Exceedance Probabilities of MDOF-Systems Under Stochastic Excitation

1997 ◽  
Vol 50 (11S) ◽  
pp. S168-S173 ◽  
Author(s):  
H. J. Pradlwarter ◽  
G. I. Schue¨ller
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Numerical Procedure ◽  
Random Excitation ◽  
Stochastic Excitation ◽  
First Passage ◽  
Simulation Procedure ◽  
Mdof Systems ◽  
Low Probability ◽  
Probability Domain

A numerical procedure of evaluating the exceedance probabilities of MDOF-systems under non-stationary random excitation is presented. The method is based on a newly developed Controlled Monte Carlo simulation procedure applicable to dynamical systems. It uses “Double and Clump” to assess the low probability domain and employs further intermediate thresholds to increase the efficiency of MCS for estimating first passage probabilities. Applied to a hysteretic type of MDOF-system, the method shows good results when compared with direct MCS.

Structural Modal Multifurcation With Internal Resonance: Part 2—Stochastic Approach

1993 ◽  
Vol 115 (2) ◽  
pp. 193-201 ◽  
Author(s):  
R. A. Ibrahim ◽  
B. H. Lee ◽  
A. A. Afaneh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Axial Load ◽  
Random Excitation ◽  
Stochastic Bifurcation ◽  
Density Level ◽  
Analytical Treatment ◽  
Asymmetric Mode ◽  
Non Gaussian ◽  
Gaussian Closure

Stochastic bifurcation in moments of a clamped-clamped beam response to a wide band random excitation is investigated analytically, numerically, and experimentally. The nonlinear response is represented by the first three normal modes. The response statistics are examined in the neighborhood of a critical static axial load where the normal mode frequencies are commensurable. The analytical treatment includes Gaussian and non-Gaussian closures. The Gaussian closure fails to predict bifurcation of asymmetric modes. Both non-Gaussian closure and numerical simulation yield bifurcation boundaries in terms of the axial load, excitation spectral density level, and damping ratios. The results of both methods are in good agreement only for symmetric response characteristics. In the neighborhood of the critical bifurcation parameter the Monte Carlo simulation yields strong nonstationary mean square response for the asymmetric mode which is not directly excited. Experimental and Monte Carlo simulation exhibit nonlinear features including a shift of the resonance peak in the response spectra as the excitation level increases. The observed shift is associated with a widening effect in the response bandwidth.

scholarly journals A CONDITIONAL STOCHASTIC PROJECTION METHOD APPLIED TO A PARAMETRIC VIBRATIONS PROBLEM

2014 ◽  
Vol 20 (6) ◽  
pp. 810-818 ◽  
Author(s):  
Wlodzimierz Brzakala ◽  
Aneta Herbut
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Polynomial Chaos ◽  
Excitation Frequency ◽  
Finite Sequence ◽  
Excitation Amplitude ◽  
Random Excitation ◽  
Parametric Vibrations ◽  
Representative Points ◽  
Cable Stayed Bridges

Parametric vibrations can be observed in cable-stayed bridges due to periodic excitations caused by a deck or a pylon. The vibrations are described by an ordinary differential equation with periodic coefficients. The paper focuses on random excitations, i.e. on the excitation amplitude and the excitation frequency which are two random variables. The excitation frequency ωL is discretized to a finite sequence of representative points, ωL,i Therefore, the problem is (conditionally) formulated and solved as a one-dimensional polynomial chaos expansion generated by the random excitation amplitude. The presented numerical analysis is focused on a real situation for which the problem of parametric resonance was observed (a cable of the Ben-Ahin bridge). The results obtained by the use of the conditional polynomial chaos approximations are compared with the ones based on the Monte Carlo simulation (truly two-dimensional, not conditional one). The convergence of both methods is discussed. It is found that the conditional polynomial chaos can yield a better convergence then the Monte Carlo simulation, especially if resonant vibrations are probable.

Risk assessment of construction projects using Monte Carlo simulation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abroon Qazi ◽  
Mecit Can Emre Simsekler
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Attitude ◽  
High Impact ◽  
Risk Exposure ◽  
Risk Matrix ◽  
Content Type ◽  
Simulation Based ◽  
Project Risks ◽  
Low Probability

PurposeThis paper aims to develop a process for prioritizing project risks that integrates the decision-maker's risk attitude, uncertainty about risks both in terms of the associated probability and impact ratings, and correlations across risk assessments.Design/methodology/approachThis paper adopts a Monte Carlo Simulation-based approach to capture the uncertainty associated with project risks. Risks are prioritized based on their relative expected utility values. The proposed process is operationalized through a real application in the construction industry.FindingsThe proposed process helped in identifying low-probability, high-impact risks that were overlooked in the conventional risk matrix-based prioritization scheme. While considering the expected risk exposure of individual risks, none of the risks were located in the high-risk exposure zone; however, the proposed Monte Carlo Simulation-based approach revealed risks with a high probability of occurrence in the high-risk exposure zone. Using the expected utility-based approach alone in prioritizing risks may lead to ignoring few critical risks, which can only be captured through a rigorous simulation-based approach.Originality/valueMonte Carlo Simulation has been used to aggregate the risk matrix-based data and disaggregate and map the resulting risk profiles with underlying distributions. The proposed process supported risk prioritization based on the decision-maker's risk attitude and identified low-probability, high-impact risks and high-probability, high-impact risks.

Assessing Wear Evolutions in Railway Wheelsets Using a Survival Modeling Approach

2021 ◽  
Author(s):  
Guilerme A. C. Caldeira ◽  
JoaquimAP Braga ◽  
António R. Andrade
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle ◽  
Condition Based Maintenance ◽  
Monitoring Systems ◽  
Simulation Procedure ◽  
Condition Monitoring Systems ◽  
Survival Modeling ◽  
Wheel Profile ◽  
Independence Of Events

Abstract The present paper provides a method to predict maintenance needs for the railway wheelsets by modeling the wear out affecting the wheelsets during its life cycle using survival analysis. Wear variations of wheel profiles are discretized and modelled through a censored survival approach, which is appropriate for modeling wheel profile degradation using real operation data from the condition monitoring systems that currently exist in railway companies. Several parametric distributions for the wear variations are modeled and the behavior of the selected ones is analyzed and compared with wear trajectories computed by a Monte Carlo simulation procedure. This procedure aims to test the independence of events by adding small fractions of wear to reach larger wear values. The results show that the independence of wear events is not true for all the established events, but it is confirmed for small wear values. Overall, the proposed framework is developed in such a way that the outputs can be used to support predictions in condition-based maintenance models and to optimize the maintenance of wheelsets.

Some Observations on the Random Response of an Elasto-Plastic System

1988 ◽  
Vol 55 (4) ◽  
pp. 911-917 ◽  
Author(s):  
L. G. Paparizos ◽  
W. D. Iwan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Brownian Motion ◽  
Markov Process ◽  
Practical Interest ◽  
Random Excitation ◽  
Process Models ◽  
Mean Square ◽  
Random Response ◽  
Monte Carlo Simulation Study

The nature of the response of strongly yielding systems subjected to random excitation, is examined. Special attention is given to the drift response, defined as the sum of yield increments associated with inelastic response. Based on the properties of discrete Markov process models of the yield increment process, it is suggested that for many cases of practical interest, the drift can be considered as a Brownian motion. The approximate Gaussian distribution and the linearly divergent mean square value of the process, as well as an expression for the probability distribution of the peak drift response, are obtained. The validation of these properties is accomplished by means of a Monte Carlo simulation study.

Deriving Proven Oil Reserves Comments on the Monte Carlo Simulation Procedure

1993 ◽  
Vol 11 (1) ◽  
pp. 62-65
Author(s):  
Mark Wallace
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Basic Principles ◽  
Oil Reserves ◽  
Confidence Levels ◽  
Simulation Procedure ◽  
Probabilistic Distribution ◽  
Reservoir Description ◽  
Definition Of

The definition of reserves categories is frequently related directly back to the probabilistic distribution of reserves in the field. Most developments are planned around the P50 or “most likely” expectation for the field a level which incorporates the Proven plus Probable categories. The Proven category is usually backed out from the resulting reserves distribution by assuming an arbitrary P90 or P80 value, similarly upside or the Reserves including the Possible category are allocated a P20 or P10 value. This approach provides an “accepted” range to the reserves but is essentially reliant upon applying a range to a set of deterministric parameters. This approach assumes the basic principles of reservoir description are correct and can be applied at all confidence levels (P90-P10). In complex reservoirs this is less of a valid assumption, and running deterministic cases using pessimistic and optimistic data interpretations is the realistic way to determine the reserves range for the field.

Analysis of the Household Distribution of Trachoma in a Gambian Village Using a Monte Carlo Simulation Procedure

1989 ◽  
Vol 18 (4) ◽  
pp. 944-951 ◽  
Author(s):  
R BAILEY ◽  
C OSMOND ◽  
D C W MABEY ◽  
H C WHITTLE ◽  
M E WARD
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Procedure
Sign in / Sign up

Export Citation Format

Share Document