Resources

covered subject areas

STRUREL (4 entries)

Expand your knowledge through our resources. Try our online tools and start reading our articles about probabilistic modeling, machine learning, risk assessment, uncertainty quantification and other fundamental topics. Here are the resources you need to get started dealing with uncertainties that arise in your day-to-day projects.

Reliability analysis

Why to avoid the coefficient of variation in the context of a Monte Carlo simulation?

The distribution quantifying the uncertainty about the probability of failure can be highly skewed, even for a large number of samples. The coefficient of variation is easier to interprete for symmetric distributions.

Probabilistic modeling

What is the difference between credible and confidence intervals?

Credible intervals express our belief that the true underlying value is contained within the interval conditional on the conducted simulation run. Contrary to that, confidence intervals are only meaningful for a large number of repeated simulation runs.

STRUREL

How to employ Monte Carlo simulation in COMREL?

After a brief overview of Monte Carlo simulation (MCS) for structural reliability, we explain the details of conducting a MCS in COMREL.

Reliability analysis

How to interpret the outcome of a Monte Carlo simulation if no failures occurred?

Even if Monte Carlo simulation returns not a single sample in the failure domain, we can still quantify the uncertainty about whether a specified target reliability level is maintained.

STRUREL

What is STRUREL?

STRUREL is a collection of software modules for probabilistic modeling in structural engineering. It offers state-of-the-art numerical methods for structural reliability analysis.

STRUREL

How to employ a MATLAB limit state function in STRUREL?

Integrating a MATLAB model into a reliability analysis is very straightforward in STRUREL. We explain the basic steps using a simple toy example.

STRUREL

How to employ a Python limit state function in STRUREL?

Integrating a Python model into a reliability analysis is very straightforward in STRUREL. We explain the basic steps using a simple toy example.

Reliability analysis

How to assess the uncertainty in the outcome of a Monte Carlo simulation?

We explain a Bayesian post-processing step for MCS. It is ideally suited to quantify the uncertainty and to evaluate credible intervals for the probability of failure.

Reliability analysis

How large is the statistical uncertainty in the estimate of a Monte Carlo simulation?

For a given probability of failure, the variance and coefficient of variation of the Monte Carlo estimate can be evaluated analytically. From this, the total number of samples required to maintain a target coefficient of variation can be deduced.

Reliability analysis

Why is Monte Carlo simulation so robust?

Monte Carlo simulation is a very robust structural reliability method because its performance depends solely on the total number of samples and the underlying probability of failure.

covered subject areas

Reliability analysis (16 entries)

Sensitivity analysis (11 entries)

Probabilistic modeling (10 entries)

STRUREL (4 entries)

Machine learning (2 entries)

Eracons GmbH

info@eracons.com
+49 (0)89 21709083