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What We Did
Dist Prof Chase and Dr Cong Zhou have developed the methods using sensor data from instrumented buildings to accurately and robustly detect changes in structural stiffness, or damage, after major earthquakes. Accurate damage detection allows far better decision making on whether to repair or demolish a structure, because it provides much more precise information than current inspection-based methods. The cost of sensors to instrument a building is less than 0.1% of building cost, so it is readily achievable. They have then taken this data and used it to develop models able to accurately predict what will happen to the building should another major aftershock or earthquake arrive.
No one else in the world can predict such outcomes. The ability to know the potential risks and to quantify them could significantly increase the speed of decision making and rebuild, reduce debate with insurers and owners, and thus improve social and economic recovery of cities and regions. The technology has been validated and proven in full-scale experiments in Japan, and a wide range of other experiments.
This research strongly complements Prof Chase’s work with Prof Rodgers in novel devices to improve structural resilience and dissipate earthquake response energy.
Who Was Involved
The research has been partly funded by the EQC and the Chinese Science Council. It involves Prof Chase and Dr Zhou at UC, and their students, as well as collaborators across the world.
Why It Matters
Economic recovery after an earthquake can take up to 20 years. Accurate damage assessment offers far greater and more precise information than current inspection methods, enabling the quality and speed of post-event decision-making to be optimised. The further ability to predict the damage from future events and risk of collapse would also save significant lives, as seen in Christchurch’s series of 4 major earthquakes over 15 months in 2010-2011.
Learn More
- See the UC Connect Lecture Series 'Earthquakes + Innovation = Resilience':
- Zhou, C., Chase, J. G., & Rodgers, G. W. (2021) Support vector machines for automated modelling of nonlinear structures using health monitoring results. Mechanical Systems and Signal Processing, 149, 107201. https://doi.org/10.1016/j.ymssp.2020.107201
- Zhou, C., & Chase, J. G. (2020). A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring. Structural Health Monitoring, https://doi.org/10.1177/1475921720920641
- https://www.youtube.com/watch?v=nBPn5ZrMa6E
