cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds2-32fp-8535
title: Mochikoshi Tailings Dam Simulation
title: in Analysis of Mochikoshi Dam Failure using a Hybrid FEM-MPM Approach

description: The Mochikoshi Tailings Dam simulation is divided into three phases: the FEM Phase, the Transfer Phase, and the MPM phase. The model for the FEM Phase is an OpenSees model which captures the seismic response of the tailings dam during an earthquake. The Transfer Phase is a script that converts the FEM model at a given time into an MPM model. The MPM Phase is the analysis of the MPM model to capture the runout of the liquefaction induced tailings dam failure. One FEM analysis was performed but several MPM analyses were performed considering different transfer times from FEM and different strength characterizations.
description: This dataset includes results from an analysis of the earthquake-induced failure of the Mochikoshi Tailings Dam in 1978 during the Izu Oshima earthquake in Japan. The earthquake induced liquefaction of the retained tailings and the associated strength loss resulted in a breach of the tailings dam, releasing the liquefied tailings to flow downhill for hundreds of meters. The numerical simulations provided in this dataset capture the entire failure process including dynamic response, liquefaction, dam breach, and runout through a hybrid Finite Element Method (FEM) and Material Point Method (MPM) analysis. The data provided in this project include the FEM model of the dam, the FE results of the analysis, the script that transfers from FEM to MPM, the MPM model, and the MPM runout results.  Results are provided for a range of FEM to MPM transfer times and different characterizations of liquefied residual strength.
DOI: 10.17603/ds2-85fv-n684
title: RAPID: Assessing the Performance of Elevated Wood Buildings, including Manufactured Housing, in Florida during 2018 Hurricane Michael

description: Hurricane Michael made landfall as a Category 4 hurricane on October 10, 2018. This hurricane was the strongest to hit the continental United States since Hurricane Andrew in 1992. The observed impact of Hurricane Michael presents a unique and important opportunity to investigate the wind performance of building structures given that recorded wind speeds reached and exceeded design winds at the coast and further inland.
Following landfall, this Rapid Response Research (RAPID) grant performed door-to-door (D2D) assessments to investigate the wind performance of elevated site-built and manufactured housing. The RAPID reconnaissance was performed October 20-23, 2018 in the Florida Panhandle, spanning coastal and inland areas to survey damage caused by a range of peak and sustained wind speeds. In total 233 structures were surveyed, including 69 site-built and 164 manufactured homes. 
Florida has one of the largest inventories in the United States of manufactured housing, the most vulnerable residential structure to hurricanes, which also often houses the most socially vulnerable households. While damage to manufactured housing has been widely noticed following major hurricane events, this type of structure has received minimal research attention. Manufactured housing is a subset of elevated housing, where elevated site-built homes are also very common in the areas impacted by Hurricane Michael. The influence that the elevation height, stilt distribution, and foundation type have on wind loading and damage experienced by elevated site-built and manufactured housing structures is presently unknown. This RAPID project provides necessary post-disaster data needed to investigate these fundamental gaps in knowledge.
DOI: 10.17603/ds2-fq79-1926
title: Research Experiences for Undergraduates (REU), NSF NHERI 2024: Unveiling Debris Flow Dynamics: Physics-Informed Machine Learning via MPM Simulation

description: Tsunamis, landslides, and storm-surges can mobilize debris, compounding the hazards faced by the built environments. Predicting the dynamics of entrained debris in these complex flow events is challenging due to the varying materials, phases, and the uncertainty associated with their respective properties. This study presents a novel approach to address this challenge using a combination of machine learning (ML) and high-performance numerical simulation. Our understanding of the dynamics of these hazards is underpinned by experimental facilities, which we aim to augment with a digital twin surrogate modeling workflow. In this manuscript, we introduce a prototype digital twin of the Hinsdale Wave Research Facility's Large Wave Flume at Oregon State University (OSU LWF) for studying wave-debris dynamics. The first-phase of our two-stage high-performance digital twin uses the Material Point Method (MPM), implemented within the Taichi programming language, to support classical numerical simulation of large-deformation, multi-material dynamics. For the creation of lightweight surrogate models in the second-phase, we leverage the highly flexible Graph Network Simulator (GNS), a graph neural network software package (GNN), to enable application of our workflow to a plethora of unexplored experimental facilities which may or may not center on wave-debris studies.  By utilizing high-performance computing (HPC) and deep learning networks, this approach enables the efficient representation of complex physics and facilitates uncertainty quantification in debris hazard events. With this, the ongoing integration of these technologies within the NHERI SimCenter’s engineering workflow, specifically into HydroUQ, has great potential to provide a robust framework to run high-fidelity simulations. This work demonstrates the potential of ML-driven surrogate models to enhance the predictive capabilities and efficiency of simulation, paving the way for improved debris flow hazard mitigation strategies.
DOI: 10.17603/ds2-j91d-z448
title: NHERI SimCenter - Pelicun Examples

description: This project collects example calculations that demonstrate the capabilities and suggest best practices for using pelicun, the performance assessment library developed at the NHERI SimCenter. Each example is provided as a Jupyter notebook that collects the scripts to run the calculation and provides detailed description of each step as well as visualizations of intermediate and final results. The examples featured in SimCenter's Live Expert Tips webinars have a link to the recorded webinar embedded in the Jupyter Notebook.

The scripts in these examples are meant to serve as templates for researchers working on performance assessment of individual assets or risk assessment of a portfolio of assets. Each example is self-contained and does not expect researchers to have additional knowledge about Pelicun or the SimCenter.
DOI: 10.17603/ds2-nvm9-1w53
title: Research Experiences for Undergraduates (REU), NSF NHERI 2024: Hyperparameter Optimization of Graph Neural Network Simulator for Liquid and Granular Flow 

description: This project aims to optimize the Hyperparameters of the Graph Neural Network Simulator (GNS) to address the limitations of GNS -simulation scalability and error accumulation from longer rollouts- which restricts its application in engineering fields. GNS was developed to accurately simulate granular and liquid flow while maintaining a low computational cost and generalizability. It is used by engineers to mitigate the effects of natural hazards such as landslides.  This project data can be used to further improve GNS model and increase its application in mitigating natural hazards. It would be the first time GNS's hyperparameters were optimized, which is an important process in improving machine learning models. The audience would be natural hazards engineers.
DOI: 10.17603/ds2-sp3e-dp21
title: Mechanics-informed machine learning for geospatial modeling of soil liquefaction: example model implementation in Jupyter Notebook and Matlab

description: This code repository contains an example Jupyter Notebook and Matlab script to implement a geospatial liquefaction model. This geospatial liquefaction model is designed to predict manifestation of earthquake-induced soil liquefaction at the ground surface through geospatial surrogate models of three standard-of-practice geotechnical models: the liquefaction potential index (LPI); a modified LPI, termed LPIISH; and the liquefaction severity number (LSN). Model users are referred to the journal publication for details regarding model development, performance, and use:

Sanger, M.D., Geyin, M., & Maurer, B.W. (2024). Mechanics-informed machine learning for geospatial modeling of soil liquefaction: global and national surrogate models for simulation and near-real-time response. In Review.
DOI: 10.17603/ds2-sp99-xv89
title: Report
title: in Seaside Testbed Data Inventory for Infrastructure, Population, and Earthquake-Tsunami Hazard

description: This project contains datasets associated with Seaside, Oregon, a multi-hazard testbed for infrastructure resilience. Seaside is located along the Oregon coast and is subject to both seismic and tsunami hazards associated with the Cascadia Subduction Zone (CSZ). The Seaside testbed was established as a multi-hazard testbed for the NIST Community Resilience Center of Excellence. The size of Seaside lends itself to parcel-level analyses of multi-infrastructure damage, loss, and recovery modeling. 


Datasets are provided for built-, natural-, and social-systems in Seaside. The built environment consists of buildings, electric power, transportation, and water infrastructures. The building inventory was constructed from tax-lot data, Google street view, and a field survey (Park et al., 2017b). The electric power network was constructed from aerial imagery. Bridges in the transportation network are from the National Bridge Inventory and roads are from the TIGER/Line database. The water network is from the city of Seaside public works (Kameshwar et al., 2019). The natural environment (hazards) are the result of a probabilistic seismic-tsunami hazards analysis (PSTHA; Park et al., 2017a). The social systems consist of results from a housing unit allocation (Rosenheim et al., 2019). Researchers, decision makers, and planners, among others, can utilize these datasets to conduct community-scale multi-hazard risk assessment, focusing on application to the coastal urban city of Seaside, Oregon.
description: Overview report provides a summary of the datasets, including built-. natural-, and social-systems in Seaside, Oregon. Relevant references are also provided. Additional information is available in the README file in the correspondent dataset in DesignSafe.
DOI: 10.17603/ds2-xe2x-xs40
title: Primary and Secondary Health Impacts of the COVID-19 Pandemic on New Yorkers (May-June 2020)
title: in National Food Access and COVID Research Team (NFACT) - New York

description: This research study aims to examine the primary and secondary health consequences of COVID-19 with a focus on food security and the social determinants of health among New York State residents. 

This research is conducted as part of the National Food Access and COVID Research Team (NFACT), which is implementing common measurements and tools across study sites in the US> NFACT is a national collaborative of researchers committed to rigorous, comparative, and timely food access research during the time of COVID-19. We do this through collaborative, open access research that prioritizes communication to key decision-makers while building our scientific understand of food system behaviors and policies.
description: Research brief on primary and secondary health impacts of COVID-19 on New Yorkers in May and June 2020. Report includes direct COVID-19 impact as well as employment, concerns about well-being, food access, mental health, risk factors, and sociodemographic characteristics of the sample.
DOI: 10.17603/ds2-2dk8-rr43
title: Hurricane and Public Mental Health: A Rapid Review through a Social Work Lens

description: Through a rapid literature review approach, this project strives to synthesize current knowledge regarding individual and collective mental health experiencing hurricanes internationally. The multi-database search identified 61 journal articles ranging from January 2000 to June 2021. The dataset compiled from the noted articles includes: (1) authors, (2) article title, (3) article abstract, (4) journal volume, issue, and page range, (5) year of publication, and (6) DOI. This hurricane-specific literature dataset illustrates how hurricanes impact mental health and provides information, context, and ongoing research and practice concerned associated with individual and collective mental health and well-being. This dataset could assist disaster and emergency management professionals and health and social care practitioners in identifying evidence-based strategies to support their research and practice efforts. For example, the research team, three Canadian social workers, evaluated the knowledge from a social work lens and applied the outcomes in a Canadian mental health intervention and support context.
DOI: 10.17603/ds2-a1t2-c650
title: Tsunami Hazards
title: in Seaside Testbed Data Inventory for Infrastructure, Population, and Earthquake-Tsunami Hazard

description: This dataset contains the tsunami results of a probabilistic seismic tsunami hazard analysis.
description: This project contains datasets associated with Seaside, Oregon, a multi-hazard testbed for infrastructure resilience. Seaside is located along the Oregon coast and is subject to both seismic and tsunami hazards associated with the Cascadia Subduction Zone (CSZ). The Seaside testbed was established as a multi-hazard testbed for the NIST Community Resilience Center of Excellence. The size of Seaside lends itself to parcel-level analyses of multi-infrastructure damage, loss, and recovery modeling. 


Datasets are provided for built-, natural-, and social-systems in Seaside. The built environment consists of buildings, electric power, transportation, and water infrastructures. The building inventory was constructed from tax-lot data, Google street view, and a field survey (Park et al., 2017b). The electric power network was constructed from aerial imagery. Bridges in the transportation network are from the National Bridge Inventory and roads are from the TIGER/Line database. The water network is from the city of Seaside public works (Kameshwar et al., 2019). The natural environment (hazards) are the result of a probabilistic seismic-tsunami hazards analysis (PSTHA; Park et al., 2017a). The social systems consist of results from a housing unit allocation (Rosenheim et al., 2019). Researchers, decision makers, and planners, among others, can utilize these datasets to conduct community-scale multi-hazard risk assessment, focusing on application to the coastal urban city of Seaside, Oregon.
DOI: 10.17603/ds2-c1zt-sy71
title: Natural Hazards Research Summit 2024: Integrating REWET into R2D for Flexible Restoration and Functionality Assessment of a Damaged Water Distribution Networks

description: The Restoration Tool for Water Network after Event (REWET) is a standalone tool for the functionality and resiliency assessment of water networks following events. It incorporates restoration efforts made postevent to restore the system’s functionality to the consumers. REWET requires damage realization and provides functionality data, such as water outage time, to users and various metrics for further assessment of other dependent metrics, such as social adaptability. To enable potential users to have a more effective post-event water distribution network performance assessment tool, REWET has been integrated into NHERI SimCenter’s R2D Tool. The R2D Tool (Regional Resilience Determination Tool) is designed to assist researchers, practitioners, and other potential users in regional risk assessment. Thus, with the integration of REWET into the R2D Tool’s flexible workflow, all steps for a thorough analysis of post-event damaged water distribution networks incorporating restoration efforts, including hazard scenario generation, hazard assessment, damage assessment, and functionality assessment of water distribution networks with and without restrictions, can all be done in one place.
DOI: 10.17603/ds2-c9fr-x257
title: Next Generation Liquefaction (NGL) - Supporting Studies: Overburden and Initial Shear Stress

description: Results from laboratory tests have shown that effective confining pressure and non-zero initial static shear stress can affect a soil’s cyclic resistance ratio (CRR). To account for these effects, the factors Kσ and Kα are used to modify the CRR, respectively. To facilitate the validation of existing Kσ and Kα models and the development of new models, we compiled results from dozens of published studies and organized them in a digital data set. The compiled data set contains CSR vs. N (cyclic stress ratio vs. number of cycles) data, reported and computed CRR values, and reported and computed Kσ and Kα values that span a range of soil type, relative density, initial stress, fines’ content, and specimen preparation method. This data set extends the amount of data available for evaluation and development of Kσ and Kα models and is provided for use in next generation liquefaction (NGL) models.
DOI: 10.17603/ds2-fm59-xa94
title: BEHAVIOUR OF PROTOTYPE MASONRY BUILDINGS PRODUCED WITH DIFFERENT BOND ON THE SHAKING TABLE
title: in Prototype Masonry Buildings

description: ADXL345 Acceleration Data
description: Acceleration data of the test specimens were measured with ADXL345 accelerometers
DOI: 10.17603/ds2-m9k2-wr35
title: Joplin Testbed for Multi-Disciplinary Community Resilience Modeling Under Wind-Induced Hazards

description: This project encompasses a synthesized dataset of buildings and electric power facilities for the Joplin testbed, developed following the 2011 Joplin tornado and comprehensively used for multi-disciplinary community resilience simulations. The data and simulation for this testbed have been extensively and will be continually used as an input and a technique for collaboration with groups in economics, social science, computer science, and operations research devoted to multi-disciplinary community resilience modeling.
DOI: 10.17603/ds2-pz0s-xv32
title: PRELIMINARY g2F-Compatible 4-State CCLS Models for Use in Fragility Illustration in EQE-20-122
title: in T1780 | Compatible 4-State CCLS Models for Use in Fragility Analysis of Bridges & Seat Abutments in California Bridges

description: This project provides component capacity limit state (CCLS) benchmark values for uniform comparison of component fragilities associated with the various abutment backwall modeling schemes.  The bridge components considered in this project include columns, pier wall, piles, shafts, bearings, strip seal, and unseating.  Photos of the corresponding damage states of each bridge component have been collected.  Most of the data were obtained through experimental results, papers, and reconnaissance reports.  A column curvature metric for local section damage has been developed from numerical analysis. Preliminary simplified documents of sample of abutments from California bridge inventory are included.
description: Capacity Limit State Benchmark Values of Bridge Components
DOI: 10.17603/ds2-w36s-z688
title: Research Experiences for Undergraduates (REU), NHERI 2023: Comparing methods to Reduce Hallucinations in Large Language Models: A study of Cosine Similarity, Fine-Tuning and Zero-shot learning.

description: Large Language Models have revolutionized natural language processing and artificial intelligence, showcasing remarkable capabilities in generating human-like text and assisting with various task. However, it risks generating plausible but incorrect information (hallucinations) that lack factual grounding. This study presents a comprehensive comparison of three method for reducing hallucinations in LLMs: Cosine similarity, Fine-Tuning and Zero-shot learning. This project can be reused by both researchers and professionals in the field of Artificial intelligence and it is unique because it does a comparative analysis of methods to mitigating hallucinations. The intended audience of this research paper are professionals and students who working on Large Language Models, Natural Language Processing and Machine Learning.
DOI: 10.17603/ds2-zcrk-jm87
title: XBeach Scenarios
title: in Emulator for Nourished Beach Scarping due to Storms

description: This simulation contains files to generate 1250 synthetic storms interacting with 1250 different cross-beach nourishment profiles to assess erosion impacts with the XBeach model. The project contains processing scripts to create synthetic storms based on historical information, to create nourishment profiles based on surveys from 2011-2019 in Nags Head, NC. Analysis scripts are also provided to identify the beach scarp generated in XBeach and create a Gaussian Process Regression model to predict an exponential fit for the eroded scarp.
description: An emulator is developed to predict erosion of nourished beaches due to possible sea-storms and cross-shore beach profiles. With a large library of simulations on possible beach profile response, the emulator can predict cumulative erosion due to a series of storms. For the 2019 hurricane season in Nags Head, NC, the emulator predicts the shoreline changes and erosion volumes with high accuracy.
DOI: 10.17603/ds2-03km-9v86
title: Ehime University Centrifuge Experiments of the Seismic Performance of Sheet Pile Walls in Liquefiable Soils
title: in LEAP-GWU-2022 Experiments:  Seismic Performance of Sheet Pile Walls in Liquefiable Soils

description: Two centrifuge tests (EU1, EU2) were conducted at Ehime University to evaluate the seismic response of sheet-pile retaining structures in liquefiable soils for the 2022 Liquefaction Experiments and Analysis Project (LEAP-GWU-2022).
description: As part of the Liquefaction Experiments and Analysis Projects (LEAP - an international collaboration among several institutions with expertise and capabilities in centrifuge modeling, constitutive modeling, and numerical modeling) LEAP-2022 investigated the seismic performance of sheet pile walls in liquefiable soils.  The results of twenty centrifuge model tests, performed as part of a round robin test program, are shared in this archive. The datasets produced by the model tests are published as six separate experiments in this archive (one experiment per centrifuge facility). Each "experiment" includes a few model tests and each model test includes between one and three destructive shaking events. All of the tests modeled a sheet pile wall supporting a liquefiable soil (Ottawa F-65 sand and Silica sand).  The tests covered a range of ground motion intensities and a range of relative densities to define the median response and the sensitivity of the response to relative density and shaking intensity. The six centrifuge facilities involved in this test program included Ehime University (Japan), Korea Advanced Institute of Science & Technology (ROK), National Central University (Taiwan), Rensselaer Polytechnic institute (USA), University of Cambridge (UK), and Zhejiang University (PRC).  For each "experiment", a presentation file (video), prepared for LEAP-GWU-2022 workshop, is also included.  The presentations describe the centrifuge experiments in each participating centrifuge facility and the observed responses in details. A Web application for visualization of LEAP-GWU-2022 centrifuge data and their comparisons with user-generated numerical simulations is provided in the Code Project (PRJ-4245) published in Design-Safe.