cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds2-6c0s-5432
title: Aerodynamic Testing of a steel-lattice-type cat-head overhead tower 
title: in Downburst Effect on Transmission Towers

description: It's an aerodynamic testing of sectional models for a standalone transmission tower. The transmission tower is a steel-lattice-type cat-head overhead tower commonly incorporated in 110 kV, 150 kV, 220 kV, and 500 kV transmission lines. It has a height of 59.75m (196ft) with a square-shaped base of 13m (42.65ft), comprising four legs. These legs are made of steel single-angle members (L5x5x5/16, “L”-shaped cross-section) presented in Zone 1. The outer shape of the tower is pyramidal-shaped up to the bottom of the cat-head top portion, with a slope of 8:1. The tower is designed to carry three parallel conductors in which two conductors are bundled on each cross-arm, and the third conductor is suspended at the mid-point of the top girder through the cat-head window. 

Awards: Collaborative Research: Downburst Fragility Characterization of Transmission Line Systems Using Experimental and Validated Stochastic Numerical Simulations || CMMI 1762968 & CMMI 1762918
description: It's an aerodynamic testing of sectional models for a standalone transmission tower. The transmission tower is a steel-lattice-type cat-head overhead tower commonly incorporated in 110 kV, 150 kV, 220 kV, and 500 kV transmission lines. It has a height of 59.75m (196ft) with a square-shaped base of 13m (42.65ft), comprising four legs. These legs are made of steel single-angle members (L5x5x5/16, “L”-shaped cross-section) presented in Zone 1. The outer shape of the tower is pyramidal-shaped up to the bottom of the cat-head top portion, with a slope of 8:1. The tower is designed to carry three parallel conductors in which two conductors are bundled on each cross-arm, and the third conductor is suspended at the mid-point of the top girder through the cat-head window.
DOI: 10.17603/ds2-6h08-0p18
title: A Template for Multidisciplinary Virtual Reconnaissance Research—CONVERGE Extreme Events Research Check Sheets Series

description: This check sheet describes the function and importance of conducting virtual reconnaissance before entering the field after a disaster. Resources for secondary data collection, topics and information to pursue, and tips for compiling and sharing preliminary findings are provided. About the CONVERGE Extreme Events Research Check Sheets Series: The National Science Foundation-supported CONVERGE facility at the Natural Hazards Center at the University of Colorado Boulder has developed a series of short, graphical check sheets that are meant to be used as researchers design their studies, prepare to enter the field, conduct field research, and exit the field. The series offers best practices for extreme events research and includes check sheets that are free to the research community. More information is available at: https://converge.colorado.edu/resources/check-sheets.
DOI: 10.17603/ds2-mtn8-ra67
title: Extracting 4-D Engineering-Relevant Wind Information from Research Radar Measurements

description: Historically, engineering-oriented characterization of low-level atmospheric winds has been dependent upon obtaining relevant measurements from anemometry, but recent research has shown promise in extracting relevant scales using radar measurements. Central to the success of these new analysis concepts was the proper correction of the acquired data fields to account for momentum advection and the implementation of a robust space-to-time conversion methodology. Combined, these facets allowed researchers to interpolate high resolution “tower-like” time histories from across the entire radar analysis domain, resulting in a more comprehensive evaluation of flow field evolution. However, the previous methodologies failed to properly characterize highly complex wind flows that contain wind speed and direction gradients. This study built upon these previous methodologies to better characterize complex wind flows, while also preserving engineering-relevant scales of motion. The new methodology was implemented on a wind record collected from a passing thunderstorm on 24 May 2020 at Reese Center, TX and included several sharp wind speed and direction changes. Validation of the resulting dual-Doppler-derived second-by-second projections was completed through comparison with available anemometry. The results highlight the significant advantages of using research radar information to bolster engineering perspectives.
The data provided within this publication includes the final 4-dimensional dataset that resulted from the new space-to-time methodology along with the raw binary SIGMET file format as output from a Vaisala digital radar receiver from both of Texas Tech University’s mobile Ka radars, Ka1 and Ka2, that were used for data collection.  The raw SIGMET data are contained in directories labeled “raw_data” for the respective radar and the files are named according starting with the radar name (Ka1 or Ka2) followed by a date and time formatted as YYMMDDhhmmss.  The raw files can be opened by numerous software applications and programming languages, such as MATLAB and Python.  The final 4-dimensional data set contained within in the “4d_processed_dataset” features Network Common Deta Form (NetCDF) files that house the 3-dimensional dual-Doppler-derived wind speed and direction fields for each second of the time frame that was processed.  These files are labeled as dates and times in the format YYYYMMDD_hhmmss.  NetCDF files can be opened by numerous software applications and programming languages as well.  The “deployment_notes.txt” file contains information for each radar truck while deployed, such as their GPS location, the direction the front of the trucks are facing, and the pitch and roll of the radar trucks.  It also includes the truck relative sectors and elevation angles that each radar scanned for the duration of the event.
DOI: 10.17603/ds2-nwcb-r259
title: Research Experiences for Undergraduates (REU), NHERI 2023: Deep Learning Based Estimation of Peak Wind Pressures on Buildings from Short Duration Measurements

description: This project presents a deep learning model to predict peak wind pressures experienced on vertical building surfaces from statistical features extracted from short durations of boundary-layer wind tunnel data with varying wind conditions. The multi-layer perception model yielded excellent results in training and testing and is thus considered a viable solution to determining peak wind pressures from fewer data, proving its potential in reducing costs associated with boundary-layer wind tunnel experiments and computational fluid dynamics simulations. This project is unique as it utilizes short-duration measurements to determine peak wind pressures. The intended audience is those interested in machine learning applications to wind engineering.
DOI: 10.17603/ds2-vd28-pe79
title: Annual County-Level Housing Inventory Projections Comparing Linear Trend, ARIMA, and LSTM Methods
title: in Regional county-level housing inventory predictions and the effects on hurricane risk using long-short term memory (LSTM) methods and applied to the southeastern United States (US)

description: This simulation produced 10-, 20-, and 30-year projected housing inventory estimates for 1,000 counties in the southeastern United States in annual intervals. These housing inventory projections can be integrated with existing natural hazard risk models to evaluate how a changing building inventory affects regional risk.
description: This project provides estimates of the projected annual number of housing units for each of 1,000 counties in coastal states from Delaware to Texas over a 10-, 20-, and 30-year time horizon (2020-2029, 2020-2039, 2020-2049, respectively), using a long short-term memory (LSTM) neural network model. The absolute relative testing errors for the 10-, 20-, and 30-year projection models are 0.29%, 0.20%, and 0.25%, respectively. These housing inventory projections can be integrated with existing natural hazard risk models to evaluate the effects of a changing building inventory on regional risk.

This project supports the publication by Williams et al. (2022) and contains the data and scripts used to evaluate three candidate modeling methods (linear trend models, autoregressive integrated moving average (ARIMA) models, and LSTM models) that predict the annual number of housing units per county. Python and R scripts including data processing steps, forecast modeling methods, results processing, and analysis figures are provided. Please see the “Williams et al. 2022_Supplemental Section.pdf” document for background information supporting the files contained in this project.

Supporting Publication:

Williams, C. J., R. A. Davidson, L. K. Nozick, J. E. Trainor, M. Millea, and J. L. Kruse.. (2022). “Regional county-level housing inventory predictions and the effects on hurricane risk.” Natural Hazards and Earth System Sciences.
DOI: 10.17603/ds2-vw5f-t920
title: 62 Years Simulated Sakurajima Taisho Eruption Ashfall Deposit Data (1958-2019)

description: We present the ashfall deposit and airborne ash concentration dataset from ash dispersal simulation of a large-scale explosive volcanic eruption as a reference for ashfall disaster countermeasure. We select the Taisho (1914) eruption in Sakurajima volcano, regarded as the strongest eruption in Japan in the last century, as our case study to provide a baseline for the worst-case scenario. We employ one eruption scenario approach by replicating the actual eruption under various extended weather conditions to show how it would affect contemporary Japan. Accumulated ashfall has devastating impacts on both surrounding areas of the volcano and other regions, affecting airline transportation, socio-economics activities, and human health. Therefore, it is crucial to discover places with a high probability of exposure to ashfall deposition. This knowledge can help assess the additional risk in the infrastructures, human lives, and economic impacts to make a better volcanic eruption response plan. We generate the ash dispersal dataset by simulating the ash transport of the Taisho eruption scenario with a volcanic ash dispersal model and meteorological reanalysis data for 64 years (1958-2021). We explain the dataset production process and provide the dataset in multiple formats for broader audiences. We also clarify the validity of the dataset with its limitations and uncertainties. Please select the Version 2 of the dataset for the latest update.
DOI: 10.17603/ds2-0phb-dg64
title: Graph Network Simulator Datasets

description: The synthesis of graph networks and deep learning models present a unique opportunity to scale computationally intensive simulations beyond current capabilities. In their original work, Sanchez-Gonzalez et al. (2020) demonstrated that using graph networks with relatively simple deep learning models, so called “Graph Network-based Simulators” (GNS), could be used to model complex physics involving interactions between fluids, rigid solids, and deformable materials. Alongside their paper, Sanchez-Gonzalez et al. (2020) released a proof-of-concept implementation of their GNS written in TensorFlow version 1 and several datasets in the .tfrecord format for model training, validation, and testing. The data in this project includes three of the datasets from Sanchez-Gonzalez et al. (2020), in particular, Sand, SandRamps, and WaterDropSample, converted to the .npz format and  designed to integrate with a recently released port of the GNS from TensorFlow version 1 to PyTorch (Kumar and Vantassel, 2022). The PyTorch version of the GNS code is available at https://github.com/geoelements/gns.
DOI: 10.17603/ds2-1deq-4z12
title: STEEL-3dPointClouds: 3D-point cloud database for supporting automated quantification of residual life and reusability of steel beam-columns

description: This repository presents the STEEL-3dPointClouds, a large-scale database of deformed steel beam-column members obtained using high-fidelity physics-based numerical simulations. These simulations trace the inelastic deformations of hot-rolled wide-flange steel beam-columns under different loading protocols covering a range of responses, starting with no strength loss and up to at least 60% loss of load-bearing capacity for each considered steel member. Each of the ∼323k samples in the database is a unique point extracted from the hysteretic response of the loaded member and consists of the deformed member shape – represented as a 3D point cloud – along with the corresponding reserve capacity, characteristic stress and strain fields, and macroscopic responses such as axial shortening and base end moment.

This database was generated as part of a broader research program aimed at quantifying the residual life and reuse potential of structural steel members. It stands out for its use of 3D point clouds which directly provide comprehensive geometric information especially compared to 2D images. The data can be used to map deformed shapes of beam-column members to the response quantities of interest thus facilitating the development of automated inspection methodologies and computer vision tools. Furthermore, the database can be used to characterize beam-column nonlinear deformation characteristics and as well as define geometric tolerances for component reuse, amongst other applications.

The primary audience for this project includes researchers in structural engineering, mechanical engineering, machine learning, and computer vision. Additionally, the database can be of interest to professionals involved in infrastructure inspection and maintenance. The data offers a valuable resource for those working to develop automated, quantitative methods for assessing structural health and predicting remaining lifespan of steel components. However, given the comprehensive response data that is directly available or can be readily extracted from the database, the STEEL-3dPointClouds database can also be leveraged for development and benchmarking of other computer vision and deep learning methodologies that are not necessarily aimed at structural inspections.
DOI: 10.17603/ds2-3fn5-4b44
title: Systematic Literature Review Toolkit

description: Academic literature provides a vast landscape to explore. Students and academics may find the terrain treacherous and hard to navigate. A systematic literature review provides a roadmap and milestones. Xiao and Watson (2019) provide an overview of how to conduct a systematic literature review in eight steps. This archive currently provides tools that future researchers may find helpful for conducting step 1 - Formulate the problem, step 3 - Search the literature, step 4 - Screen for inclusion and step 6 - Extract data. Each step has a folder which contains a short overview report and example data files. Step 3 includes a Jupyter Notebook to help automate the literature review process, basic code editing skills are necessary to modify the notebook. More tools will be developed for step 2 - Develop and validate the review protocol, step 5 - Asses quality, and step 7 - Analyze and synthesize data. This archive provides tools that will enhance the quality, replicability, reliability, and validity of literature reviews.
DOI: 10.17603/ds2-6vmm-2g61
title: HTI Interview De-Identification Guide
title: in Evaluation of hurricane threats and impacts visualizations through interviews with broadcast meteorologists and emergency managers

description: The overall goal of this project was to improve the National Weather Service's (NWS) Hurricane Threats and Impacts (HTI) visualizations through a user-driven, iterative design process, in order to improve risk communication and decision-making during multi-hazard tropical cyclone events. The component of the project documented here focused on evaluating a prototype redesign of the NWS HTI graphics with broadcast meteorologists and emergency managers in hurricane-prone regions. The prototype was co-developed with our research partners at the University of Miami based on initial feedback from members of the public, as well as best practices in visual design and the various disciplinary expertise of our research team. We conducted 20 interviews in total - 10 with broadcast meteorologists and 10 with emergency managers. The interviewees were recruited based on variability in several dimensions, including how long they’ve worked in their hurricane-related job roles and characteristics of the areas they served (e.g., coastal vs. inland, urban vs. rural, local vs. regional). Interviewees were first asked about their perspectives on the current NWS HTI graphics (at the time of the interviews), including their uses, interpretations, and feedback for improvement. They were then shown the prototype redesigned HTI graphic and were again asked about their overall interpretations, likes and dislikes, potential uses, and needs and constraints that would need to be addressed to facilitate their use of the graphic. This repository includes the semi-structured interview protocol and stimuli used during our interviews, as well as a guide for how we de-identified interview transcripts to protect interviewee confidentiality.
description: This document describes our process for de-identifying interview transcripts for confidentiality of interviewees.
DOI: 10.17603/ds2-ae92-6v90
title: StEER - 14 March and 25 April, 2019 Cyclones Idai and Kenneth in Mozambique: Event Briefing

description: Two major tropical cyclones made landfall on the Mozambique coastline within a six weeks of each other resulting in over 1000 fatalities and causing significant wind and flooding damage to the coastal cities of Beira and Pemba. The objectives of this Event Briefing are: 
(1) to summarize the meteorological characteristics of the two cyclones, (2) to overview damage to buildings and other infrastructure, and (3) to summarize key lessons learned.
DOI: 10.17603/ds2-bqvw-x969
title: Research Experiences for Undergraduates (REU), NHERI 2023: Water Content Measurement Calibration for Fly Ash Centrifuge Model During Water Migration

description: The focus of this project is to develop a calibration procedure for moisture probes used in fly ash centrifuge experiments. Centrifuge experiments allow researchers to physically simulate the behavior of soils deposits under varying conditions. Failure of fly ash impoundments have motivated studies into fly ash containment facilities. Dangerous debris flows after containment failure are caused by the liquefaction of saturated fly ash deposits. The dewatering of fly ash ponds is one strategy for reducing risks and preventing future destabilization. Current centrifuge experiments partly focus on how varying the water table within a deposition can impact fly ash properties. Soil moisture probes are embedded within the model to monitor the fluctuation of the fly ash water contents. This paper will focus on the relationship between readings from the modified GS3 soil moisture probes and the gravimetric water content. Measuring the water content of the tested fly ash, Ash #1, has been challenging due to its unusually high dielectric permittivity levels. Previous attempts to calibrate Ash #1 were not successful due to its unique properties. The calibration curves derived in this project can be used to accurately interpret the sensor readings from previous experiments. The audience for this paper would be geotechnical researchers interested in monitoring the water content of fine ground soils with high salinity levels.
DOI: 10.17603/ds2-zeb9-9n84
title: Research Experiences for Undergraduates (REU), NSF NHERI 2024:Understanding Social Vunerability and  the Complexities of Homelessness in Natural Hazards and Disasters

description: This project is a free online training module available on CONVERGE to address the vulnerabilities of homeless populations in the context of natural hazards and disasters. It aims to enhance ethical practices in disaster management by focusing on the intersection of homelessness and social vulnerability. The data can be reused by educators, researchers, and practitioners for training or as a foundation for further studies. Its uniqueness lies in its focus on the often-overlooked ethical implications of homelessness in disaster contexts. The primary audience includes disaster researchers, emergency responders, and students in related fields.
DOI: 10.17603/ds2-1a86-h371
title: UCD4, UCD5 and UCD6 - UC Davis Experiments
title: in LEAP-ASIA-2019 Experiments (Liquefaction Experiments and Analysis Projects)

description: Building upon the database of LEAP-UCD-2017, twenty centrifuge model tests of liquefaction and lateral spreading are shared and compared in this archive. Some of the experiments were conducted according to the conventional centrifuge scaling laws; others were conducted according to a generalized scaling law. Please see the general report section of the published project for an overview comparison and background of all of the experiments. The comparisons presented in the general report section will serve as an index to help the users find individual experiments of interest.  This data from the model tests is published as ten separate experiments in this archive (one experiment per centrifuge facility). Each "experiment" includes one or more model tests and each model test includes between one and three destructive shaking events.
All of the tests modeled a 4 m thick deposit of Ottawa F-65 sand with a 5-degree surface slope in a rigid box. 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 ten centrifuge facilities involved in this test program included Cambridge University (UK), Ehime University (Japan), IFSTTAR (France), NCU (Taiwan), KAIST (Korea), Kyoto University (Japan), RPI (USA), UC Davis (USA), Tokyo Institute of Technology (Japan) and Zhejiang University (China).
description: Lateral spreading of a submerged slope and validation of generalized scaling laws for LEAP-ASIA-2019
DOI: 10.17603/ds2-6xp3-xj95
title: Repeated Wind Tunnel Section Model Tests of a Closed-Box Bridge Deck – Scanlan Derivatives

description: Scanlan derivatives (i.e. flutter derivatives) are force coefficients employed for aeroelastic analysis of long-span bridges. These coefficients are usually determined through wind tunnel tests. The data set shared herein is related to the test results of a set of free decay aeroelastic experiments, carried out in the wind tunnel of Northeastern University, Boston, MA (USA). In order to examine the variability and identify experimental errors, each test was repeated thirty times at various flow speeds. Experimental variability enables further evaluation of uncertainty propagation in aeroelastic systems, structural reliability and performance-based wind engineering for long-span bridges. The main experimental results are included in the enclosed data file.