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This dataset contains 332 CPT-based liquefaction case histories from 25 globally distributed earthquakes. These data, or subsets of these data, have been analyzed in the literature and are provided here to replicate published results. These cases are sourced from existing studies, to include observations of manifestation severity, CPT soundings, and estimation of GWT depth and ground-motion IMs, as generally reported by original investigators. When available, recent refinements were adopted from the literature. Of the 332 global cases, ~55% are “Manifestation” and ~45% are “No Manifestation.” The compiled, post-processed data is presented in a structure array (a single file), allowing researchers to easily access and analyze information pertinent to free-field liquefaction response (i.e., triggering and surface manifestation). Research opportunities using this data include, but are not limited to, the training or testing of new and existing liquefaction-prediction models. Information describing the contents and structure of the case-history data are provided in a ReadME document. Note: This database substantially augments, and thus supersedes, that of Geyin and Maurer (2021). Geyin, M., B. Maurer (2021). "CPT-Based Liquefaction Case Histories from Global Earthquakes: A Digital Dataset (Version 1)", in CPT-Based Liquefaction Case Histories from Global Earthquakes: A Digital Dataset (Version 1). DesignSafe-CI. https://doi.org/10.17603/ds2-wftt-mv37 

This dataset contains 400 cone penetration test (CPT) results from the Cascadia Subduction Zone (CSZ) region of the United States and Canada. The data are provided in both Python and Matlab file formats and a README describes the data processing, formatting, and structure in detail. This regional collection of CPT data has multiple potential uses, including liquefaction hazard assessments and studies of subsurface variability, among others. 

Abstract The shear-wave velocity time averaged over the upper 30 m (VS30) is widely used as a proxy for site effects, forms the basis of seismic site class, and underpins site-amplification factors in empirical ground-motion models. Many earthquake simulations, therefore, require VS30. This presents a challenge at regional scale, given the infeasibility of subsurface testing over vast areas. Although various models for predicting VS30 have thus been proposed, the most popular U.S. national, or “background,” model is a regression equation based on just one variable. Given the growth of community data sets, satellite remote sensing, and algorithmic learning, more advanced and accurate solutions may be possible. Toward that end, we develop national VS30 models and maps using field data from over 7000 sites and machine learning (ML), wherein up to 17 geospatial parameters are used to predict subsurface conditions (i.e., VS30). Of the two models developed, that using geologic data performs marginally better, yet such data are not always available. Both models significantly outperform existing solutions in unbiased testing and are used to create new VS30 maps at ∼220 m resolution. These maps are updated in the vicinity of field measurements using regression kriging and cover the 50 U.S. states and Puerto Rico. Ultimately, and like any model, performance cannot be known where data is sparse. In this regard, alternative maps that use other models are proposed for steep slopes. More broadly, this study demonstrates the utility of ML for inferring below-ground conditions from geospatial data, a technique that could be applied to other data and objectives. 

While soil liquefaction is common in earthquakes, the case-history data required to train and test state-of-practice prediction models remains comparatively scarce, owing to the breadth and expense of data that comprise a single case history. The 2001 Nisqually, Washington, earthquake, for example, occurred in a metropolitan region and induced damaging liquefaction in the urban cores of Seattle and Olympia, yet case-history data have not previously been published. Accordingly, this article compiles 24 cone-penetration-test (CPT) case histories from free-field locations. The many methods used to obtain and process the data are detailed herein, as is the structure of the digital data set. The case histories are then analyzed by 18 existing liquefaction response models to determine whether any is better, and to compare model performance in Nisqually against global observations. While differences are measured, both between models and against prior global case histories, these differences are often statistically insignificant considering finite-sample uncertainty. This alludes to the general inappropriateness of championing models based on individual earthquakes or otherwise small data sets, and to the ongoing needs for additional case-history data and more rigorous adherence to best practices in model training and testing. 

While soil liquefaction is common in earthquakes, the case history data required to train and test state-of-practice prediction models remains comparatively scarce, owing to the breadth and expense of data that comprise a single case history. The 2001 Nisqually, Washington, earthquake, for example, occurred in a metropolitan region and induced damaging liquefaction in the urban cores of Seattle and Olympia, yet case history data has not previously been published. Accordingly, we compile 24 cone-penetration-test (CPT) case histories from free-field locations. The many methods used to obtain and process the data are detailed in the accompanying manuscript, as is the structure of the digital dataset. The case histories are then analyzed by 18 existing liquefaction response models to determine whether any is better, and to compare model performance in Nisqually against global observations. While differences are measured, both between models and against prior global case histories, these differences are often statistically insignificant considering finite-sample uncertainty. This alludes to the general impropriety of championing models based on individual earthquakes or otherwise small datasets, and to the ongoing need for additional case history data and more rigorous adherence to best practices in model training and testing. 

RapidLiq is a Windows software program for predicting liquefaction-induced ground failure using geospatial models, which are particularly suited for regional scale applications such as: (i) loss estimation and disaster simulation; (ii) city planning and policy development; (iii) emergency response; and (d) post-event reconnaissance (e.g., to remotely identify sites of interest). RapidLiq v1.0 includes four such models. One is a logistic regression model developed by Rashidian and Baise (2020), which has been adopted into United States Geological Survey (USGS) post-earthquake data products, but which is not often implemented by individuals owing to the geospatial variables that must be compiled. The other three models are machine and deep learning models (ML/DL) proposed by Geyin et al. (2021). These models are driven by algorithmic learning (benefiting from ML/DL insights) but pinned to a physical framework (benefiting from mechanics and the knowledge of regression modelers). While liquefaction is a physical phenomenon best predicted by mechanics, subsurface traits lack theoretical links to above-ground parameters, but correlate in complex, interconnected ways - a prime problem for ML/DL. All four models are described in an acompanying paper manuscript. All necessary predictor variables are compiled within RapidLiq, making user implementation trivial. The only required input is a ground motion raster easily downloaded within minutes of an earthquake, or available for enumerable future earthquake scenarios. This gives the software near-real-time capabilities, such that ground failure can be predicted at regional scale within minutes of an earthquake. The software outputs geotiff files mapping the probabilities of liquefaction-induced ground failure. These files may be viewed within the software or explored in greater detail using GIS or one of many free geotiff web explorers (e.g., http://app.geotiff.io/). The software also allows for tabular input, should a user wish to enter specific sites of interest and ground-motion parameters at those sites, rather than study the regional effects of an earthquake. RapidLiq v.1.0 operates in the contiguous U.S. and completes predictions within 10 seconds for most events. 

This dataset contains 275 CPT-based liquefaction case histories from 21 globally distributed earthquakes. These data, or subsets of these data, have been analyzed in the literature and are provided here to replicate published results.These cases are sourced from existing studies, to include observations of manifestation severity, CPT soundings, and estimation of GWT depth and ground-motion IMs, as generally reported by original investigators. When available, recent refinements were adopted from the literature. The compiled, post-processed data is presented in a structure array (a single file), allowing researchers to easily access and analyze information pertinent to free-field liquefaction response (i.e., triggering and surface manifestation). Research opportunities using this data include, but are not limited to, the training or testing of new and existing liquefaction-prediction models. Information describing the contents and structure of the case-history data are provided in a ReadME document. 

Earthquakes occurring over the past decade in the Canterbury region of New Zealand have resulted in liquefaction case-history data of unprecedented quantity. This provides the profession with a unique opportunity to advance the prediction of liquefaction occurrence and consequences. Toward that end, this article presents a curated dataset containing ∼15,000 cone-penetration-test-based liquefaction case histories compiled from three earthquakes in Canterbury. The compiled, post-processed data are presented in a dense array structure, allowing researchers to easily access and analyze a wealth of information pertinent to free-field liquefaction response (i.e. triggering and surface manifestation). Research opportunities using these data include, but are not limited to, the training or testing of new and existing liquefaction-prediction models. The many methods used to obtain and process the case-history data are detailed herein, as is the structure of the compiled digital file. Finally, recommendations for analyzing the data are outlined, including nuances and limitations that users should carefully consider.