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Please use this identifier to cite or link to this item: http://hdl.handle.net/1903/12780

Title: Statistical Characterization and Prediction for a Stochastic Sea Environment
Authors: Chang, Che-yu
Advisors: Ayyub, Bilal M.
Department/Program: Civil Engineering
Type: Dissertation
Sponsors: Digital Repository at the University of Maryland
University of Maryland (College Park, Md.)
Subjects: Civil engineering
Keywords: characterization
sea
statistical
Time series
unicertainty
Issue Date: 2012
Abstract: Designing marine and maritime systems requires the probabilistic characterization of sea waves in the time-history and spectral domains. These probabilistic models include parameters that can be empirically estimated based on limited data in durations, locations and applicability to particular designs. Characterizing the statistical uncertainties associated with the parameters and the models is an essential step for risk-based design methods. A framework is provided for characterizing and predicting the stochastic sea-state conditions using sampling and statistical methods in order to associate confidence levels with resulting estimates. Sea-state parameters are analyzed using statistical confidence intervals which give a clear insight for the uncertainties involved in the system. Hypothesis testing and goodness-of-fit are performed to demonstrate the statistical features. Moreover, sample size is required for performing statistical analysis. Sample size indicates the number of representative and independent observations. Current practices do not make a distinction between the number of discretization points for numerical computations and the number of sampling points, i.e. sample size needed for statistical analysis. Sample size and interval between samples to obtain independent observations are studied and compared with existing methods. Further, spatial relationship of the sea-state conditions describes the wave energy transferred through the wave movement. Locations of interest with unknown sea-state conditions are estimated using spatial interpolations. Spatial interpolation methods are proposed, discussed, and compared with the reported methods in the literature. This study will enhance the knowledge of sea-state conditions in a quantitative manner. The statistical feature of the proposed framework is essential for designing future marine and maritime systems using probabilistic modeling and risk analysis.
URI: http://hdl.handle.net/1903/12780
Appears in Collections:UMD Theses and Dissertations
Civil & Environmental Engineering Theses and Dissertations

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