cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds2-877y-cc73
title: Data Files for San Francisco Bay Area (SFBA) Ground Motion Study

description: This dataset is part of an on-going effort to develop a sub-regional site response model for the San Francisco Bay Area (SFBA) in northern California. We performed semi-automated, component-specific data processing with the aim of optimizing usable bandwidth following procedures consistent with Next Generation Attenuation (NGA) project protocols for 28 recent earthquakes not included in prior data collection efforts (Bozorgnia et al. 2014; Wang 2020; Buckreis 2022). These events were selected because they were recorded by at least one station within the SFBA, although all available records for each event were processed regardless of their site locations. The present dataset includes computed intensity metrics including peak velocity (PGV) and acceleration (PGA), 5% damped spectral accelerations (PSA), effective amplitude spectra (EAS) (Kottke et al. 2021), cumulative absolute velocity (CAV), thresholded cumulative absolute velocity (CAV5), Arias intensity (IA), and times to achieve every 5th percentile of the maximum Arias intensity (used to compute significant durations); metadata related to source, path, and site conditions; intensity metrics are computed for the as-recorded components (H1, H2, and V) and for the minimum, median, and maximum combined horizontal components as defined by Boore (2010) (RotD0, RotD50, and RotD100, respectively). Note that the unprocessed time-series in their original raw format can be obtained from the Incorporated Research Institutions for Seismology (IRIS; Trabant et al. 2012). The processed time-series data can be obtained at https://gmdatabase.org.
DOI: 10.17603/ds2-8tgq-7h70
title: Simulating the Effects of Earthquake Shaking Characteristics on Lateral Spread Displacements
title: in Numerical modeling of lateral spread displacements at free-face sites using OpenSees

description: This data set contains files from numerical simulations of free-face lateral spread sites that were analyzed using OpenSees.  The main goals of this study were to investigate the effects of site geometry, soil relative density, and earthquake shaking characteristics on lateral spreading displacements.  As such, the data set is split into two simulations:  (1) A suite of 132 models were analyzed with one ground motion to investigate the effects of site geometry and soil relative density on lateral spread displacement patterns, (2) A subset of 6 of the site geometries from the first set of simulations was analyzed with 2 soil relative densities and 38 ground motions for a total of 456 models to study the effects of earthquake shaking characteristics on lateral spread displacements.
description: This data set contains files from a suite of 456 OpenSees models that were analyzed to study the effects of ground motion characteristics on lateral spreading displacements.
DOI: 10.17603/ds2-asez-6v23
title: Field Assessment Structural Teams: FAST-1, FAST-2
title: in StEER - Hurricane Dorian: Field Assessment Structural Team (FAST) Dataset

description: Field teams conducted building performance assessments, deployed rapid-imaging technologies, and performed coastal site hazard characterization surveys. Buildings captured in this dataset include single- and multi-family residential, commercial, industrial, and governmental facilities. Both failures and successes were documented throughout clusters in Abaco Island, Grand Bahama, and several smaller cays. Associated hazard intensity indicators (e.g., high water marks) were also captured where present.
description: This dataset includes door-to-door damage assessments and aerial and terrestrial imagery collected by StEER Field Assessment Structural Teams (FASTs) responding to the passage of Hurricane Dorian over the Bahamas. The data was collected in two missions, September 24-25 and October 5-8, 2019. The missions include data for Grand Bahama Island and Great Abaco Island, as well as surrounding out islands. Collections include this data and a Data Report summarizing the equipment deployed, as well as methodology and resolutions.
DOI: 10.17603/ds2-fx45-dd16
title: Code and Data for a Real-time Structural Seismic Response Prediction Framework based on Transfer Learning and Unsupervised Learning

description: Conventional data-driven methods for predicting the seismic response of structures often require extensive data and computational resources. To address these challenges, we here publish a deep learning framework - code and data - that can efficiently and accurately predict the structural seismic responses. The framework overcomes aforementioned limitations by utilizing transfer learning based on the most relevant knowledge determined via the unsupervised learning technique. The framework leverages the seismic information history database to identify the most similar previous earthquake, and subsequently transfers the corresponding knowledge from the Structural Seismic Response network (SSR net) to predict structural responses caused by a new earthquake. This method significantly reduces the need for extensive training and data collection and provides efficient predictions. Case studies demonstrate the framework's ability to predict seismic structural responses effectively. The framework can reliably capture the complex nonlinear dynamics of structures under seismic loads and offer significant potential for advancing seismic fragility analyses and reliability assessments.
DOI: 10.17603/ds2-gjcq-n790
title: Natural Hazards Research Summit 2024: Improving Low-Cost Air Quality Monitoring Network through Probabilistic Spatiotemporal Modeling: Enabling Smart Cities in Coastal Regions of Environmental and Industrial Change

description: In this poster, we present a new data-driven approach that integrates spatiotemporal information to increase the data quality of post-processed particulate matters 2.5. This method assimilates multiple data channels from nearby monitoring stations using a probabilistic spatiotemporal model that describes a two-dimensional advection and diffusion process with stochastic uncertainties. The assimilated data consists of two types of measurements: highly accurate readings at distant stations and fairly accurate readings at the ambient locations in the area of interest. We apply the method to post-process monitored PM2.5 concentrations from low-cost sensors (SCI-608, SailBri Cooper Inc.) deployed near Corpus Christi, Texas. We test the prediction performance by comparing the post-processed low-cost sensor data with co-located regulatory-grade sensor measurements. Our findings demonstrate that integrating heterogeneous and multi-modal datasets in the post-processing of sensor data can improve data quality and accuracy of the monitoring network while enabling uncertainty-aware predictions.
DOI: 10.17603/ds2-hd41-qs65
title: Research Experiences for Undergraduates (REU), NHERI 2023: Assessing Liquefaction Potential in Kawaihae Harbor Using CPT

description: After the occurrences of two earthquakes at Kawaihae Harbor in Hawaii, a review of methods used to calculate the probability of liquefaction based on earthquake severity was made, with an emphasis on the Cone Penetrometer Testing (CPT) cone resistance, sleeve friction, and pore pressure. This paper analyzes the site layering at Kawaihae Harbor following the 2006 earthquake through CPT testing, and whether CPT can serve as a deterministic and simplified method for predicting liquefaction in calcareous deposits making it unique as all current CPT methods test liquefaction prediction against silica sands. This research is aimed to help geotechnical engineers assessing and learning about different methods to assess liquefaction potential in various sand types.
DOI: 10.17603/ds2-jx64-ce09
title: Research Experience for Undergraduates (REU), NHERI 2021: Coupling Processed-Based and Neural Network-Based Models for Studying Coastal Hazards

description: This project studies the efficiency of a neural network-based model in comparison with a physics-based model. Hurricane Ike storm surge data was used within the GeoClaw package to produce a series of outputs, then a neural network was trained to replicate that data. The data in this project can be reused to further study the accuracy, loss, computational time, and storage compatibilities of neural networks in comparison to physics-based models. This project is unique as it is working to combine previously used forecasting methods to create a more efficient, hybrid method. The main audience is those researching storm surges and different forecasting models.  A broader audience would include those interested in the uses of machine learning systems.
DOI: 10.17603/ds2-vnac-se13
title: Research Experiences for Undergraduates (REU), NHERI 2022: Image Analysis of Settlement-Induced Cracking in Centrifuge Models

description: Image analysis is becoming more commonly used to observe, gather, and understand meaningful information from natural hazards such as slope failures, dam failures, and landslides. Traditionally, the initial and final positions of points are known in a test, but with advanced software, it is possible to gather continuous data on a point on the surface. Three tests are conducted using high-speed cameras and image analysis software to extract information from a recording, such as 3D stereophotogrammetry and 3D displacements. The results are analyzed by comparing experimental data collected via software to physical measurements, to then determine software reliability and accuracy for a centrifuge levee model test.
DOI: 10.17603/ds2-x9kt-jy53
title: Identifying Hurricane Evacuation Intent on Twitter

description: Evacuations have a significant impact on saving human lives
during hurricanes. However, as a complex dynamic process, it
is typically difficult to know individual evacuation decisions
in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them
to understand individual evacuation behavior. In this paper,
we collect tweets during Hurricane Irma in 2017 and train a
text classifier in an active learning way to distinguish tweets
expressing positive evacuation decisions from both negative
and irrelevant ones. Additionally, we perform a demographic
analysis and content clustering to investigate the potential
causes and correlates of evacuation decisions. The results can
be used to help inform planning strategies of emergency response agencies.
DOI: 10.17603/ds2-xz1j-nm14
title: StEER - 28 MAY 2019 LINWOOD, KS EF4 TORNADO: FIELD ASSESSMENT STRUCTURAL TEAM (FAST) EARLY ACCESS RECONNAISSANCE REPORT (EARR)

description: An EF4 tornado occurred in eastern Kansas on May 28, 2019 causing considerable damage in Linwood, Kansas, and surrounding areas. Damage mostly consisted of single-family residential structures, although one daycare facility south of Lawrence, Kansas, was destroyed. The tornado ultimately resulted in 18 injuries and no fatalities.
This report provides a preliminary summary of the damage and impacts of the tornado, and details on-site investigations conducted by researchers from the University of Kansas and the University of Missouri in Kansas City. The on-site investigations were funded by the Structural Extreme Events Reconnaissance (StEER) network. Founded under an NSF EAGER grant, StEER seeks to build societal resilience by generating new knowledge on the performance of the built environment through impactful post-disaster reconnaissance disseminated to affected communities.
DOI: 10.17603/ds2-y7nj-9t47
title: Pre-Facilitated Session Survey Instrument
title: in Building State Voluntary Organizations Active in Disaster (VOAD) Capacities to Protect Children in Emergencies: An Evaluation Research Project

description: As part of the larger Save the Children evaluation, our research team developed and disseminated an online survey to VOAD members and those partnered or affiliated informally with Arkansas and Nebraska state-level VOADs. The intent of this survey was to assess individual and organizational levels of disaster preparedness and the state of child-centered disaster preparedness activities. The Natural Hazards Center team created one survey, which was then updated to be specific to each state. The survey is divided into the following six sections: (a) organizational characteristics, VOAD involvement, and experience with disaster; (b) knowledge and awareness about children in disasters; (c) capacity for child-focused disaster response; (d) readiness for child-focused disaster response; (e) perceptions about child-focused disaster response; and (f) demographic information. We used the online survey platform Qualtrics to disseminate surveys through anonymous survey links. These links were sent to Arkansas and Nebraska VOADs and their partners on December 3, 2018. In all, a total of 24 surveys in Arkansas (7 of which were partially completed), and 46 surveys in Nebraska (13 of which were partially completed) were submitted via Qualtrics by December 17, 2018. This publication includes the final survey instrument, which may be of interest to other evaluation researchers.
description: Between 2018 and 2020, the Natural Hazards Center at the University of Colorado Boulder conducted an evaluation entitled: Building State Voluntary Organizations Active in Disaster (VOADs) Capacities to Protect Children in Emergencies. This project was designed to assess the capacity of state-level VOADs to address children’s needs before, during, and after disasters. This project involved multi-method research in the focal states of Arkansas and Nebraska and in collaboration with members of VOADs, emergency management, Save the Children, and other child-serving organizations in both states. The evaluation team conducted participatory engagement exercises, survey research, secondary data analysis and GIS mapping, and an interorganizational network analysis survey. This project includes research instruments and the final reports produced as part of this project. The envisioned audience for these materials includes researchers, emergency managers, and professionals who work for child-serving organizations.
DOI: 10.17603/ds2mx1q
title: ECJ Building vibration monitoring
title: in Explore UT - vibration monitoring of the ECJ building at UT Austin

description: NHERI@UTexas participated in the Explore UT on March 3rd, 2018.  Explore UT is annual campus wide event, hosted by the University of Texas at Austin, which aims to inform students, parents, teachers and community members from across the state about the importance of the public research institution and higher education in Texas. T-Rex was brought to Parking Lot # 25 next to the Ernest Cockrell Jr. Hall (the ECJ Building) on UT main campus to demonstrate earthquake engineering research for the Explore UT event.  A total of 30, 1-Hz geophones were placed in the ECJ building to record building motion from T-Rex shaking and ambient vibrations.  Data recorded from these geophones are made available in this project.
DOI: 10.17603/ds2sx1f
title: Dam-break swash
title: in Large eddy simulation of dam-break-driven swash on a rough-planar beach

description: Turbulence characteristics in the swash zone are investigated using a 3-D large eddy simulation
model. The numerical simulation was carried out using OpenFOAM which solves the filtered Navier-Stokes
equations for two immiscible fluids (air and water) with a standard Smagorinsky subgrid-scale closure. 
This work is published in Journal of Geophysical Research: Oceans (2017), doi:10.1002/2016JC012366.
The case setup, simulation data are provided here to facilitate successive works.
description: InterFoam
DOI: 10.17603/ds2-0ksb-yy40
title: ADCIRC Simulations of Synthetic Tropical Cyclones Impacting Coastal Louisiana
title: in Surrogate Modeling for Storm Surge Prediction

description: Planners who wish to manage coastal flood risk with long-lived infrastructure (e.g., levees, floodwalls) under a constrained computational budget face a tradeoff. Simulating a large number of future time periods or scenarios with different assumptions about land subsidence, sea level rise, land accretion, imposes a limit on how many storm simulations can be run in each scenario and time period. Machine learning approaches have been developed to reduce the computational burden of predicting storm surge from many tropical cyclone events, but prior efforts focus on predicting surge as a function of storm parameters on a single landscape. This data set represents inputs and training data to a deep learning model that also incorporates landscape characteristics and boundary conditions (e.g., mean sea level). The model is informed by a dataset of peak surge elevations from Advanced Circulation (ADCIRC) hydrodynamic simulations of coastal Louisiana in eleven scenarios: a 2020 baseline and decadal time slices from 2030 to 2070 under two scenarios varying land subsidence and sea level rise rates. Training on ten scenarios to make predictions on the eleventh yields a grand RMSE of 0.086 m and grand MAE of 0.050 m over 90 storms per scenario and over 94,000 geospatial locations. We also aggregated the 90 storms in each scenario to generate an annual exceedance probability distribution; a two-sided Kolmogorov-Smirnov test comparing AEP estimates from the model predictions to the original ADCIRC simulations rejected the null hypothesis that the predictions and ADCIRC AEP values were drawn from the same distribution only 1.1% of the time.
description: ADCIRC simulations of peak storm surge elevations from 645 synthetic tropical cyclones (TCs) in a landscape representing coastal Louisiana in 2020, and 90 synthetic TCs in ten future landscapes representing five decadal time slices (2030 to 2070) under two scenarios varying in assumptions about sea level rise, land subsidence, and other environmental factors. These simulations were executed to support development of Louisiana's 2023 Coastal Master Plan.
DOI: 10.17603/ds2-2qkc-3q70
title: Fiber optic monitoring of offshore wind turbines using OFDR and Phi-OTDR technologies
title: in Structural health monitoring of offshore wind turbines using distributed acoustic sensing (DAS)

description: The use of distributed fiber optic monitoring using Rayleigh based technologies for the monitoring of off shore wind turbines needs to be validated. In this experiment, a four-point bend test was conducted, as well as a shake table test. For both tests, OFDR and phi-OTDR technologies were used and their respective datasets were compared. The data can be reused to replicate figures used for the journal article regarding these experiments, as well as explore other quantities of interest in the data that have been unexplored.
description: The project is on the validation of distributed fiber optic monitoring of offshore wind turbines for the use in Structural Health Monitoring (SHM), sponsored by the California Energy Commision (CEC). This data can be used for system identification of offshore wind turbines, as well as exploratory data analysis (EDA). The dataset contains data collected by two distributed fiber optic technologies for shake table tests of a wind turbine undergoing typical dynamic strain profiles of an offshore wind turbine. This project is the first-of-its-kind validation experiment for distributed fiber optic sensing using DAS for offshore wind turbine towers. This audience is for the SHM, distributed fiber optic sensing, and experimental community. The code can be found here: https://github.com/DeJong-Group/structural-health-monitoring-of-offshore-wind-turbines-using-distributed-acoustic-sensing-DAS.git
DOI: 10.17603/ds2-5tft-9b17
title: Undergraduate Research Experience (REU) 2019: CPT Behavior of Well-graded Granular Soils

description: An investigation of the influence of soil gradation (particle size distribution) on the tip resistance profiles of cone penetration test used to characterize in situ soils. Centrifuge modeling was necessary to simulate high stresses found deep in the earth. Accomplishments addressed in this paper have an emphasis on the preparation and execution of centrifuge model construction.
DOI: 10.17603/ds2-jt8d-sx39
title: Undergraduate Research Experience (REU), NHERI 2019: A Data Processing and Visualization Framework for Hurricane Debris Modeling

description: Participated as a member of Dr. Jamie E. Padgett's research lab at Rice University in an experience that developed visual models for hurricane debris modeling. Data processing and visualization of debris are recorded in the paper "A Data Processing and Visualization Framework for Hurricane Debris Modeling." I accomplished the production of visual models to aid in the spatial understanding of hurricane damage with the support of my fellow lab members.
DOI: 10.17603/ds2-68d6-2x23
title: Deformation Mechanisms in Stratigraphically Layered Liquefiable Deposits Near Buildings on Shallow Foundations

description: This project presents a numerical database of 2D, 3D, and free-field soil column, fully-coupled, nonlinear finite element analyses performed in the object-oriented, open-source, FE computational OpenSees platform. The database is comprised of a numerical sensitivty study performed to investigate the influence of layering, stratigraphic variations in one horizontal direction, and vertical changes in the groundwater table near and away from two structures with different characteristics, on a mat and a basement foundation. The initial numerical sensitivity study was expanded with 2D plane-strain representations of the 3D soil-foundation-structure system and a larger suite of ground motions.
DOI: 10.17603/ds2-p9td-r942
title: Blind Prediction Experiments
title: in Collaborative Research: Simulating Crack Propagation in Steel Structures Under Ultra-Low Cycle Fatigue and Low-Triaxiality Loading from Earthquakes and Other Hazards

description: Experimental test data from this project has been used to develop, calibrate and validate computational models to simulate ductile crack initiation and propagation in steel structures under ultra-low cycle fatigues.  The resulting models can be used in fundamental and applied research to evaluate earthquake-induced fracture limit states in steel structures.  The experimental test data can be used by researchers to calibrate and validate alternate computational models, which they have developed.
description: Tests performed to provide validation data for computational model to simulate crack propagation in steel specimens under monotonic and cyclic loading. Data captured includes applied load and a measure of surface displacement, obtained from a 2-inch linear extensometer. Data may be reused for future research in studies of crack propagation in steel.
DOI: 10.17603/ds2-xg0h-w556
title: 2nd NHERI-EUCentre Collaboration Meeting (September 12-13, 2019)

description: Researchers and administrators from NHERI and EUCENTRE met September 12-13, 2019, in Pavia, Italy, for the
second NHERI-EUCENTRE Collaboration Meeting.  Topics included possible future projects for the
collaboration, presentations on new post-event reconnaissance tools, hybrid simulation, and the outcome
of recent hazards engineering research.