cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17603/ds2-ksng-y953
title: FEMA P-2335: Database of repaired reinforced concrete walls

description: Welcome to the Database of repaired reinforced concrete walls! This database is a collection of experimental programs on reinforced concrete (RC) walls repaired after different levels of visible damage. A key purpose of the database is to enable practising engineers engaged in RC building repairs following an earthquake to easily identify past research on different repair techniques.

This database is used in the application of FEMA P-2335 as part of the guidelines for post-earthquake assessment of RC structures.

The work forming the basis for this publication was conducted pursuant to a contract with the Federal Emergency Management Agency. The substance of such work is dedicated to the public. The authors are solely responsible for the accuracy of statements or interpretations contained in this publication. No warranty is offered with regard to the results, findings, and recommendations contained herein, either by the Federal Emergency Management Agency, the Applied Technology Council, its directors, members or employees. These organisations and individuals do not assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any of the information, product, or processes included in this publication.

Version v2 includes variables in US units.
DOI: 10.17603/ds2-nadf-h758
title: 2023 Texas, Oklahoma, and Kansas Dataset for Investigating NGA-East GMM and Site Amplification Models

description: This dataset supported a USGS-funded project to investigate bias in earthquake ground motions predicted for Texas, Oklahoma, and Kansas (TOK) using NGA-East ground motion models (GMMs) and site amplification models. The dataset includes 12,771 earthquake ground motion recordings from 344 events, occurring between January 2005 and December 2022 within the TOK region. The metadata of events, stations, and ground motions as well as the 5% damped pseudo-spectral acceleration (PSA) of the ground motions are presented in the files of this project.
DOI: 10.17603/ds2-pnw4-9y53
title: A Knowledge Transfer Enhanced Ensemble Approach to Predict the Shear Capacity of Reinforced Concrete Deep Beams without Stirrups

description: This study proposes a novel learning algorithm, the Transfer Ensemble Neural Network (TENN) model, to increase the performance of shear capacity predictions on small datasets, reducing the need for these expensive physical tests. By incorporating ensemble learning and transfer learning, the TENN model is designed to control the high variability inherent when trained on small amounts of data. The novel TENN model is validated to predict the shear capacity of deep RC beams without stirrups across varying data availability levels. Knowledge acquired through pretraining a model on slender RC beams is utilized for training a model to better predict the shear capacity of deep RC beams without stirrups. To evaluate the performance of the TENN model, three baseline models are developed and examined across multiple data availability levels. The novel TENN model outperforms the baseline models, particularly when trained on a very limited dataset. Furthermore, the proposed algorithm achieves a higher accuracy than the currently accepted design standards in accurately predicting deep RC beams' shear capacity and demonstrates the capabilities of the TENN model to extrapolate on other domains where large-scale or physical testing is cost-prohibitive.
DOI: 10.17603/ds2-247n-w437
title: Human-AI Teaming for Big Data Analytics to Enhance Response to the COVID-19 Pandemic: Online Interviewing Protocol

description: This research focuses on capturing the ephemeral data from a variety of social media sources and our two research thrusts include: 1) online observations of Community Emergency Response Team (CERT) volunteers and a manager (a collaborator on this project) using think-aloud and cognitive interviewing strategies to reveal the real-time mental models used to make coding decisions for annotation tasks; and 2) an empirical analysis of different sampling algorithms for active (machine) learning paradigms to develop a typology of machine errors under diverse contexts that affect the quality of human decision making for annotation. This research will generate design guidelines that bridge the gap between the mechanisms used for real-time data processing with AI models and the understanding of context contributed by a human user teaming with the AI models. Using theories of human decision-making combined with knowledge of how AI functions, this project provides a real-time, mid-disaster examination of 1) how humans understand, process, and interpret social media messages, and 2) how to refine AI algorithms to optimize active learning paradigm. This understanding will provide a theoretical framework enabling future research to develop protocols to optimize human-AI teaming by using concepts such as motivation and information theory. This work can help emergency managers conduct better training of their CERT volunteers and other annotators and provide clearer guidelines for how to communicate the unique value that humans bring to the annotation process for AI systems. Both our protocols and developed understanding of how humans interact with AI systems will be helpful for global health organizations, local and state-level disaster decision-makers, as well as provide direction for the vast CERT network in the United States.
DOI: 10.17603/ds2-3rf5-sd58
title: ISLAND: Interpolating Land Surface Temperature using land cover (Public Data Products)

description: This work is published on Remote Sensing Applications: Society and Environment: https://doi.org/10.1016/j.rsase.2024.101332


Abstract
Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST). Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from the Earth’s surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable LST images for downstream applications, making it impractical to perform intricate time-series analysis of LST. In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Interpolating Land Surface Temperature using land cover. Our approach uses LST images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler’s first law of Geography, ISLAND predicts occluded LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.
DOI: 10.17603/ds2-5mdw-8n50
title: Testing of Building Models in a Boundary-Layer Wind Tunnel
title: in Benchmark Study of Tornado Wind Loading on Low-Rise Buildings with Consideration of Internal Pressure

description: This experiment takes the WERFL building model as the research object to test the wind loading in the wind tunnel. According to the different openings and porosity of the WEFRL building model, the experiment was divided into 13 cases, each of which contains 19 wind orientations, which are from 0 degrees to 180 degrees with a gradient of 10 degrees. This experiment will get the external pressure coefficients, internal pressure coefficients, and force coefficients of WERFL building model under different openings, different porosity, and different wind direction. The goal of this experiment aims to compare the differences between loading on low-rise buildings by tornadic and boundary-layer-type winds.
description: The goal and the specific objectives of this project will be achieved through implementation of a research plan composed of experimental, analytical and numerical components. This research plan consists of three main tasks. The first two tasks will produce the necessary data needed through systematically designed experiments in a tornado simulator and a boundary-layer wind tunnel. The third tasks will characterize, model and simulate tornado loading through methods based on an innovative framework to produce the indicators, such as the peak pressure and force coefficients, that are needed for the evaluation of the effects of the internal pressure and the non-stationary, non-Gaussian, nature of the fluctuation of the loading on the overall loading on buildings by tornado winds. A comparison of these indicators with the corresponding ones for the building models tested in the boundary-wind tunnel can highlight the differences between loading on low-rise buildings by tornadic and boundary-layer-type winds. Finally, this third task will also provide a dataset as well as methods that will be needed in performance-based evaluation of low-rise buildings in tornado hazards.
DOI: 10.17603/ds2-835m-zp94
title: Combined Geotechnical and Geophysical Investigation of Texas Rivers Post Hurricane Harvey.
title: in Combined Geotechnical and Geophysical Investigation of Texas Rivers Post Hurricane Harvey

description: Hurricane Harvey caused major flooding events across the Gulf of Mexico, leading to geomorphological and site characteristics changes at several rivers. Post hurricane, scour holes and erosion were observed at bridge piers, river embankments, sheet pile walls, boulder walls, and other infrastructure with submerged foundations. To investigate the the local geomorphological changes and sediment dynamics during extreme events, a combination of geotechnical and geophysical methods, that can be deployed from small vessels and shallow waters, were used. The geotechnical methods consisted of a portable free fall penetrometer Bluedrop that penetrates through the riverbed, in addition to a grab sampler to collect disturbed samples. The acoustic methods consisted of a chirp sonar system  deployed to resolve the riverbed strata; an acoustic Doppler current profiler (ADCP) Nortek AquaDopp HR  to measure the water flow velocity during the survey period; and a side scan sonar  to provide riverbed imaging.
description: Hurricane Harvey caused severe wind damages and flooding in Fall 2017. In fact, the associated flooding can be classified as a 5,000 year event. Associated to the flooding, significant sediment relocation processes occurred. Sediment erosion around submerged structures and foundations, commonly referred to as scour, represents a significant risk to the stability of infrastructures. During Hurricane Harvey, early reconnaissance teams observed significant scour and erosion at bridge piers, river embankments, sheet pile walls, boulder walls, and other infrastructure with submerged foundations. However, safety constraints limited on site investigations while water levels were still elevated. An issue of post-event scour and erosion investigations is the refilling of scour holes. This means that when with receding water levels and flow velocities, sediment transported in suspension settles and infills the scour holes, often leading to false assessments of the magnitude of erosion during the event. Additionally, the infill is freshly deposited and often weak, potentially providing a false image of stability. This award will support the collection of perishable field data investigating scour and erosion, as well as post-event infill at locations impacted by flooding and storm surge during Hurricane Harvey. The collected data will advance the understanding of scour, erosion, and infill during extreme flooding events, and therefore, contribute directly to the improvement of risk assessment.
DOI: 10.17603/ds2-94jv-8j27
title: Regional Earthquake-Induced Landslide Assessments for Evaluating Seismic Risk to Critical Infrastructure

description: In California, several thousands of kilometers of spatially distributed lifeline infrastructure, such as natural gas pipelines, power lines and transportation network, traverse regions that are susceptible to earthquake-induced landslides, creating a need for a state-wide seismic risk assessment of natural gas pipelines. These assessments require the spatial distributions of earthquake-induced landslides within these regions as well as the characteristics of the landslides (such as displacement amount, size, and direction of movement) as inputs into pipeline fragility models. To accurately estimate displacements on a regional scale, there is a need for adequate characterization of shear strength properties of geologic units, slope properties and groundwater table. This project includes datasets such as geologic maps, digital elevation model, shear strength parameters and ground motion data from the USGS ShakeMap that were used for displacement computations across multiple regions in California. These datasets and Python scripts are available to researchers who may be interested in estimating displacements from earthquake-induced landslides on a regional scale.
DOI: 10.17603/ds2-k2bp-t724
title: Preliminary Virtual Reconnaissance Report
title: in StEER - Crucecitas, Mexico Mw 7.4 Earthquake: Preliminary Virtual Reconnaissance Report (PVRR)

description: The initial product of the StEER response to the 2020 Oaxaca Mexico Earthquake is this Preliminary Virtual Reconnaissance Report (PVRR), which is intended to (1) Provide an overview of the main characteristics of the event; (2) Summarize the seismicity of this region of Mexico; (3) Provide a brief overview of seismic codes and construction practices in Mexico; (4) Summarize preliminary reports of damage to wide-range of structures and infrastructure; and (5) Provide some preliminary recommendations.
description: This project encompasses a Preliminary Virtual Reconnaissance Report, a virtual StEER response mechanism that summarizes the event and any key lessons that can be learned or reemphasized from the 23 June 2020 Mw 7.4 Oaxaca, Mexico Earthquake.
DOI: 10.17603/ds2-kgwv-bj26
title: Testing of a T-shaped Precast Concrete Rocking Shear Wall under Multi-directional Loading
title: in Laboratory testing of a T-shaped unbonded post-tensioned precast concrete rocking wall

description: Precast rocking shear walls with unbonded post-tensioning provide a low damage alternative to traditional monolithic reinforced concrete shear walls. However, laboratory testing of precast rocking shear walls has been limited to rectangular cross-section, despite the widespread use of non-rectangular shear walls in elevator cores or around hallways. This experimental study supported by National Science Foundation (NSF) aims to address this knowledge gap by conducting laboratory testing of a T-shaped precast rocking shear wall. The T-wall specimen was constructed at 1/3rd scale of a prototype wall and tested under multi-directional loading up to 1.50% drift. The T-wall specimen was 5 in. thick, 5 ft. and 6 ft. wide in flange and web direction respectively, with a loading height of 136.75 in. This data set consists of measured results from the laboratory testing. The data from this project is believed to be useful for future design and numerical modelling of non-rectangular precast rocking walls.
description: The behavior of a T-shaped precast concrete rocking shear wall under lateral loading was investigated. The specimen was loaded in multiple direction using pseudo static cyclic loading protocol. The specimen showed minimized damage, excellent self-centering and adequate energy dissipation capacity similar to standard rectangular rocking walls. The data from this test can be used to develop numerical models and further the design and understanding on non-rectangular rocking precast concrete shear walls.
DOI: 10.17603/ds2-0rky-9w25
title: Undergraduate Research Experience (REU), NHERI 2019: Progressive Damage Assessment of On-Slab and Elevated Structures from Storm Surge and Waves Using LiDAR in a Laboratory Wave Basin

description: In the experiment conducted in the O.H. Hinsdale Wave Research Laboratory’s Directional Wave Basin, one on-slab and one elevated 1:6 scale light-frame wood specimens with identical superstructures were subjected to increasing water depths and wave heights until failure. Using terrestrial light detection and ranging (LiDAR) scans conducted during the experiment, point clouds showing progressive damage to the light-frame wood specimens were collected. Progressive damage rate and cumulative damage fraction as the houses were destroyed by storm surge and wave conditions were calculated using wall models fitted to the point clouds through Leica Cyclone’s RANSAC algorithm and manual processing.
DOI: 10.17603/ds2-503k-tj94
title: Impact of Axial Loading on Reinforced HPFRCC Beam-Column Elements for Understanding Seismic System-level BehaviorNatural Hazards Research Summit 2022:

description: This poster provides an overview of on-going work related to assessing the rotational capacity of beam-column reinforced HPFRCC elements with varying axial load ratios. The poster presents preliminary numerically simulated findings as part of a larger research program. The target audience is researchers and practitioners who are interested in ductile concrete systems.
DOI: 10.17603/ds2-bzv3-nc78
title: Laser-scanned roadway range image dataset (LRRD)
title: in Laser-scanned range image dataset from asphalt and concrete roadways for DCNN-based crack classification

description: This experiment aims at generating a laser-scanned range (i.e., elevation) image dataset for roadway crack classification. A vehicle-mounted laser imaging system called Laser Image and Measurement System was utilized for roadway image data collection. On 12/30/2019, the experiment was performed on asphalt and concrete roadways in Northport, Alabama. A dataset was obtained, which consists of 5000 crack image patches (dimension: 256-by-256 pixels) and 5000 non-crack patches cropped from the acquired roadway image frames. This range image dataset can be used for training and testing DCNN classifiers.
description: In recent years, deep convolutional neural network (DCNN) has been extensively explored and applied for civil engineering applications such as roadway crack detection. Despite a certain success, several challenges in DCNN-based roadway crack detection are yet to be fully addressed. For example, upon using three-dimensional (3D) range (i.e., elevation) images for crack detection, disturbances such as surface variation can negatively affect the detection performance. Thus, further discussions and experiments are needed to achieve a better understanding on the characteristics of image range data. In current literature, however, there is a lack of publicly available laser-scanned range image datasets for benchmark study on DCNN-based roadway crack detection. In this project, a field test was performed to acquire laser-scanned range images from both asphalt and concrete roadways. Crack and non-crack image patches were cropped from the image frames. As a result, a laser-scanned roadway range image dataset is constructed, consisting of 5000 crack and 5000 non-crack image patches. The acquired image dataset can be used to train and test DCNN classifiers for crack detection on asphalt and concrete surfaces.
DOI: 10.17603/ds2-dza2-tz58
title: Zhejiang University Centrifuge Experiments of the Seismic Interaction of a Liquefiable Soil with a Cantilever Retaining Wall
title: in LEAP-RPI-2020: Seismic interaction of a liquefiable soil with a cantilever retaining wall

description: Four centrifuge tests (ZJU1, ZJU2, ZJU3, ZJU4) were conducted at Zhejiang University (ZJU) to evaluate the seismic response of sheet-pile retaining structures in liquefiable soil for the 2020 Liquefaction Experiments and Analysis Project (LEAP-RPI-2020).
description: The Liquefaction Experiments and Analysis Projects (LEAP) is an international collaboration to develop a databank of high-quality centrifuge experimental data and to use this data in calibration and validation of numerical tools of soil liquefaction and associated consequences. LEAP-2020 investigated the performance and seismic interaction of a liquefiable (Ottawa sand) soil with a cantilever retaining wall. A total of twenty-three centrifuge model tests were performed at eight centrifuge facilities, as part of a round robin testing program. The experimental results of these tests are published and shared in this archive as eight separate experiments (one experiment per centrifuge facility). Each “experiment” includes a number of tests and each model test includes one or multiple destructive input excitations. The conducted tests cover a broad range of soil relative densities and input motion conditions. These tests provide valuable information on the trends and sensitivity of the soil-retaining wall response to variations in shaking intensity and soil relative density. The eight centrifuge facilities involved in LEAP-2020 program included Ehime University (Japan), KAIST (Korea), Kyoto University (Japan), National Central University (Taiwan), Rensselaer Polytechnic Institute (USA), University of California Davis (USA), University of Cambridge (UK), and Zhejiang University (China). The “Experimental Data Archive Overview” document provides an outline of the associated organization.
DOI: 10.17603/ds2-tjr2-ey50
title: Hurricane Ian Evacuation Decision Making Survey Instrument
title: in Hurricane Ian Evacuation Decision Making

description: Researchers developed a digital survey for residents impacted by Hurricane Ian in September 2022 designed to understand hurricane evacuation perceptions and hurricane preparedness. With the new use of meteorological messaging and maps used during the 2022 Hurricane season, this survey also explored the perceptions of storm surge predictions and the visualization of the hurricane track cone with their evacuation decision-making.
description: Researchers conducted a digital survey of residents impacted by Hurricane Ian in September 2022, with 1393 respondents as of January 8, 2022, to understand hurricane evacuation perceptions and hurricane preparedness. With the new use of meteorological messaging and maps used during the 2022 Hurricane season, this survey also explored the perceptions of storm surge predictions and the visualization of the hurricane track cone with their evacuation decision-making. This report summarizes the respondents’ preparedness to handle Hurricane Ian and their risk perceptions that led to their decision to sheltering-in-place, go to an emergency shelter, or evacuate elsewhere when threatened by Hurricane Ian. This survey was provided as an online survey through Qualtrics, and responses were gathered from October 2022 to March 2023.
DOI: 10.17603/ds2sq2k
title: TESTING OF SEVENTEEN IDENTICAL DUCTILE REINFORCED CONCRETE BEAMS WITH VARIOUS LOADING PROTOCOLS AND BOUNDARY CONDITIONS
title: in Testing of seventeen identical ductile reinforced concrete beams with various loading protocols and boundary conditions

description: This data set contains experimental data from simulated seismic loading tests on seventeen nominally identical ductile reinforced concrete beams, as described in an Earthquake Spectra Data Paper.