# 5. ExamplesΒΆ

The following are a few examples showing the usage of quoFEM. Video companions showing these examples are also provided for selected problems. The files for the examples are available on Github.

This example illustrates how quoFEM interacts with the Tcl interpreter for OpenSees. A simple forward propagation procedure is run to estimate the first and second central moments of a FE model's response, given the marginal distributions of various random parameters.

This example illustrates how quoFEM interacts with OpenSeesPy. A simple forward propagation procedure is run to estimate the first and second central moments of a FE model's response, given the marginal distributions of various random parameters.

This example illustrates how Python scripting can be used with quoFEM to express general mathematical models without the use of a dedicated finite element analysis engine.

In this example, a parameter estimation routine is used to estimate column stiffnesses of a simple steel frame, given data about its mode shapes and mass distribution.

A global sensitivity analysis is conducted with correlated random variables using the SimCenterUQ engine for sensitivity analysis.

In this example, Bayesian estimation is used to estimate the lateral story stiffnesses of the two stories of a simple steel frame, given data about its mode shapes and frequencies. The transitional Markov chain Monte Carlo algorithm is used to obtain samples from the posterior probability distribution of the lateral story stiffnesses

This example constructs a Gaussian process-based surrogate model for the response of a building structure given a ground motion time history. We are interested in the maximum inter-story drift/acceleration response determined in 14 structural parameters.

This example constructs a Gaussian process-based surrogate model for the mean and variance of peak building responses subjected to ten different ground motions. The 2nd floor response of a six-story shear building model is investigated.

In this example, the parameters of the STEEL02 material model in OpenSees are calibrated by Bayesian inference. Experimental data is passed in to quoFEM from an external file, and the output is the time-history of stress - a non-scalar response quantity.

In this example, the goal is to demonstrate the use of PLoM model method to predict the response of the example truss under the given load.

In this example, global sensitivity analysis is demonstrated for field QoI quantities. To gain efficiency dealing with high-dimension outputs, principal component analysis and probability model-based global sensitivity analysis (PCA-PSA) method is used in the analysis.

The Heteroscedastic Gaussian process model is trained based on partial replication strategy