Software Open Access
Probabilistic Estimation of Losses, Injuries, and Community resilience Under Natural disasters
What is it?
pelicun is a Python package that provides tools for assessment of damage and losses due to natural hazards. It uses a stochastic damage and loss model that is based on the methodology described in FEMA P58 (FEMA, 2012). While FEMA P58 aims to assess the seismic performance of a building, With
pelicun we provide a more versatile, hazard-agnostic tool that estimates losses for several types of assets in the built environment.
Detailed documentation of the available methods and their use is available at http://pelicun.readthedocs.io
What can I use it for?
pelicun quantifiies losses from an earthquake or hurricane scenario in the form of decision variables. This functionality is typically utilized for performance-based engineering and regional risk assessment. There are several steps of performance assessment that
pelcicun can help with:
Describe the joint distribution of asset (e.g. building) response. The response of a structure or other type of asset to an earthquake or hurricane wind is typically described by so-called engineering demand parameters (EDPs).
pelicun provides methods that take a finite number of EDP vectors and find a multivariate distribution that describes the joint distribution of EDP data well. You can control the type of target distribution, apply truncation limits and censor part of the data to consider detection limits in your analysis. Alternatively, you can choose to use your EDP vectors as-is without resampling from a fitted distribution.
Define the damage and loss model of a building. The component damage and loss data from the first two editions of FEMA P58 and the HAZUS earthquake and hurricane models for buildings are provided with
pelicun. This makes it easy to define building components without having to collect and provide all the data manually. The stochastic damage and loss model is designed to facilitate modeling correlations between several parameters of the damage and loss model.
Estimate component damages. Given a damage and loss model and the joint distribution of EDPs,
pelicun provides methods to estimate the amount of damaged components and the number of cases with collapse.
Estimate consequences. Using information about collapse and component damages, the following consequences can be estimated with the loss model: reconstruction cost and time, unsafe placarding (red tag), injuries and fatalities.
Why should I use it?
pandaslibraries to efficiently propagate uncertainties and provide detailed results quickly.
pelicunis tested after every commit. See the Travis-CI and Coveralls badges at the top for more info.
pelicunwith your approach. You do not need to share your extended version with the community, but if you are interested in doing so, contact us and we are more than happy to merge your version with the official release.
Major changes in v2.0:
Major changes in v1.2: