Accounting for spatial variability in partial factor based design verifications

<p>Traditional design and assessment approaches usually assume that e.g. material properties and environmental influences are uniform in space. However, it is well-known that such parameters can show considerable spatial variability. Furthermore, it has been shown that such spatial variability can significantly influence structural reliability. One way to account for spatial variability is by means of random fields. However, the use of such advanced calculations has not found its way to everyday engineering practice. Therefore, a methodology is developed in order to include spatial variability in the partial factor method in a way which is consistent with the current Eurocode format for design. This is done by introducing a separate partial factor which depends on the correlation length and the variability of the parameter under consideration. As such, an easy-to-use graph is generated, which can be applied in practice for the adjustment of partial factors to take into account spatial correlation. Finally, the proposed approach is validated by means of full-probabilistic calculations.</p>

Safety Formats for Non-Linear Analysis: Are the Current Structural Codes Applicable for Practice?

The present paper describes and critically analyses the most widely used safety formats (i.e., partial factor method, global resistance methods, and probabilistic method) for non-linear finite element analysis. It is shown that the global resistance approach initiated by the implementation of non-linear analysis, which is on a global structural resistance model and offers tools for the safety evaluation. In the general case, the global safety concept reflects the variability of the structural responses due to the random properties of basic variables. It concluded that all safety formats for non-linear analysis implemented in currently developed codes are contained many uncertainties, statistically incorrect and vague formulations.

Updating Material Factors for Assessment of Historic Reinforced Concrete Bridge

This paper is focused on the reliability analysis of an existing reinforced concrete bridge from 1908. The load bearing capacity is assessed in accordance with valid standards using updated partial factors and the partial factors for structural design. Load bearing capacities obtained by these methods are critically compared. The application of the updated partial factors leads to 15% higher load bearing capacity than the ordinary partial factor method used for structural design.

Probabilistic Assessment of Historic Reinforced Concrete Bridge

This paper is aimed at the reliability analysis of an existing reinforced concrete bridge from 1908. The load bearing capacity is assessed in accordance with valid standards using the partial factor method and probabilistic approach. Load bearing capacities obtained by these methods are critically compared. The application of probabilistic method leads to 40 % higher load bearing capacity then the partial factor method used for structural design.

Fully probabilistic design: the way for optimizing of concrete structures

Some standards for the design of concrete structures (e.g. EC2 and the original ČSN 73 1201-86) allow a structure to be designed by several methods. This contribution documents the fact that even if a structure does not comply with the partial reliability factor method, according to EC2, it can satisfy the conditions during the application of the fully probabilistic approach when using the same standard. From an example of the reliability of a prestressed spun concrete pole designed by the partial factor method and fully probabilistic approach according to the Eurocode it is evident that an expert should apply a more precise (though unfortunately more complicated) method in the limiting cases. The Monte Carlo method, modified by the Latin Hypercube Sampling (LHS) method, has been used for the calculation of reliability. Ultimate and serviceability limit states were checked for the partial factor method and fully probabilistic design. As a result of fully probabilistic design it is possible to obtain a more efficient design for a structure.