Abstract

In this paper, special emphasis is given to the inclusion of uncertainties in the evaluation of structural behaviour aiming at a better representation of the system characteristics and the quantification of the importance of these uncertainties in the project. It deals with the structural reliability analysis problem accounting the effect of spatial variability of material properties. To this end it is proposed a finite element model to represent the behaviour of reinforced concrete for short and long-term loads, which includes the main features observed in this material. It was developed a model for the generation of multidimensional non-Gaussian stochastic fields for the material properties that is independent of the finite element mesh. First, an example of a two-dimensional non-Gaussian stochastic field generation in a square steel plate is presented. Latter, the reliability analysis is performed to a limit state function based on prescribed values of mid-span displacements on a simply-supported reinforced beam. Finally, the influence of long-term effects on the reliability of a reinforced concrete beam is studied considering the effect of steel reinforcement corrosion.

Abstract

To address the fatigue damage induced by wind on the wind turbine tower, the present work introduces a novel probabilistic fatigue assessment framework. The idea is based on the deterministic fatigue approach combining with various statistical and probabilistic techniques. The proposed framework is applied to a Design Load Case (DLC) given in IEC 61400-1 standard using a reference wind turbine and carry out a probability distribution of cumulative fatigue damage on the cross-section of wind turbine tower under a turbulent wind condition.

Abstract

In this paper, an approach is proposed to conduct reliability analysis on an offshore jacket considering corrosion degradation under extreme load cases. Corrosion degradation is considered as thickness wastage of the jacket element, which is seen as time-dependent variables. One probabilistic corrosion in literature is adopted by using different distribution models. Also, three different inspection cases (environmental conditions) of the corrosion are studied. The reliability assessment is evaluated by Crude Monte Carlo simulation based on the trained surrogate model. Deep neural networks are used to train the surrogate model, because they are not limited by the distribution and dimension of variables. The results show that using different corrosion distribution model, the probabilities of failure of the jacket are different, even though they have the same mean and standard deviation values. In addition, with same assumption of the distribution model in corrosion, the reliability of the jacket changes a lot concerning different inspection cases. Furthermore, it is noted that the inspection cases have more influences on the reliability analysis of jacket than different corrosion distribution assumptions. At the end, two recommendations are derived from this work.