Teaching Gallery

The SimCenter has developed the course modules below for use in your course curriculums. Included in each module are: the target audience, learning objectives, research tools involved, class materials, instructions to instructors, and contacts for questions.

 

NOTE: This content is one of the topics in the UQ educational module series. UQ educational modules provide single-topic-focused lectures that can be plugged into an existing engineering course. Either SimCenter personnel can give lectures as guest lecturers or assist the instructors in developing tailored course materials. Other available topics include Bayesian updating, adaptive design of experiments in global surrogate modeling, and Bayesian model selection.

This course module provides an introduction to Global Sensitivity Analysis (GSA). Unlike local sensitivity analysis, where the variability of output is explored around a specific reference parameter value, GSA aims to evaluate the overall importance of each parameter in an engineering system by taking the full input space into account. Identification of the important parameters as well as the screening of trivial parameters is a fundamental task to any engineering analysis. Further, GSA can serve as a useful pre-analysis step to reduce the computational efforts in the main analysis, such as system optimization, system identification, model updating, and uncertainty analysis.

Target Audience: Graduate-level classes

  • Target class 1: Traditional engineering courses for which GSA can be useful. For example, classes that deal with model updating (GSA can assist in identifying important parameters to update) or classes that use computer simulations with multiple inputs of varying importance (such as performance-based earthquake engineering classes using nonlinear time history analysis).
  • Target class 2: Reliability, computational statistics courses in the engineering department. This audience will benefit from exploring uncertainty reduction using GSA.

NOTE: The contents and examples will be tailored to fit the level of each class. The materials for Target class 1 are designed to convey the concepts without UQ backgrounds. Therefore, the students do not require an understanding of random variables, probability density functions, or other UQ theories. Target class 2 will focus more on the UQ point of view both in theory (algorithms) and applications.

Learning Objectives:

  • Understand the concept of the global sensitivity index and perform efficient GSA
  • Interpret the GSA results with an understanding of their value and limitation
  • Discover the usage of GSA by investigating examples that use GSA to assist in solving various engineering problems, including optimization, parameter selection, and importance sampling

Tools Involved:

Example class materials for Target Class 1:

  • To be added

Example class materials for Target Class 2:

Instructions for Instructors:

  • Please contact us if you need assistance using this module in your course or are interested in collaborating to develop new course materials.

Contributors
Sang-ri Yi, UC Berkeley
Alexandros Taflanidis, University of Notre Dame
Ziqi Wang, UC Berkeley

Contact
Sang-ri Yi

Last Updated: February, 2023
Course questions:
simcenter_education@berkeley.edu

This assignment provides a high-level introduction to FEMA P-58 to structural engineering students. The starter package includes the performance model and demand data for the analysis of a 12-story reinforced concrete shear wall building at three intensities of ground shaking.

Students can use the SimCenter PBE Application to calculate the expected damages in the building and the consequences of those damages. The assignment has students process the results and guides them through the kinds of questions they can answer with this type of high-resolution analysis.

Target Audience:

Graduate students in Structural Engineering with basic knowledge of probabilities, earthquake engineering, and performance-based engineering.

Learning Objectives:

  • Describe the inputs required and the results of a FEMA P58 analysis.
  • Perform a FEMA P58 analysis in the SimCenter PBE Application using a pre-defined performance model and demand data.
  • Process the results of a FEMA P58 analysis and review the key performance indicators.

Tools Involved:

Introduction to FEMA P-58 Seismic Performance Assessment Course Materials:

Instructions for Instructors or TAs:

  • Introduction to SimCenter PBE
    Describes: i) how to download the files for the assignment; ii) how to load the inputs; iii) how to run the analysis iv) how to find the results in the working directory; and v) how to interpret the results. (Powerpoint)

Contributors
Adam Zsarnóczay, Stanford University

Contact
Adam Zsarnóczay

Last updated: February, 2023
Course questions: simcenter_education@berkeley

This module introduces OpenSeesPy for nonlinear analysis of structures using notebook scripting. The module prompts the student to simulate the nonlinear response of a single-degree-of-freedom structural member using three different nonlinear models: (1) plastic hinge model, (2) fiber-section lumped plasticity model, and (3) distributed plasticity model. Through this module, students familiarize themselves with OpenSeesPy and notebook scripting and develop an understanding of the capabilities and limitations of each modeling approach.

Target Audience:
Graduate students in Structural Engineering with basic knowledge of programing, linear and nonlinear structural analysis.

Learning Objectives:

  • Compare the most common models for nonlinear simulation of structural members.
  • Present the Open System for Earthquake Engineering Simulation (OpenSees).
  • Develop an OpenSees model using notebook scripting.

Tools Involved:

  • OpenSeesPy
  • Google Colab (or other installations for Jupyter notebooks). Google Colab is one of the most convenient ways to do notebook scripting on Python since it requires no local installations and uses free cloud computations resources.

Supplemental material:

Documents:

Instructions for Instructors or TAs: Please contact us if you need assistance using this module in your course or are interested in collaborating to develop new course materials.

Contributors
Greg Deierlein, SimCenter co-Director, Stanford University
Francisco Galvis, Project Engineer, Thornton Tomasetti

Contact
Francisco Galvis

Last updated: February, 2023
Course questions: simcenter_education@berkeley.edu

This module addresses the different questions that arise while calibrating a nonlinear structural model using experimental results. To this end, the students will use quoFEM to calibrate a concentrated plastic hinge model in OpenSeesPy of a rectangular concrete column tested by Soesianawati et al. (1989). The module guides the students in the execution of a parametric study, a deterministic calibration, and a Bayesian calibration or the parameters of the chosen model.

Target Audience:
Graduate students in Structural Engineering with basic knowledge of programing, linear and nonlinear structural analysis.

Learning Objectives:

  • Calibrate an OpenSees structural model based on test data.
  • Quantify the impact of the model calibrations based on modeling decision such as including or excluding axial load effects.
  • Estimate the uncertainty in the model parameters.

Tools Involved:

Supplemental material:

Documents:

Instructions for Instructors or TAs: Please contact us if you need assistance using this module in your course or are interested in collaborating to develop new course materials. The completed answer sheet and code is available from the instructor.

Contributors
Greg Deierlein, SimCenter co-Director, Stanford University
Francisco Galvis, Project Engineer, Thornton Tomasetti
Adam Zsarnóczay, Stanford University

Contact
Francisco Galvis
Adam Zsarnóczay

Last updated: February, 2023
Course questions: simcenter_education@berkeley.edu

This module shows how to use quoFEM to perform forward uncertainty propagation of modeling parameters in a nonlinear static analysis (pushover) of a 4-story building. The building in this module is simulated in OpenSees tcl interpreter to expose the students to this alternative approach for structural simulations with OpenSees. The student will evaluate the seismic performance of this frame following the ASCE/SEI 41 provisions, quantify the uncertainty in the pushover curve for different scopes of the experimental campaign to collect data of the frame, and provide a final recommendation of the experimental campaign scope.

Target Audience:
Graduate students in Structural Engineering with basic knowledge of programing, linear and nonlinear structural analysis.

Learning Objectives:

  • Introduce the concept of nonlinear static analysis (NSA).
  • Describe the general computational workflow for NSA.
  • Quantify uncertainty in the nonlinear response of structures.

Tools Involved:

Supplemental material:

Documents:

Instructions for Instructors or TAs: Please contact us if you need assistance using this module in your course or are interested in collaborating to develop new course materials. The completed answer sheet and code is available from the instructor.

Contributors
Greg Deierlein, SimCenter co-Director, Stanford University
Francisco Galvis, Project Engineer, Thornton Tomasetti

Contact
Francisco Galvis

Last updated: February, 2023
Course questions: simcenter_education@berkeley.edu

This module introduces EE-UQ as a tool to perform nonlinear response history analyses (NLRHA) of structures using the same 4-story building in the Uncertainty in Nonlinear Static Analysis of Structures Module. The students will select ground motions to match a target spectrum and perform NLRHA using the high-performance computing resources at DesignSafe. Finally, the students will have the opportunity to interpret the response of the frame based on the NLRHA results.

Target Audience:
Graduate students in Structural Engineering with basic knowledge of programing, linear and nonlinear structural analysis.

Learning Objectives:

  • Introduce the concept of nonlinear response history analysis (NLRHA).
  • Describe the general computational workflow for NLRHA.
  • Learn the basic requirements for ground motion selection and scaling.

Tools Involved:

Supplemental material:

Documents:

Instructions for Instructors or TAs: Please contact us if you need assistance using this module in your course or are interested in collaborating to develop new course materials. The completed answer sheet and code is available from the instructor.

Contributors
Greg Deierlein, SimCenter co-Director, Stanford University
Francisco Galvis, Project Engineer, Thornton Tomasetti
Kuanshi Zhong, Assistant Professor, University of Cincinnati

Contact
Francisco Galvis
Kuanshi Zhong

Last updated: February, 2023
Course questions: simcenter_education@berkeley.edu

This module provides an introduction to i) Bayesian updating for model calibration and quantification of the uncertainty in estimated model parameters and ii) the use of the SimCenter’s quoFEM tool to solve this computational problem.

Target Audience:

Graduate students and upper-level undergraduate students interested in solving system identification, parameter identification, reliability analysis, risk analysis, and model calibration and validation problems, where model updating based on data is important or required.

Learning Objectives:

  • Learn how to set up a Bayesian parameter estimation problem for an engineering system.
  • Learn how to use quoFEM to solve Bayesian parameter estimation problems using computational models.
  • Gain experience applying Bayesian parameter estimation for engineering systems using quoFEM through pertinent examples.

Tools Involved:

Supplemental material:

Documents:

Instructions for Instructors or TAs: Please contact us if you need assistance using this module in your course or are interested in collaborating to develop new course materials. The completed answer sheet and code is available from the instructor.

Contributors
Aakash Bangalore Satish, UC Berkeley
Joel P. Conte, UC Berkeley

Contact
Aakash Bangalore Satish

Last Updated: April, 2024
Course questions:
simcenter_education@berkeley.edu