Design of earthquake-resistant buildings for extreme events

Buildings and urban infrastructure are designed to withstand earthquakes and other natural disasters, based on data from past events. But how do you design a building to survive a severe disaster that has yet to happen?

In other words, you can design a building or infrastructure and be reasonably confident that it will withstand a moderate earthquake. But how confident can you be if the quake was a magnitude 7.5 or 8 — one of those extreme events that rarely occur and for which we have limited data?

Associate Professor of Civil and Environmental Engineering Floriana Petroni is working on this.

She is developing new scientific methods – using simulations, machine learning and probability models – to better predict what happens to infrastructure in severe disasters. Her project, Toward a Next Generation Data-Driven Probabilistic Framework for Infrastructure Seismic Risk Assessment and Uncertainty Estimation, was supported by a five-year, $575,478 CAREER award from the National Science Foundation (NSF) last summer. The Faculty Early Career Development (CAREER) program is NSF's most prestigious award for supporting early-career faculty.

“We are very excited about Dr. Petrone’s cutting-edge research and the educational opportunities it will provide our students,” said Kerry Ryan, chair of the Department of Civil and Environmental Engineering.

Closing the data gap

Advances in computer technology and simulation techniques in the early 2010s led to detailed and accurate disaster simulations. The resulting big data (which can tell us about large-scale patterns) and high-resolution models, which can show detail, have produced a wealth of data about disaster scenarios.

The problem? Traditional methods for assessing structural risk rely heavily on observational data (recordings and experiments) that do not statistically cover extreme events, leaving gaps in our ability to predict structural risk in an extreme event, according to Petrone.

“So far, we have developed our models based on the data we have,” she said. “However, large-scale simulations provide, for the first time, the opportunity to address critical knowledge gaps, allowing us to validate and update our models to improve estimates of infrastructure behavior under extreme events.”

“However, while nowadays simulations can be performed for any conceivable scenarios, a methodology that synthesizes the knowledge coming from this new simulation data is missing,” Petrone said.

The new framework she is developing will integrate state-of-the-art disaster simulations into traditional risk assessment methods.

“The idea is to develop a framework capable of collecting monitoring and simulation data, leveraging machine learning to obtain new physics-informed models and predict infrastructure response under a wide range of events, including extreme scenarios,” she said.

This will be an open framework – that anyone can use – to leverage simulation data that already exists and integrate new simulation data that will become available.

The ultimate goal is to improve the rules and standards for infrastructure design, so that buildings and structures have a better chance of withstanding “major events” or other extreme events caused by natural disasters.

“The wellness of modern society is closely linked to the function of urban infrastructure and facilities that support basic services,” Petroni said. “Enhancing our ability to correctly assess extreme hazard loads on a local to site-specific scale and develop reliable probabilistic demand models is critical for planning a wide range of surface and subsurface activities on a fully risk-informed basis, with direct implications for safety, cost and societal disruption.”

Reflection of new scientific and technological progress towards engineering

Petrone received her Ph.D. in Structural Engineering at Sapienza University of Rome.

Because Petroni lived in Italy, a seismically active region, “I've always had this interest in earthquakes and how engineers can solve classic problems using new methods,” she said.

She moved to the United States in 2012 as a Transatlantic Partnership for Engineering Excellence Fellow, and completed her doctoral studies while at the University of California, Davis. She worked at Lawrence Berkeley National Laboratory, where she contributed to the development of digital tools to analyze how nuclear facilities behave during earthquakes.

She joined the University of Nevada, Reno as an assistant professor in 2019, where she worked on several projects related to high-performance computing applied to civil engineering problems and advanced numerical modeling of reinforced concrete and composite structures. She has recently expanded her research interests in additive construction to investigate the use of local materials for rapid deployment of on-site infrastructure. In this field, she is leading a 3D printed concrete project in collaboration with the Construction Engineering Research Laboratory of the US Army Engineers Research and Development Center and with support from the US Department of Defense.

Petron, who was promoted to associate professor last summer, sees this latest professional award as an opportunity to advance the field of structural engineering.

“This career award represents a long-term vision that I want to build to enhance our approach to classic engineering problems, which are not yet fully solved,” she said. “My primary goal is to develop the scientific knowledge and computational tools needed to enhance the resilience of civil infrastructure and energy systems in the face of natural hazards. It is also a commitment to mentoring the next generation of engineers at the intersection of computer science, data analysis, and civil engineering.”

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