scholarly journals Risk Analysis of Structures in Presence of Stochastic Fields of Deterioration: Flowchart for Coupling Inspection Results and Structural Reliability

2009 ◽  
Vol 9 (1) ◽  
pp. 67-78 ◽  
Author(s):  
F Schoefs
Author(s):  
Sviatoslav A. Timashev

The paper considers the safety problem for large potentially dangerous systems (LPDS). Disruption of their normal operations may lead to casualties, ecological and property damage. Solution to the above problem is sought in the framework of risk control of LPDS during their normal operation, based on the principle of preventive actions. Risk is described as the product of conditional probability of failure and the overall consequences of such failure. Methods of brining down risk analysis problems to reliability problems are presented. They are based on the following: assessments of “cost of life” (as economic equivalent of casualty); simultaneous optimization of the LPDS and its safety subsystem (expansion of the object of optimization). Such an approach allows unification and merging of structural reliability theory and probabilistic risk analysis. A quantitative method of damage size (the first component of risk) assessment is described, based on computer modeling of a full group of scenarios of a structural failure developing into a full blown LPDS catastrophe. As a result of modeling, the destruction zones and the character, size and probabilities of all kinds of damage (casualties, ecological damage, loss of property) are assessed. It is proposed, as the main method of securing LPDS integrity and safety, to equip each LPDS with suitable monitoring/inspection/maintenance systems, designed as an instrument for controlling the second component of risk (conditional probability of failure), on the basis of a three-level (warning-alarm-failure) control policy. In the outlined format maintenance/repair is considered as optimal control of random degradation and renewal functions, interaction of which forms a certain regeneration process. Analysis of this process allows defining the optimal triggering levels of deterioration parameters or risk that minimize total expenditures of LPDS performance while ensuring its safety. The problem formulated above naturally embodies all existing maintenance methods (based on admissible performance time, rate of failure and on actual and prognosed system condition). Further, the problem of optimal cessation of performance is solved. It allows convoluting a multi-parameter problem into a one-parameter problem and defining the ultimate permissible level of conditional probability of failure. The described methods of risk analysis and control were used in residual lifetime monitoring systems for oil pumping aggregates and for main oil pipe line segments repair prioritization.


2000 ◽  
Vol 122 (3) ◽  
pp. 181-187 ◽  
Author(s):  
Wenche K. Rettedal ◽  
Terje Aven ◽  
Ove T. Gudmestad

This paper concerns itself with the integration of QRA (quantitative risk analysis) and SRA (structural reliability analysis) methods. For simplicity, we will use the term SRA instead of SRA methods in the paper. The Bayesian (subjective) approach seems to be the most appropriate framework for such integrated analyses. It may, however, not be clear to all what the Bayesian approach really means. There exists alternative Bayesian approaches, and the integration of SRA and QRA is very much dependent on what the basis is. The purpose of this paper is to present two marine operation examples, implementing two different Bayesian approaches: the “classical Bayesian approach” and the “fully Bayesian approach.” Following the classical Bayesian approach, we estimate a true, objective risk, whereas in the fully Bayesian approach, risk is a way of expressing uncertainty about future observable quantities. In both examples, one initial accidental event is investigated by using a fault tree and by integrating SRA into this fault tree. We conclude that the most suitable framework for integrating SRA and QRA is to adopt the “fully Bayesian approach.” [S0892-7219(00)00703-2]


1993 ◽  
Author(s):  
A.J. Adams ◽  
S.H.L. Parfitt ◽  
T.B. Reeves ◽  
J.L. Thorogood

2013 ◽  
Author(s):  
Eric J. Tuegel ◽  
Robert P. Bell ◽  
Alan P. Berens ◽  
Thomas Brussat ◽  
Joseph W. Cardinal ◽  
...  

Author(s):  
Richard Dawson ◽  
Jim Hall

Complex civil infrastructure systems are typically exposed to random loadings and have a large number of possible failure modes, which often exhibit spatially and temporally variable consequences. Monte Carlo (level III) reliability methods are attractive because of their flexibility and robustness, yet computational expense may be prohibitive, in which case variance reduction methods are required. In the importance sampling methodology presented here, the joint probability distribution of the loading variables is sampled according to the contribution that a given region in the joint space makes to risk, rather than according to probability of failure, which is the conventional importance sampling criterion in structural reliability analysis. Results from simulations are used to intermittently update the importance sampling density function based on the evaluations of the (initially unknown) risk function. The methodology is demonstrated on a propped cantilever beam system and then on a real coastal dike infrastructure system in the UK. The case study demonstrates that risk can be a complex function of loadings, the resistance and interactions of system components and the spatially variable damage associated with different modes of system failure. The methodology is applicable in general to Monte Carlo risk analysis of systems, but it is likely to be most beneficial where consequences of failure are a nonlinear function of load and where system simulation requires significant computational resources.


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