pypbee 0.1.0

Performance-Based Earthquake Engineering toolkit

PyPBEE — Performance-Based Earthquake Engineering in Python

A modular, high-performance framework that lets researchers and engineers run the full Performance-Based Earthquake Engineering (PBEE) workflow — from site‑specific seismic‑hazard curves to demand, damage and (soon) loss metrics — entirely in Python.

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Key Capabilities

• End‑to‑end PBEE pipeline
  Probabilistic Seismic Hazard Analysis (PSHA), Ground‑Motion Selection (GMS), Non‑linear Time‑History Analysis (NLTHA), Probabilistic Seismic Demand Hazard Analysis (PSDemHA) and Probabilistic Seismic Damage Hazard Analysis (PSDamHA).
• Object‑oriented core, plug‑and‑play extensions
  Clean template‑method lifecycle (setup → run → wrap_up) and abstract entity classes (IM, EDP, DS, Structure) make it straightforward to add new intensity measures, demand parameters, fragilities, or analysis stages.
• Advanced Uncertainty Quantification
  Treat aleatory and epistemic sources (random FE parameters, parameter‑estimation uncertainty, model‑form alternatives) with MultivariateNataf, Mixture, Latin‑Hypercube sampling, etc.
• Finite‑element back‑ends out of the box
  Adapters for OpenSeesTcl and OpenSeesPy; extendable to Abaqus, Ansys, …
• Scales from laptop to cluster
  Local multi‑processing via pathos or embarrassingly‑parallel Slurm jobs on HPC/HTC systems with a one‑line comp_env switch.
• Built‑in visualisation
  Hazard curves, conditional spectra, demand / damage hazard surfaces, deaggregation plots — all ready for publication.

Installation

# clone the repo
git clone https://github.com/angshuman311/PyPBEE.git
cd PyPBEE

# Windows users can run the helper script
setup_venv.bat                 # Windows CMD / double‑click

⚠️ PyPBEE is research software; pinned dependency versions are in requirements.txt.

Quick‑start

from pypbee.structures   import OSB               # Ordinary Standard Bridge
from pypbee.intensity    import AvgSa
from pypbee.edp          import MaxColRebarStrain
from pypbee.analysis     import PrelimAnalysis, PSHA, GMS, NLTHA, PSDemHA, PSDamHA

# 1 ── define model‐space & site info ------------------------------------------------
model_params   = {...}          # dict of deterministic design vars
random_params  = {...}          # dict of {param: (dist, mean, std, ...)}
corr_matrix    = [...]          # numpy array
location_info  = {"lat": 37.7531, "lon": -121.1427, "region": "California"}

# 2 ── create entities ---------------------------------------------------------------
bridge   = OSB(model_params, random_params, corr_matrix, location_info)
im       = AvgSa(bridge)
edp_list = [MaxColRebarStrain(bridge, tag='col_rebar_strain')]

# 3 ── assemble analyses -------------------------------------------------------------
pre  = PrelimAnalysis(bridge)
psha = PSHA(im)
gms  = GMS(im)
nltha   = NLTHA(im, edp_list)
psdemha = PSDemHA(edp_list, im)
psdamha = PSDamHA([], im)       # add DS objects when available

# 4 ── run workflow ------------------------------------------------------------------
for a in (pre, psha, gms, nltha, psdemha, psdamha):
    a.setup()
    a.run()
    a.wrap_up()

Full, commented examples live in examples/.

Contributing

1. Fork the repo and create a feature branch.
2. Open a pull request; describe why your change matters.
3. Bug reports and feature requests are equally welcome — open a GitHub issue.

Acknowledgements

Development supported by Caltrans (65A0594, Task 2880), PEER Transportation Systems Research Program (Project #1147‑NCTRTE), and the Reissner Chair, UC San Diego. HPC tests used TACC resources.

License

This project is released under the MIT License.