BridgeFuncRecovery 0.1.0

A Python package to model post-earthquake functional recovery of bridges.

BridgeFuncRecovery

This project aims to probabilistically model the post-earthquake functional recovery of bridges. The source codes are programmed in Python.

Reference

[1] Wu, C., Burton, H., Zsarnóczay, A., Chen. S., Xie. Y., Terzić, V., Günay, S., Padgett, J., Mieler, and M., Almufti, I. (2025). Modeling Post-earthquake Functional Recovery of Bridges. Earthquake Spectra.

Prerequisites

Python: version 3.6 or above.

Necessary Python packages: copy, numpy, pandas, os, scipy, sys, shutil, pathlib, re, time, pickle

What is each file used for

main.py inputs user-specified parameters, and performs the entire analysis.

utils.py provides necessary auxiliary functions that is called from the main script main.py.

After running all cells in main.py, a pickle file Results.pkl is stored that records model output data.

result_anlaysis.py helps visualize the output data stored in Results.pkl.

User-specified inputs

Users must specify the following inputs:

• *IM_fixed
• num_span
• CompQty
• WorkerAllo_percrew
• Worker_Replace

The main function can be called using:

from BridgeFuncRecovery import run

# Example usage:
results = run(IM_fixed=..., num_span=..., CompQty=..., WorkerAllo_percrew=..., Worker_Replace=...)

This will return the result dictionary and save it to a Results.pkl

Analyzing the Results

The functions in result_analysis.py are used to interpret and visualize output data from the main analysis. Available functions include:

• plot_fs_initial(data)
• plot_fs_reopening(data)
• plot_total_impeding_ccdf(data)
• plot_total_repair_ccdf(data)
• print_impeding_medians(data)
• print_repair_durations(data)
• plot_repair_class_distribution_single(data, comp_name)
• plot_all_repair_class_distributions(data)
• plot_closed_lane_initial(data)
• show_all_results(data)

These functions can be called using:

from BridgeFuncRecovery import run, plot_repair_class_distribution_single
# Example usage:
# Run the analysis
results = run(IM_fixed=..., num_span=..., CompQty=..., WorkerAllo_percrew=..., Worker_Replace=...)

# Plot the Repair Class distribution for columns
plot_repair_class_distribution_single(results, 'Col')

This will save the results to Results.pkl and display a figure visualizing Repair Class (RC) distribution for columns.