Abstract: Uncertainties related to fatigue damage estimation of
non-linear systems are highly dependent on the tail behaviour
and extreme values of the stress range distribution. By using
a combination of the First Order Reliability Method (FORM)
and Monte Carlo simulations (MCS), the accuracy of the fatigue
estimations may be improved for the same computational efforts.
The method is applied to a bottom-fixed, monopile-supported large
offshore wind turbine, which is a non-linear and dynamically sensitive
system. Different curve fitting techniques to the fatigue damage
distribution have been used depending on the sea-state dependent
response characteristics, and the effect of a bi-linear S-N curve is
discussed. Finally, analyses are performed on several environmental
conditions to investigate the long-term applicability of this multistep
method. Wave loads are calculated using state-of-the-art theory, while
wind loads are applied with a simplified model based on rotor thrust
coefficients.
Abstract: reliability-based methodology for the assessment
and evaluation of reinforced concrete (R/C) structural elements of
concrete structures is presented herein. The results of the reliability
analysis and assessment for R/C structural elements were verified by
the results obtained through deterministic methods. The outcomes of
the reliability-based analysis were compared against currently
adopted safety limits that are incorporated in the reliability indices
β’s, according to international standards and codes. The methodology
is based on probabilistic analysis using reliability concepts and
statistics of the main random variables that are relevant to the subject
matter, and for which they are to be used in the performance-function
equation(s) associated with the structural elements under study.
These methodology techniques can result in reliability index β, which
is commonly known as the reliability index or reliability measure
value that can be utilized to assess and evaluate the safety, human
risk, and functionality of the structural component. Also, these
methods can result in revised partial safety factor values for certain
target reliability indices that can be used for the purpose of
redesigning the R/C elements of the building and in which they could
assist in considering some other remedial actions to improve the
safety and functionality of the member.
Abstract: This paper argues that increased uncertainty, in certain
situations, may actually encourage investment. Since earlier studies
mostly base their arguments on the assumption of geometric Brownian
motion, the study extends the assumption to alternative stochastic
processes, such as mixed diffusion-jump, mean-reverting process, and
jump amplitude process. A general approach of Monte Carlo
simulation is developed to derive optimal investment trigger for the
situation that the closed-form solution could not be readily obtained
under the assumption of alternative process. The main finding is that
the overall effect of uncertainty on investment is interpreted by the
probability of investing, and the relationship appears to be an invested
U-shaped curve between uncertainty and investment. The implication
is that uncertainty does not always discourage investment even under
several sources of uncertainty. Furthermore, high-risk projects are not
always dominated by low-risk projects because the high-risk projects
may have a positive realization effect on encouraging investment.