Using DOProC Method in Structural Reliability Assessment

Abstract

Reliability of load-carrying structures has been assessed by various calculation procedures based on probability theory and mathematic statistics, which have been becoming more and more popular. The calculation procedures are well-suited for the design of elements in load-carrying structures with the required level of reliability if at least some input parameters are random and contribute to a qualitatively higher level of the reliability assessment and, in turn, higher safety of those who use the buildings and facilities. This paper discusses application of the original and new probabilistic methods – the Direct Optimized Probabilistic Calculation (“DOProC”), which uses a purely numerical approach without any simulation techniques. This provides more accurate solutions to probabilistic tasks, and, in some cases, to considerably faster completion of computations.

Numerical modelling of reinforced concrete beams with fracture-plastic material

“…With this procedure, it is, however, necessary to use a suitable the global safety limits for the design values. The authors will focus now on the stochastic modelling and probabilistic methods which include, for instance, [13], [15] and [16]. The reason is that the stochastic calculations take much time and this makes them inconvenient for wide use and for drawing conclusions.…”

Selected Problems in Using Round Timber in Building Structures

“…Due to the relatively high carrying capacity dispersion of the round timber and the bolted joints with the slotted -in plates it is reasonable to consider using some of the fully probabilistic methods e.g. [6,7,8] for the reliable and safe design and assessment of this type of structures and joints.…”

Random variation and correlation of the weather data series – evaluation and simulation using bounded histograms

“…Lot of such analyses often use the direct Monte Carlo method ( [11], [2][3][4], [12][13][14]) which is robust, although it may be time consuming. Even though, enhanced probabilistic procedures that use computer power more effectively such as Latin Hypercube Sampling [15], Response Surface Method [16] or Direct Optimized Probabilistic Calculation [17], are not discussed herein. The paper is focused on the analysis of climate random input parameters variability and their mutual correlation necessary for the probabilistic building thermal losses analysis or thermal expansion related problems.…”