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This code is a Python implementation of the parallelized adaptive Bayesian Updating with Structural reliabilty methods. The methods were described in:
Simon, P., Schneider, R., Baeßler, M., Morgenthal, G., 2024. Parallelized adaptive Bayesian Updating with Structural reliability methods for inference of large engineering models (submitted for publication).
It is based upon the original Python implementation of the original adaptive Bayesian Updating with Structural reliability methods with Subset Simulation (aBUS-SuS) by the Engineering Risk Analysis (ERA) Group of Technische Universität München.
The package is installable from the Python Package Index (PyPI) using pip:
pip install parallel-abusUsage is exemplified in the corresponding GitHub project of this package.
Examples using this package are documented in the ./tests/ folder. The number of processes can be specified as a command line parameter, for example:
python ./tests/test_main_example_3_2DOF.py 5runs inference with parallel aBUS on 5 processes.
A more comprehensive example is presented in ./example/bayesian_inference.py. Here, an engineering model of a reinforced concrete beam including an OpenSees finite element model is updated. Details on this example are found in this contribution:
Simon, P., Schneider, R., Baeßler, M., Morgenthal, G., 2024. A Bayesian probabilistic framework for building models for structural health monitoring of structures subject to environmental variability. (submitted for publication).
This example requires amongst others the python package for OpenSees.
An easy way to get this example running is to install its dependencies via Poetry:
poetry installparallel-abus is distributed under the terms of the MIT license.