Research Papers: Structures and Safety Reliability

Probabilistic Seismic Collapse Analysis of Jacket Offshore Platforms

[+] Author and Article Information
M. Zarrin

Faculty of Civil Engineering,
K. N. Toosi University of Technology,
P.O. Box 15875-4416,
Tehran 1996715433, Iran
e-mail: mo_zarrin@yahoo.com

B. Asgarian

Faculty of Civil Engineering,
K. N. Toosi University of Technology,
P.O. Box 15875-4416,
Tehran 1996715433, Iran
e-mail: asgarian@kntu.ac.ir

M. Abyani

Faculty of Civil Engineering,
K. N. Toosi University of Technology,
P.O. Box 15875-4416,
Tehran 1996715433, Iran
e-mail: abyani.mohsen88@gmail.com

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received March 28, 2017; final manuscript received November 9, 2017; published online January 2, 2018. Assoc. Editor: Jonas W. Ringsberg.

J. Offshore Mech. Arct. Eng 140(3), 031601 (Jan 02, 2018) (11 pages) Paper No: OMAE-17-1047; doi: 10.1115/1.4038581 History: Received March 28, 2017; Revised November 09, 2017

This paper analyses and evaluates the variability of seismic demand and capacity of a case study jacket offshore platform considering different sources of uncertainty. The aleatoric uncertainty due to variability of near-fault ground motions, as well as uncertain properties of the damping ratio, material elastic modulus, material yield strength, mass, and gravity load have been investigated. The main aim of this study is to pursue which sources of the uncertainty considerably affect the seismic response of the platform. To this end, the sensitivity analysis was conducted not only in the particular earthquake level, but also in all other ranges of intensity using incremental dynamic analysis (IDA) with a special focus on the seismic collapse fragility. In order to reduce the number of simulations, the Latin hypercube sampling (LHS) scheme has been utilized as an efficient sampling procedure to combine the effects of modeling random variables. The collapse fragility curves are derived for each of the model realizations created with LHS technique. Thereafter, the summarized random fragility curve is compared with the deterministic mean parameter model fragility curve. It is found that the uncertainty in the mass and gravity load on the platform are the most influential variables, which can notably alter the IDA and collapse fragility curves. Furthermore, the random combination of the considered sources of uncertainty shifts the median of collapse fragility away from the mean parameter model collapse fragility. Overall, the effects of the uncertainties do not lead to notable changes in the summarized collapse fragility curve.

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Grahic Jump Location
Fig. 1

Perspective plot of sample platform

Grahic Jump Location
Fig. 2

The logarithmic response spectrum of record suite

Grahic Jump Location
Fig. 3

IDA curves of (a) the MSDR profile along the height of platform for record number eleven and (b) various story levels of jacket part under record number fourteen

Grahic Jump Location
Fig. 4

Multirecords IDA curves of case study platform under selected records

Grahic Jump Location
Fig. 5

Sensitivity of IDA fractile curves to variation in (a) mass and gravity load (upper left curve), (b) yield strength (upper right curve), (c) damping ratio (lower left curve), and (d) material Young's modulus (lower right curve)

Grahic Jump Location
Fig. 6

Sensitivity of collapse fragility curve to variation in (a) mass and gravity load (upper left curve), (b) yield strength (upper right curve), (c) damping ratio (lower left curve), and (d) material Young's modulus (lower right curve)

Grahic Jump Location
Fig. 7

Cloud of random fragility curves and its final expected value




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