Award-winning dataset helps in seismic liquefaction research

No one knows exactly when or where an earthquake will occur. But computer simulations are helping scientists and engineers improve liquefaction predictions. Liquefaction is an effect of earthquakes that causes soil to lose its rigidity and cause buildings to collapse, sometimes fatally.

The dataset, which provides critical input for assessing seismic liquefaction induction, was awarded the 2024 DesignSafe Dataset Award in recognition of the dataset’s diverse contributions to natural hazard research.

“The main purpose of this project was to provide measurements of seismic motion intensity for liquefaction triggering assessments,” said Renmin Pretel, assistant professor of civil and environmental engineering at the University of Nevada, Reno (UNR).

Scott Brandenberg (UCLA), Jonathan Stewart (UCLA), and Renmin Pretell (UNR) jointly published the award-winning dataset PRJ-4022 | Consistently calculated ground motion intensity measurements for liquefaction induction assessment. This dataset is publicly available on the NHERI DesignSafe Cyberinfrastructure. Details of the study can be found in a recently published report.

Liquefaction occurs when the ground moves due to an earthquake, destroying the stiffness and shear strength of the soil by increasing pore water pressure and reducing the effective stress holding soil particles together. Soil liquefaction has been observed and documented in California, Alaska, Japan, South America, Turkey, and other earthquake-prone regions around the world.

“A liquefaction induction model was developed based on the documentation of the case record,” Pretel said. “One of the key components of these models is the maximum ground acceleration where liquefaction occurs.”

“The goal of our project was to use a systematic approach to calculate the maximum ground acceleration at all case history sites,” Pretel said. The team is using improved technology uniformly across 565 case history sites, based on the latest datasets, and is improving over time, he said.

In addition to estimating peak ground acceleration, Pretell’s team also estimated other ground motion intensity measurements that could advance predictions of causing liquefaction, such as peak ground velocity, arias intensity, and cumulative absolute velocity.

“The main finding of our dataset is that there are important discrepancies between the peak ground accelerations used in the past and the peak ground accelerations that we estimated based on a systematic approach.” Pretel said.

Liquefaction is one of the most destructive phenomena in geotechnical engineering. Over the years, many researchers have dedicated their lives to exploring and investigating liquefaction phenomena, building datasets that are still in use decades later.

“The concern is that we have a semi-empirical model that relies heavily on historical observations, and one of its components is seismic demand,” Pretel said.

These models allow practicing engineers and researchers to assess the potential for liquefaction to occur in a particular location, such as a future housing development. Additionally, because there are various techniques for estimating seismic demand, some of which are subjective, the team used the latest datasets available and recent methodologies and statistical techniques to estimate liquefaction-induced This motivated me to explore ways to improve the model.

The award-winning dataset PRJ-4022 benefited from several previous efforts. It was drawn primarily from two large databases established to study earthquake engineering.

The Next Generation Liquefaction Database (NGL) collects all liquefaction cases and provides information on geotechnical parameters to help understand ground conditions, geology, soil geotechnical properties, groundwater levels, and other properties. Site characteristics are assigned to each liquefaction site. leading to the development of surface liquefaction, or lack thereof.

Another major data source used in PRJ-4022 is from the Next Generation Attenuation Project (NGA). This includes NGA-West2 for shallow crustal earthquakes in active tectonic regions. NGA-East for a stable continental region. and NGA-Subduction (subduction earthquake).

“These are important projects that bring together ground motion records collected from extensive records of earthquakes from observatories around the world,” Pretel said. “Our project benefited from previous data collection efforts.”

The data lifecycle typically moves from collection to documentation to storage. However, the liquefaction dataset PRJ-4022 is different.

“We used the NGL and NGA databases, developed spatial correlation models, and estimated ground motion intensity measurements. Our dataset can be thought of as more of a simulation-type data set. “Our dataset provides a collection of ground motion intensities. For the most important seismic events, measures should be taken at historical sites of liquefaction events, rather than raw data.” said.

“Our goal is to benefit any community, and more generally the research community. We have included a Python-based Jupyter notebook in our dataset repository, which can be used to analyze the seismic target locations that we have included in our research. “For this purpose, the user only needs to input one soil parameter, consisting of the latitude, longitude, and time-averaged shear wave velocity of the site,” Pretell said. I added.

Additionally, Pretell has developed and updated Python packages on GitHub to facilitate the use of Jupyter notebooks.

Spatial correlation models developed as part of the project will also help researchers and institutions conduct risk analysis of extended infrastructure such as lifelines and transportation.

Pretell’s team expanded the dataset to produce spatial correlation models and ground motion intensity measurements for four events in the February 2023 Türkiye earthquake sequence, and the results were published in Earthquake Spectra.

“We provided estimates of maximum ground acceleration at dam, building and hospital sites to various research teams visiting Turkey,” Pretel said. Researchers sought to understand, for example, why some hospitals suffered earthquake damage while others did not.

“We studied the spatial variation of peak ground acceleration and found that in some zones it was overall underestimated compared to the model we are using,” Pretel said. he added.

Another study related to the PRJ-4022 dataset focused on the 1989 M6.9 Loma Prieta earthquake, presented at Geo-Congress 2024 in February 2024, and determined the maximum ground acceleration values ​​at case history sites. I estimated it.

“In this publication, we have demonstrated the systematic method we are using to calculate ground motion intensity measurements for the Loma Prieta earthquake,” Pretel said.

One of the big challenges scientists have had with award-winning datasets is the variability of the database, where errors are corrected and records are added to. Data versioning is important to your project because each time a change occurs to the input database, new calculations must be performed to produce new results.

“DesignSafe provides the perfect platform to update datasets and release different versions as more data is collected or results improve. ) and the ability to update the results while maintaining the results, DesignSafe helps provide something very useful,” Pretell said.

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

“The important thing that people need to know is that there is a dataset of ground motion intensity measurements that have been consistently estimated at nearly every liquefaction case history site that has ever been collected. , it can be used not only for liquefaction-induced models but also for lateral ground motions caused by liquefaction.” This dataset is useful for researchers developing methods and models to assess the hazardous effects of seismic liquefaction. ,” Pretell said.