The Need for Structural Health Monitoring in the San Francisco Bay Area

The San Francisco Bay Area faces one of the highest seismic risks in the United States. Structural Health Monitoring offers a vital tool to protect lives, infrastructure, and economic resilience in the face of inevitable earthquakes.

Introduction

The San Francisco Bay Area is one of the most seismically active and densely urbanized regions in the United States. Situated along the San Andreas and Hayward faults, the region faces a well-documented likelihood of future major earthquakes. The 2023 United States National Seismic Hazard Model (NSHM) identifies the Bay Area as having among the highest probabilities nationwide for damaging ground shaking within the next century (Petersen et al., 2024). This seismic risk, coupled with rapid urban densification and an aging building stock, underscores the urgent need for systematic Structural Health Monitoring (SHM) to safeguard lives, infrastructure, and economic activity.

Seismic Hazard in the Bay Area

The Bay Area’s hazard profile is shaped by its complex tectonic environment and history of large earthquakes, including the 1906 San Francisco event and the 1989 Loma Prieta earthquake. The 2023 NSHM integrates new data on earthquake rupture forecasts, ground motion models, and site amplification, showing particularly high hazard in California’s coastal urban centers, including San Francisco and Los Angeles (Petersen et al., 2024). Geotechnical conditions add further risk: much of the Bay shoreline consists of artificial fill, mud, and clay with high liquefaction potential, where earthquake shaking can lead to severe ground failure (Fuller et al., 2018).

Vulnerabilities in the Built Environment

Although California has made progress in strengthening critical lifelines such as hospitals, bridges, and transportation corridors, the broader building stock remains vulnerable. Current building codes are designed primarily to prevent collapse, providing structures with approximately a 90% probability of avoiding total failure during severe shaking. However, codes do not require that buildings remain functional after an earthquake. As a result, while collapse risk may be limited, significant numbers of structures may still be rendered unusable due to nonstructural damage to plumbing, elevators, or internal systems (Fuller et al., 2018).

Historical lessons also highlight potential weaknesses in certain construction practices. For example, welded steel moment-frame buildings erected prior to the mid-1990s have been found to contain a critical design flaw that may substantially elevate their risk of fracture during extreme shaking. A study by the U.S. Geological Survey identified dozens of such structures in downtown San Francisco, many of them high-rise commercial or residential towers built between the 1960s and early 1990s (Fuller, 2018). While retrofitting efforts have been initiated in some jurisdictions, the scale of the problem remains considerable.

The Role of Structural Health Monitoring

Structural Health Monitoring provides a pathway to mitigate these vulnerabilities. SHM systems employ continuous sensing of structural response — through accelerometers, strain gauges, or distributed networks of low-cost devices — to track changes in stiffness, frequency content, and other dynamic properties. The benefits of SHM in the Bay Area context include:

  • Rapid Post-Event Assessment: SHM enables real-time identification of damaged or compromised structures following an earthquake, supporting emergency response and prioritization.
  • Enhanced Resilience Planning: Long-term monitoring helps detect gradual degradation due to repeated minor events or environmental factors, informing retrofit strategies.
  • Economic Risk Reduction: By distinguishing between safe and unsafe buildings, SHM can reduce unnecessary evacuations, downtime, and economic disruption after earthquakes.
  • Public Safety and Confidence: Transparent monitoring and reporting foster trust in the safety of urban infrastructure.

Conclusion

The San Francisco Bay Area’s unique combination of high seismic hazard, concentrated urban development, and an aging building inventory makes it especially vulnerable to future earthquakes. Current codes and preparedness measures, while essential, are insufficient to guarantee post-event functionality. Integrating Structural Health Monitoring across the region’s built environment offers a critical step toward seismic resilience, enabling both immediate safety assurance and long-term risk reduction.

References

  1. Fuller, T., Singhvi, A., & Williams, J. (2018). San Francisco’s Big Seismic Gamble. The New York Times. Retrieved from https://www.nytimes.com/interactive/2018/04/17/us/san-francisco-earthquake-seismic-gamble.html
  2. Fuller, T. (2018). At Risk in a Big Quake: 39 of San Francisco’s Top High Rises. The New York Times. Retrieved from https://www.nytimes.com/2018/06/14/us/california-earthquakes-high-rises.html
  3. Petersen, M. D., Shumway, A. M., Powers, P. M., Field, E. H., Moschetti, M. P., Jaiswal, K. S., Milner, K. R., Rezaeian, S., Frankel, A. D., & others. (2024). The 2023 US 50-State National Seismic Hazard Model: Overview and ImplicationsEarthquake Spectra, 40(1), 5–88. https://doi.org/10.1177/87552930231215428
Utpal Kumar
Utpal Kumar

Geophysicist | Geodesist | Seismologist | Open-source Developer
I am a geophysicist with a background in computational geophysics, currently working as a postdoctoral researcher at UC Berkeley. My research focuses on seismic data analysis, structural health monitoring, and understanding deep Earth structures. I have had the opportunity to work on diverse projects, from investigating building characteristics using smartphone data to developing 3D models of the Earth's mantle beneath the Yellowstone hotspot.

In addition to my research, I have experience in cloud computing, high-performance computing, and single-board computers, which I have applied in various projects. This includes working with platforms like AWS, GCP, Linode, DigitalOcean, as well as supercomputing environments such as STAMPEDE2, ANVIL, Savio and PERLMUTTER (and CORI). My work involves developing innovative solutions for structural health monitoring and advancing real-time seismic response analysis. I am committed to applying these skills to further research in computational seismology and structural health monitoring.

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