Uncertainty & Reliability Analysis of Structural Dynamical Systems

this paper various methods to analyze structural systems under stochastic dynamic loading are qualitatively compared. While the Karhunen-Lo eve expansion proved to be advantageous when uncertainty estimation is required, (advanced) Monte Carlo simulation procedures are recommended for reliability estimates. The computational efficiency of the methods play an important role w.r.t. practical applications. A further quantitative benchmark study is recommen

Developments in Stochastic Structural mechanics

Uncertainties are a central element in structural analysis and design. But even today they are frequently dealt with in an intuitive or qualitative way only. However, as already suggested 80 years ago, these uncertainties may be quantified by statistical and stochastic procedures. In this contribution it is attempted to shed light on some of the recent advances in the now established field of stochastic structural mechanics and also solicit ideas on possible future developments

Computational Stochastic Structural Analysis (COSSAN)- A Software Tool

The paper provides an overview of the current status of the COSSAN [1, 2] software developed at the Institute of Engineering Mechanics of the Leopold Franzens University, Innsbruck, Austria, EU. Two options provided by COSSAN are described: (1) A 'Stand Alone Tool Box' which is an event driven modular general purpose code and (2) the 'Third Party Communication Tools' which allow to employ deterministic Third Party (FE-) codes for stochastic analysis without the need to access and modify the third party source code. The Stand Alone Tool Box covers a fairly wide field of stochastic methods including various sampling techniques, random fields, fatigue analysis, reliability based optimization, random vibration, Monte Carlo simulation and FE-analysis. The Third Party Communication Tools are designed to extend existing deterministic FE-codes for considering uncertainties and consequently perform stochastic analyses using methods based on Monte Carlo procedures

Abstract Mechanics & Analysis ⋆

In this paper the need for a rational treatment of uncertainties in structural mechanics and analysis is reasoned. It is shown that the traditional deterministic conception can be eas-ily extended by applying statistical and probabilistic concepts. The so-called Monte Carlo simulation procedure is the key for those developments, as it allows the straightforward use of the currently used deterministic analysis procedures. A numerical example exemplifies the methodology. It is concluded that uncertainty anal-ysis may ensure robust predictions of variability, model verification, safety assessment, etc

STOCHASTIC ANALYSIS OF FATIGUE CRACK GROWTH

Due to cost and time limitations, experiments for fatigue crack growth are only feasible below certain cycle numbers. In this paper, it is investigated whether or not fatigue crack growth laws allow for an extrapolation of the statistics of the cycle number to reach a certain crack length from limited experimental data, and hence reduce costs

Computational Mechanics ON COMPUTATIONAL PROCEDURES FOR PROCESSING UNCERTAINTIES IN STRUCTURAL MECHANICS

A quantitative assessment of uncertainties in load, geometrical and material properties by using probabilistic and stochastic analysis is discussed. This allows a rational determination of the propagation of uncertainties through structural systems and ultimately reliability assessment

volume 2, pages 17–27, 2002. Past, Present & Future of Simulation − Based Structural Analysis

The Monte Carlo method -- which based on the game of chance -- was named after the famous Casino of Monte Carlo in Monaco. It was developed in the early fourtieth of the last century. For solving the so-called random neutron transport problems, i.e. diffusion in fissile materials. Already at an early stage of these investigations, the direct simulation was refined with certain variance -- reducing techniques. About the same time the methods and theories of structural safety and reliability have been developed, and it was until the late sixtieth and early seventieth, respectively, until Monte Carlo simulation (MCS) techniques have been introduced to this field. First for simulating random variables, and random processes, later on for random fields. It took more than another decade until variance reduction techniques were applied in this area as well. Efficient MCS procedures were considerably advanced through the availability of digital, high-speed computer. Currently the MCS procedure is applied to various aspects of analysis, design of structures, such as uncertainty, reliability, safety and other types of analyses. It proves to be the most versatile tool in structural analysis when taking into account uncertainties in load, material and geometrical parameters. Future developments of MCS techniques will encompass their use within user - friendly computer codes for analysis and design, mainly based on a modular basis for maintaining flexibility and parallel processing for reducing computational efforts

On Procedures for Reliability Assessment of Mechanical Systems and Structures

In this paper a brief overview of methods to assess the reliability of mechanical systems and structures is given. A selection of computational procedures, stochastic structural dynamics, stochastic fatigue crack growth, and reliability based optimization are discussed. It is shown that reliability based methods may form the basis for a rational decision making.