Statistical analysis of nonGaussian environmental loads and responses
(2002) Abstract
 The thesis deals mainly with offshore engineering related problems where the dominant source of uncertainty is related to the loading. Loads arise from environmental random processes; e.g. waves, currents and winds. Complex as they are, such processes beg the consideration of randomness whence the need of associating probabilistic models to the engineering problems treated here.
Two different types of problems are investigated. Given a seastate, or wind condition, we model: (i) the sea surface elevation at a fixed location, and (ii) the response of structures to environmental loads.
We start by assuming the sea surface elevation, at a fixed location, as a Gaussian process. For this case, exact... (More)  The thesis deals mainly with offshore engineering related problems where the dominant source of uncertainty is related to the loading. Loads arise from environmental random processes; e.g. waves, currents and winds. Complex as they are, such processes beg the consideration of randomness whence the need of associating probabilistic models to the engineering problems treated here.
Two different types of problems are investigated. Given a seastate, or wind condition, we model: (i) the sea surface elevation at a fixed location, and (ii) the response of structures to environmental loads.
We start by assuming the sea surface elevation, at a fixed location, as a Gaussian process. For this case, exact integral forms of the joint long run distributions for the wave characteristics (wave periods, lengths, and heights) are derived. As the water depth decreases or the sea severity increases, the sea surface elevation departs from the Gaussian assumption and the wave profile becomes asymmetric. From a practical point of view this fact has several important consequences. Thus, the sea surface elevation is then considered to be a stationary nonGaussian process: i.e. a sum of a Gaussian process and a secondorder correction term. For such processes the problem of estimating the marginal probability density function is considered. The statistical analysis proceeds with the problem of calculating the mean upcrossing intensity function. Two different methods to obtain numeric estimates of the latter function are presented: (i) a method based on the saddlepoint approximation, and (ii) a method based on numerical integration. The mean upcrossing intensity function as estimated by these methods is then used to estimate the distribution of wave characteristics through a transformed Gaussian model.
In engineering applications the process which represents the response of structures to environmental loads can often be written as a sum of a Gaussian process and a secondorder correction term. The statistical analysis of such responses follows the same pattern as the one outlined above. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/464756
 author
 Machado, Ulla E.B. ^{LU}
 opponent

 Professor Butler, Ronald, Colorado State University.
 organization
 publishing date
 2002
 type
 Thesis
 publication status
 published
 subject
 keywords
 actuarial mathematics, programmering, aktuariematematik, Statistik, operationsanalys, Statistics, operations research, programming
 pages
 190 pages
 publisher
 Centre for Mathematical Sciences, Lund University
 defense location
 Matematikcentrum, SÃ¶lvegatan 18, Lund, sal C
 defense date
 20020611 10:15
 ISSN
 14040034
 ISBN
 916285254X
 language
 English
 LU publication?
 yes
 id
 4809cc9914414cd2909ef28ae39b1a83 (old id 464756)
 date added to LUP
 20070927 15:06:04
 date last changed
 20160919 08:44:57
@phdthesis{4809cc9914414cd2909ef28ae39b1a83, abstract = {The thesis deals mainly with offshore engineering related problems where the dominant source of uncertainty is related to the loading. Loads arise from environmental random processes; e.g. waves, currents and winds. Complex as they are, such processes beg the consideration of randomness whence the need of associating probabilistic models to the engineering problems treated here.<br/><br> <br/><br> Two different types of problems are investigated. Given a seastate, or wind condition, we model: (i) the sea surface elevation at a fixed location, and (ii) the response of structures to environmental loads.<br/><br> <br/><br> We start by assuming the sea surface elevation, at a fixed location, as a Gaussian process. For this case, exact integral forms of the joint long run distributions for the wave characteristics (wave periods, lengths, and heights) are derived. As the water depth decreases or the sea severity increases, the sea surface elevation departs from the Gaussian assumption and the wave profile becomes asymmetric. From a practical point of view this fact has several important consequences. Thus, the sea surface elevation is then considered to be a stationary nonGaussian process: i.e. a sum of a Gaussian process and a secondorder correction term. For such processes the problem of estimating the marginal probability density function is considered. The statistical analysis proceeds with the problem of calculating the mean upcrossing intensity function. Two different methods to obtain numeric estimates of the latter function are presented: (i) a method based on the saddlepoint approximation, and (ii) a method based on numerical integration. The mean upcrossing intensity function as estimated by these methods is then used to estimate the distribution of wave characteristics through a transformed Gaussian model.<br/><br> <br/><br> In engineering applications the process which represents the response of structures to environmental loads can often be written as a sum of a Gaussian process and a secondorder correction term. The statistical analysis of such responses follows the same pattern as the one outlined above.}, author = {Machado, Ulla E.B.}, isbn = {916285254X}, issn = {14040034}, keyword = {actuarial mathematics,programmering,aktuariematematik,Statistik,operationsanalys,Statistics,operations research,programming}, language = {eng}, pages = {190}, publisher = {Centre for Mathematical Sciences, Lund University}, school = {Lund University}, title = {Statistical analysis of nonGaussian environmental loads and responses}, year = {2002}, }