The assessment of probabilistic seismic risk using ground-motion simulations via a Monte Carlo approach

Rudman, Archie and Douglas, John and Tubaldi, Enrico (2024) The assessment of probabilistic seismic risk using ground-motion simulations via a Monte Carlo approach. Natural Hazards. ISSN 1573-0840 (https://doi.org/10.1007/s11069-024-06497-1)

[thumbnail of Rudman-etal-NH-2024-The-assessment-of-probabilistic-seismic-risk-using-ground-motion]
Preview
 Text. Filename: Rudman-etal-NH-2024-The-assessment-of-probabilistic-seismic-risk-using-ground-motion.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (3MB)| Preview

Abstract

Accurately characterizing ground motions is crucial for estimating probabilistic seismic hazard and risk. The growing number of ground-motion models, and increased use of simulations in hazard and risk assessments, warrants a comparison between the different techniques available to predict ground motions. This research aims at investigating how the use of different ground-motion models can affect seismic hazard and risk estimates. For this purpose, a case study is considered with a circular seismic source zone and two line sources. A stochastic ground-motion model is used within a Monte Carlo analysis to create a benchmark hazard output. This approach allows the generation of many records, helping to capture details of the ground-motion median and variability, which a ground motion prediction equation may fail to properly model. A variety of ground-motion models are fitted to the simulated ground motion data, with fixed and magnitude-dependant standard deviations (sigmas) considered. These include classic ground motion prediction equations (with basic and more complex functional forms), and a model using an artificial neural network. Hazard is estimated from these models and then we extend the approach to a risk assessment for an inelastic single-degree-of-freedom-system. Only the artificial neural network produces accurate hazard results below an annual frequency of exceedance of 1 × 10 –3 years −1. This has a direct impact on risk estimates—with ground motions from large, close-to-site events having more influence on results than expected. Finally, an alternative to ground-motion modelling is explored through an observational-based hazard assessment which uses recorded strong-motions to directly quantify hazard.

CORE (COnnecting REpositories)