Structures Congress 2018
Application of Statistical Learning Models for Efficient Seismic Risk Assessment of Large Property Portfolios
Publication: Structures Congress 2018: Buildings and Disaster Management
ABSTRACT
Seismic risk assessments are essential for the property and casualty insurance and many public entities at the national and local levels; however, comprehensive studies for large portfolios and in seismically active regions often become time-consuming processes and require significant computational resources to run the required simulations. This research introduces surrogate models which are developed by random forests, a class of nonlinear statistical learning algorithms, to significantly reduce the computational requirements in exchange for manageable errors in predicting the portfolio losses. To demonstrate the application, a portfolio consisting of four different building classes is simulated in OpenQuake, an open-source platform for seismic risk analysis. The developed surrogate model is shown to save close to 70% of the computation time and predict the portfolio losses for small to mid-range events with small errors but underestimates the values for very large events. A similar method can be applied to develop parametric solutions.
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Published In
Structures Congress 2018: Buildings and Disaster Management
Pages: 192 - 200
Editor: James Gregory Soules, 1CB&I
ISBN (Online): 978-0-7844-8132-5
Copyright
© 2018 American Society of Civil Engineers.
History
Published online: Apr 17, 2018
Published in print: Apr 17, 2018
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