A better way to assess the earthquake safety of bridges

Comparison of (a) the conventional DoE method and (b) the AL-GPR scheme (the arrow in the figure indicates the order of data sampling). Copyright: Earthquake Engineering & Structural Dynamics (2024). DOI: 10.1002/eqe.4144

Researchers at McGill University have developed a more efficient way to assess the likelihood that a bridge will be damaged in an earthquake. This information could help authorities prioritize infrastructure development and improve emergency response plans.

In a study published in the journal Earthquake Engineering and Structural Dynamics, the researchers focused on a critical tool called an earthquake fragility model. Until now, creating these models has required hundreds of thousands of simulations, making the process slow and expensive. The new method, which combines statistical techniques and artificial intelligence, requires just 70 simulations.

Using advanced techniques, the researchers have created a smarter, faster way to assess earthquake risk. These new models can automatically adapt as new types of bridges are added to the study, ensuring up-to-date safety assessments.

The new models could be integrated into systems that create detailed risk maps, helping officials better plan emergency routes and prioritize which bridges need strengthening. This would be critical in earthquake-prone areas, enabling faster and more effective responses during crises.

“The findings are essential for protecting bridge infrastructure in seismic regions around the world,” said Yazhu Shi, lead author and assistant professor in the Department of Civil Engineering. “They will enable decision makers to identify emergency passage routes so that first responders can better prepare, plan and respond to the next major earthquake.”

The study results may also help determine how the performance of individual bridges affects the overall transportation network after an earthquake. This insight could help policymakers make informed decisions about how to enhance cities’ resilience to seismic threats.

For more information: Chunxiao Ning et al, Enabling efficient regional seismic fragility evaluation of multi-component bridge portfolios through Gaussian process regression and active learning, Earthquake Engineering & Structural Dynamics (2024). DOI: 10.1002/eqe.4144

Brought to you by McGill University

Citation: A Better Way to Assess Bridge Earthquake Safety (2024, September 9) Retrieved September 9, 2024 from https://techxplore.com/news/2024-09-bridges-earthquake-safety.html

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