36]. However, developing a robust analytical model that describes the behavior and capacity of suction caisson systems is a challenge owing to, among other factors, the uncertainties in soil characteristics and failure mechanisms postulated.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy modeling of holding capacity of offshore suction caisson anchors

Kim

Ahn

Jung

2017

Num Anal Meth Geomechanics

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Summary A fuzzy algorithm, the Takagi–Sugeno model, is implemented to develop a fuzzy inference system for predicting the holding capacity of suction caisson foundations for offshore platforms. The premise parameters of the fuzzy model are optimized by using a subtractive clustering algorithm. The consequent parameters are optimally determined via a weighted least square estimation. The input variables used for training the fuzzy model include the aspect ratio of the caisson, the undrained shear strength of the clay, and the angle that the chain force forms with the horizontal. The output of the proposed fuzzy model is the capacity of the suction caisson anchor. To demonstrate the effectiveness of the fuzzy modeling framework, the results of extensive finite element analyses are investigated. Comparisons of the trained fuzzy model with the data demonstrate that the proposed modeling framework is an effective method to estimate the holding capacity of offshore suction caisson systems. Moreover, the performance of the fuzzy model is robust against higher levels of input data uncertainties. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Fuzzy modeling of holding capacity of offshore suction caisson anchors

Kim

Ahn

Jung

2017

Num Anal Meth Geomechanics

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“…Various methods are in use to find the uplift capacity of suction caisson. Various studies using upper bound analysis (Clukey et al 1995), finite element method (Whittle and Kavvadas 1994, El-Gharbawy and Olson 2000, Zdravkovic et al 2001, Cao et al 2001, 2002a, laboratory model (Goodman et al 1961, Larsen 1989, Steensen-Bach 1992, Datta and Kumar 1996, Singh et al 1996, Rao et al 1997a, 1997b, centrifuge model Morrison 1993, Clukey et al 1995) and prototype model tests (Hogervorst 1980, Tjelta et al 1986, Dyvik et al 1993, Cho et al 2002 have been done to understand the axial and lateral load capacity of suction caisson for static and cyclic load under different soil conditions. Though the finite element method (FEM) along with the laboratory and centrifuge tests are the most popular methods in predicting the uplift capacity of suction caisson, but soil properties are highly variable over short distances.…”

Section: Introductionmentioning

confidence: 99%

Prediction of uplift capacity of suction caisson in clay using extreme learning machine

Muduli¹,

Das²,

Samui³

et al. 2015

Ocean Systems Engineering

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This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

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“…Field and laboratory experiments were carried out by a number of researchers across the globe [5,6]. An analytical solution for estimating the horizontal pullout capacity of embedded suction anchors in clay with the loading point being anywhere along its length and pile with or without flanges was developed [7].…”

Section: Introductionmentioning

confidence: 99%

Estimation of pull out capacity of suction anchors in soft clays using numerical modelling

Kiran

Vijaya

Sivapriya³

2015

2015 IEEE Underwater Technology (UT)

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Offshore mooring requires temporary foundations/anchors in water depths exceeding 1000 m. Suction anchors / piles are feasible and preferred solution in such large water depths and find widespread applications in the offshore oil industry. Suction anchors are large cylindrical (inverted bucket type) structure open at the bottom with a sharp cutting edge and closed at the top. Such foundations are placed on the seabed and allowed to penetrate under self-weight; the rest of the suction anchor is sunk into the seabed by pumping water from inside thereby creating a differential pressure on the inner side. National Institute of Ocean Technology (NIOT), Chennai, India proposes to install an offshore desalination plant at approximately 1000 m water depths. Also, mining of gas hydrates from greater water depths require mooring points. Suction anchors are considered as a suitable option for such mooring.Numerical modelling has been carried out using PLAXIS-3D for simulating the suction anchor behavior and estimating ultimate pull out capacity under various loading conditions and soil type. The validation of the model is done by comparing the results with field tests carried out at Royapuram fishing harbor. It is observed that the model conforms to the ultimate pullout load obtained from the field tests and hence can be used for estimation of pull out capacity of suction anchors at other location having different soil conditions.

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Field Tests on Suction Pile Installation in Sand

Cited by 13 publications

References 0 publications

Fuzzy modeling of holding capacity of offshore suction caisson anchors

Fuzzy modeling of holding capacity of offshore suction caisson anchors

Prediction of uplift capacity of suction caisson in clay using extreme learning machine

Estimation of pull out capacity of suction anchors in soft clays using numerical modelling

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