Improve earthquake damage predictions

No one knows exactly when and where an earthquake will strike. But computer simulations are helping scientists and engineers improve predictions of liquefaction, a sometimes deadly seismic effect in which soil loses its rigidity, bringing down buildings and more.

The dataset that provides key inputs for assessing earthquake-induced liquefaction won the 2024 DesignSafe Dataset Award, which recognized the dataset's diverse contributions to natural hazards research.

“The main goal of this project was to provide ground motion intensity metrics for liquefaction stimulation evaluations,” said Renmin Brettell, assistant professor of civil and environmental engineering at the University of Nevada, Reno (UNR).

The award-winning dataset PRJ-4022 | Continuously calculated ground motion intensity measurements to evaluate liquefaction stimulation. The dataset is publicly available on the NHERI DesignSafe cyber infrastructure. Details of the study are contained in a recently published report.

Liquefaction occurs when the ground moves from an earthquake, breaking down the soil's rigidity and shear strength as pore water pressure increases and the effective stress holding soil particles together decreases. Soil liquefaction has been observed and documented in earthquake-prone areas such as California, Alaska, Japan, South America, Turkey, and elsewhere around the world.

“Based on documented case histories, liquefaction stimulation models were developed,” Brittle said. “A key component of these models is the peak ground acceleration at locations where liquefaction occurred.”

“The goal of our project was to use a systematic technique to calculate peak ground acceleration at all case history locations,” Brittle said. The team used its improved technology uniformly across 565 case registry sites based on the latest datasets, which it said was improving over time.

Britell's team estimated not only the peak ground acceleration, but also other ground motion intensity measurements that would likely induce liquefaction leading to the prediction, such as maximum ground velocity, Arias density, and cumulative absolute velocity.

“The main finding of our data set is that there are important discrepancies between the peak ground accelerations that have been used in the past and the peak ground accelerations that we estimated based on our methodological approach,” Brittle said.

Liquefaction is one of the most destructive phenomena in geotechnical engineering. Many researchers over the years have devoted their lives to exploring and investigating liquefaction, building data sets that are still in use today decades later.

“The concern is that we have semi-empirical models that rely heavily on the case history of past observations, and one of the components is seismic demand,” Brittle said.

Using these models, practicing engineers and researchers can evaluate the potential for liquefaction release for a particular site, such as a future housing development. Because there are different techniques for estimating seismic demand, some more subjective than others, this motivated the team to search for improvements to liquefaction stimulation models using the latest available dataset and modern methods and statistical techniques.

The award-winning dataset PRJ-4022 has benefited from several previous efforts. It was mainly drawn from two large databases created for research in earthquake engineering.

The Next Generation Liquefaction Database (NGL) has collected all liquefaction event histories, and assigned each liquefaction site a characterization of geotechnical parameters to help understand ground conditions, geology, soil geotechnical properties, groundwater levels, and other characteristics. Which leads to the appearance of surface liquefaction or its absence.

The other main source of data used in PRJ-4022 is from Next Generation Attenuation (NGA) projects, which include NGA-West2 for shallow crustal earthquakes in active tectonic regions; NGA-East for stable continental areas; and NGA-Subduction for earthquakes in subduction zones.

“These are important projects that have combined ground motion records compiled from large-scale recordings of earthquakes from stations around the world,” Brittle said. “Our project has benefited from previous data collection efforts.”

The data life cycle usually goes from collection to documentation and then storage. But the PRJ-4022 liquefaction data set is different.

“We used the NGL and NGA databases, and developed spatial correlation models and estimated ground motion intensity metrics. One can think of our data set as a simulation type of data set. Our data set provides a set of ground motion intensity metrics at locations,” Brittle said. History of the liquefaction state of the most important seismic events, rather than raw data.”

“The goal is to benefit any community, most commonly the research community. We have included a Python-based Jupyter notebook in our dataset repository that can be used to calculate ground motion intensity metrics at sites of interest for the earthquakes we included in our study. To this end, The user only needs to enter the latitude and longitude of the site and one soil parameter consisting of the average shear wave speed over time.

Furthermore, Pretell has developed and updated Python packages on GitHub to make it easier to use the Jupyter notebook.

The spatial correlation models developed as part of their project can also help researchers and agencies perform risk analysis on expanded infrastructure, such as lifelines and transit.

Brettel's team expanded the dataset to create spatial correlation models and ground motion intensity metrics for the four events of the February 2023 Türkiye earthquake sequence, the results of which were published in the journal Earthquake Spectra.

“We have provided estimates of peak ground acceleration at dam sites, construction sites, hospitals and many different research teams have traveled to Türkiye,” Brettel said. Researchers sought to understand, for example, why some hospitals suffered earthquake damage and others did not.

“We looked at the spatial variation of peak ground acceleration, and we saw that in some areas there was an overall underestimate compared to the models we use,” Brettell added.

Another study related to the PRJ-4022 dataset focused on the 1989 M6.9 Loma Prieta earthquake, published in February 2024 in Geo-Congress 2024, which estimated peak ground acceleration values ​​at case history locations.

“In this post, we demonstrated the systematic technique we use to calculate ground motion intensity measures for the Loma Prieta earthquake,” Brittle said.

One of the big challenges the scientists faced with the award-winning dataset is the mutability of the database, as errors are corrected and records are added. Every time a change occurs in the input databases, new calculations must be performed to generate new results – making data release important for the project.

“DesignSafe is the perfect platform because we update our dataset as we collect more data or improve results, and release a different version. It's the ability to update our results, while keeping the same name and direct object identifier (DOI) — that helps DesignSafe “In providing it – it is very useful.”

“DesignSafe is also a reliable, long-term platform that the earthquake engineering community knows well,” he added.

“The key points people should know is that we have a dataset of consistently estimated ground motion intensity measurements at almost all liquefaction history sites collected to date. These data can be used not only to model liquefaction induction, but also estimate liquefaction-induced lateral propagation.” “This data set could benefit researchers who are developing methods or models to assess the dangerous effects of liquefaction caused by earthquakes,” Brittle said.

DesignSafe is a comprehensive cyberinfrastructure that is part of the NSF-funded Natural Hazards Engineering Research Infrastructure (NHERI) and provides cloud-based tools to manage, analyse, understand, and disseminate critical data for research to understand the impacts of natural hazards. The capabilities in the DesignSafe infrastructure are available free of charge to all researchers working in the field of natural hazards. The cyber infrastructure and software development team is located at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin, with a team of natural hazard researchers from the University of Texas, Florida Institute of Technology, and Rice University that includes leading natural hazard researchers. Management team.

NHERI is supported by multiple grants from the National Science Foundation, including DesignSafe Cyberinfrastructure, Award No. 2022469.

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