Exposure knowledge impact on regional seismic risk assessment of bridge portfolios

The lack of structural information on existing bridges is a common problem faced by engineers when performing regional seismic risk assessment of large bridge portfolios. In most regions, the bridge inventory is composed of structures built over decades and detailed structural information of the existing configurations is difficult to obtain and can be expensive to survey. Most of the regional risk studies for bridges are done with incomplete exposure knowledge and usually rely on macro taxonomy-based approaches that average fragility information of assets with similar configurations. This leads to an unknown level of uncertainty in the results that is commonly not quantified or accurately communicated to the stakeholders. Accordingly, there is a need for a better understanding on how much uncertainty can be expected in results using such approaches, as well as for recommendations to those dealing with this type of project to define an appropriate required minimum knowledge of the inventory to obtain reasonable results. In this study, the seismic risk assessment of a portfolio of 617 bridges with complete structural information was performed and its results were used as a benchmark to quantify the expected uncertainty when considering different knowledge levels using a taxonomy-based approach as well as a machine learning model. The obtained results suggest that having detailed information on at least one third of the portfolio leads to a considerable reduction in uncertainty and that machine learning models can outperform traditional taxonomy-based methodologies when a sufficient level of knowledge of the inventory is available.

Automated summary

The lack of structural information on existing bridges poses a common challenge for engineers in regional seismic risk assessment. The reliance on incomplete exposure knowledge and macro taxonomy-based approaches in bridge risk studies results in unknown levels of uncertainty. This study demonstrates the importance of detailed structural information and suggests that machine learning models can outperform traditional approaches when sufficient inventory knowledge is available.