Seismic risk prioritisation schemes for reinforced concrete bridge portfolios

Abstract

A significant portion of the existing bridge inventory in Italy is decades old, requiring continuous maintenance and safety assessment approaches. Recent collapses of existing reinforced concrete bridges have piqued public interest, placing pressure on management agencies to define methodologies with which to prioritise asset maintenance and to effectively utilise their limited resources. When looking for decision variables to perform this prioritisation, seismic risk assessment metrics, such as average annual losses (AAL), are an appealing choice. However, obtaining this metric for a large bridge inventory is technically challenging and requires large amounts of information that are seldom available, promoting the development of practical approaches that can predict the relative priority of assets within a portfolio, based on processing simple indicators with acceptable accuracy. In this research, a case study of 617 bridges from the Italian road network was assessed considering state-of-the-art approaches to calculate total losses. The results were explored with data science techniques, identifying the main features that drive the relative importance of bridges in terms of AAL and using them as guidance to calibrate a simplified methodology, based on the recent Italian Guidelines for Bridge Safety Assessment. The proposed AAL-based modifications demonstrate a notable improvement in the definition of bridge assessment priorities, as well as providing further resolution in the classification for more efficient decision making.

Keywords:

• Bridges
• machine learning
• prioritization
• regional seismic risk
• road networks
• transportation infrastructure

Acknowledgements

The work presented in this paper has been developed within the framework of the project ‘Dipartimenti di Eccellenza’, funded by the Italian Ministry of Education, University and Research at IUSS Pavia. It has also received support from the INFRA-NAT project, co-funded by European Commission ECHO – Humanitarian Aid and Civil Protection (project reference: 783298 – INFRA-NAT – UCPM-2017-PP-AG).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Ministero dell’Istruzione, dell’Università e della Ricerca.