Below are two detailed examples of how you would use the two tools. This helps you finalize your understanding on when to use each tool:

Seismic Response: you have the model of a building and want to quantify its response in statistical terms: estimate the mean response as well as a reasonable estimate of the range of possible response --> use EE-UQ
Model Calibration: you have the model of a building and to to calibrate the model properties to match a measured response --> use quoFEM

(there is no guarantee that the OpenSees Scripts generated by ChatGPT are correct -- they are intended as a guide)

Great! Below is a sample dataset you can use to try out EE-UQ and quoFEM for a 5-story reinforced concrete (RC) building. I'll provide seismic input data for EE-UQ and experimental calibration data for quoFEM so you can test both tools.

📌 Sample Dataset for EE-UQ

🎯 Goal: Simulate earthquake response of a 5-story RC building under ground motion uncertainty.

🛠 Required Inputs for EE-UQ

Finite Element Model (FEM) (Example OpenSees model in Tcl format)
Ground Motion Records (From PEER NGA-West2)
Uncertain Parameters (Concrete & steel properties, damping)
Simulation Settings (Monte Carlo sampling)

📌 1. Finite Element Model (FEM) - OpenSees Tcl File

model BasicBuilder -ndm 3 -ndf 6

# Define Nodes
for {set i 1} {$i <= 5} {incr i} {
    node $i 0.0 [expr $i*3.0] 0.0
}
node 0 0.0 0.0 0.0

# Define Constraints
fix 0 1 1 1 1 1 1

# Define Material Properties (Uncertain in EE-UQ)
set fc -4000  ; # Concrete compressive strength (psi)
set fy 60000  ; # Steel yield strength (psi)
set dampingRatio 0.05  ; # 5% Damping

# Define Elements (Using an Elastic Beam-Column Approximation)
element elasticBeamColumn 1 0 1 10 29000000 1000 1
element elasticBeamColumn 2 1 2 10 29000000 1000 1
element elasticBeamColumn 3 2 3 10 29000000 1000 1
element elasticBeamColumn 4 3 4 10 29000000 1000 1
element elasticBeamColumn 5 4 5 10 29000000 1000 1

# Apply Gravity Load
pattern Plain 1 Linear {
    load 5 0.0 -10.0 0.0 0.0 0.0 0.0
}

# Define Time History Analysis
system BandGeneral
constraints Plain
numberer RCM
algorithm Linear
integrator LoadControl 1.0
analysis Static
analyze 1

# Define Dynamic Analysis Parameters
set dt 0.02
timeSeries Path 2 -filePath ground_motion.txt -dt $dt -factor 1.0
pattern UniformExcitation 2 1 -accel 2

# Run Time History Analysis
analysis Transient
analyze 1000 $dt

# Record Displacement Response at Roof Level
recorder Node -file disp.txt -time -node 5 -dof 2 disp

📌 Save this as model.tcl and upload it into EE-UQ.

📌 2. Ground Motion Data (Example: Northridge Earthquake)

🔹 File: ground_motion.txt (Upload to EE-UQ)

0.00   0.000
0.02   0.015
0.04   -0.025
0.06   0.030
0.08   -0.040
...

📌 This is a sample time history acceleration record. You can get real data from PEER NGA-West2.

📌 3. Uncertainty Parameters for EE-UQ

Parameter	Mean Value	Standard Deviation	Distribution
Concrete Strength (fc)	-4000 psi	500 psi	Normal
Steel Yield Strength (fy)	60000 psi	5000 psi	Normal
Damping Ratio (ξ)	5%	1%	Normal

📌 4. Expected Output from EE-UQ

After running Monte Carlo simulations in EE-UQ, you'll get:

Roof displacement distribution (e.g., mean = 1.2 in, std = 0.2 in).
Story drift ratio variations.
Base shear force variations.

📌 Sample Dataset for quoFEM

🎯 Goal: Calibrate material properties (fc, fy, damping) using experimental data.

🛠 Required Inputs for quoFEM

Finite Element Model (Same as EE-UQ)
Experimental Data (Measured seismic response)
Uncertain Material Properties
Calibration Method (Bayesian Calibration)

📌 1. Experimental Data for Calibration

Story Level	Measured Displacement (in)
Roof (5th floor)	1.5
4th Floor	1.2
3rd Floor	0.9
2nd Floor	0.5
1st Floor	0.2

📌 Save as exp_data.csv and upload to quoFEM.

📌 2. Uncertain Material Properties for Calibration

Parameter	Initial Guess	Standard Deviation
Concrete Strength (fc)	-4000 psi	500 psi
Steel Yield Strength (fy)	60000 psi	5000 psi
Damping Ratio (ξ)	5%	1%

📌 3. Bayesian Calibration Settings

Algorithm: Markov Chain Monte Carlo (MCMC)
Samples: 500
Error Model: Gaussian

📌 4. Expected Output from quoFEM

After calibration, quoFEM will return:

Parameter	Initial Guess	Calibrated Value
Concrete Strength (fc)	-4000 psi	-4200 psi
Steel Yield Strength (fy)	60000 psi	58500 psi
Damping Ratio (ξ)	5%	4.8%

📌 The calibrated model will match the experimental data more closely.

🚀 Summary of EE-UQ vs. quoFEM with Sample Data

Feature	EE-UQ (Seismic Response)	quoFEM (Model Calibration)
Goal	Analyze earthquake effects	Improve model accuracy
Inputs	Ground motion, material uncertainties	Experimental data, material uncertainties
Output	Roof displacement, drift, base shear	Adjusted material properties (fc, fy, damping)
Analysis Type	Monte Carlo simulation	Bayesian Calibration
Typical Use Case	Earthquake safety assessment	Fine-tuning structural models

🚀 Next Steps

🔹 Want to Try EE-UQ?

Download EE-UQ from NHERI SimCenter.
Upload the model.tcl file.
Upload ground_motion.txt.
Set uncertainty parameters.
Run Monte Carlo simulations.

🔹 Want to Try quoFEM?

Download quoFEM from NHERI SimCenter.
Upload the model.tcl file.
Upload exp_data.csv for calibration.
Define uncertain parameters.
Run Bayesian Calibration.

Would you like help with interpreting results or setting up custom analysis cases? 😊

An example of using EE-UQ and quoFEM for a building structure.