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1,055 results for “Bridge”
Swell Tor London Bridge Stones
Stones at Swelltor Quarry (west side) on Dartmoor that were cut for but never used on London Bridge. Photos taken in April 2018 by George Fletcher. Source: Objaverse 1.0 / Sketchfab
Moulton Packhorse Bridge, Suffolk, UK
Reprocessed old data from 2011 in RealityCapture to try and get my head around it. Created in RealityCapture by Capturing Reality from 108 images in 00h:16m:11s. Source: Objaverse 1.0 / Sketchfab
Pittsburgh Bridge Collapse in Frick Park
Based on a drone video by Kevin Keys here: https://www.youtube.com/watch?v=jqFoeGbo5JI The video was processed into frames using VLC then RealityCapture was used generate the textured model. Blender and InstaLOD were then used for model prep and optimization and upload. Newer version with more complete reconstruction here: https://skfb.ly/o8Xzt Source: Objaverse 1.0 / Sketchfab
Rick Roll Bridge
The arches under the railway bridge at the junction of Freston Road and Lockton Street, Notting Hill, London. This bridge was used as a backdrop for the official music video to Never Gonna Give You Up by Rick Astley. https://www.youtube.com/watch?v=dQw4w9WgXcQ The video was largely filmed at the Harrow Club nearby. 1698 photos taken with a Sony a7R III. 877 photos taken with a Sony a7 III (by Daniel Morgan). Processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Southwark Cathedral Bridge
A small bridge at the east end of Southwark Cathedral with steps beside it leading down into Borough Market and Green Dragon Court. 1238 photos taken in October 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Bridges Low poly Scene
Low poly blockout of a scene. made in blender only materias no textures or uvs Source: Objaverse 1.0 / Sketchfab
Drunkenness of Noah (Bridge of Sighs - Venice)
"... The medieval sculpture of the Drunkenness of Noah forms part of the south-eastern corner of the Palazzo Ducale and may be the work of Filippo Calendario (d. 1355), who was one of its architects. The placing of almost life-size figures (Adam and Eve are depicted on the south-western corner) on the corners of a building was without precedent, either in Venice or elsewhere... The sculpture cleverly uses the two sides of the corner to dramatise (with a little license) this aspect of the story. Noah is depicted drunk on one side, while two of his sons are depicted on the other. We see a hand slipping surreptitiously round the corner to cover up the father's nakedness." (Citation: https://www.picturesfromitaly.com/venice/palazzo-ducale-the-drunkenness-of-noah ) Photographed using a GoProHero4 in busy conditions. Source: Objaverse 1.0 / Sketchfab
Çorovodës Bridge - Nivica
A quick low quality model of Çorovodës Bridge in Nivica. Its date is a little unclear, but cerntaly during the Ottoman rule of Albania. Source: Objaverse 1.0 / Sketchfab
London Bridge Alcove North
One of two stone alcoves of one iteration of London Bridge. Now located in Victoria Park, London. Photos taken in February 2019 with a Sony a6000 and processed in Agisoft Metashape. Source: Objaverse 1.0 / Sketchfab
Cattle Trough Ray Street Bridge
A cattle drinking trough on Ray Street Bridge, London. Date: 1887 http://www.mdfcta.co.uk/details/t002.html 177 photos taken in May 2019 with a Sony a6000 and processed in Agisoft Metashape. Source: Objaverse 1.0 / Sketchfab
ALAMEDA Data: Bridging the Early Diagnosis and Treatment Gaps of Brain Diseases (Parkinson's Disease, Multiple Slerosis and Stroke)
<p><strong>ALAMEDA</strong> is an Horizon 2020 Research and Innovation project that aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions. Its vision is to research and prototype new generation Artificial Intelligence (AI) systems to support brain disorders patients' healthcare, focusing on Parkinson's Disease (PD), Multiple Sclerosis (MS) and Stroke.</p> <p>To this end, three (one for each disease) small scale validation pilots were performed in real world settings. Throughout these pilots, various types of data, such as accelerometer, gyroscopic, heart rate, etc., were collected via smart wearable sensors from the patients enrolled. The smart devices that were employed include: a Fitbit smartwatch, a GENEActiv smart bracelet, Novel Loadsol insole sensors and a prototype smart belt with triaxial accelerometers and gyroscopes embedded. Moreover, the patients underwent several clinical assessments and filled in numerous both disease-specific and non-disease-specific questionnaires.</p> <p>In this record, both raw and processed sensory data are combined with both clinical and patient reported outcomes (PROs) to form different disease-specific datasets. More specifically:</p> <ul> <li>For <strong>Parkinson's disease</strong>: Three datasets are provided (one for tremor detection, one for dyskinesia detection, and one for Hoehn & Yahr score estimation) alongside the vertical ground reaction force recordings.</li> <li>For <strong>Multiple Sclerosis</strong>: Two datasets are provided (one for Expanded Disability Status Scale (EDSS) scores classification and one that accumulates clinical data and individual scores from various MS-related questionnaires) alongside the vertical ground reaction force and the smart belt recordings.</li> <li>For <strong>Stroke</strong>: Two datasets are provided (one for rehabilitation exercises' recognition and one for walking classification, both with and without manual annotations) alongside the smart belt recordings.</li> </ul> <p>More information about the datasets provided can be found in the respective READ ME files that are included in the current record.</p>
Data from: A winged relative of ice crawlers in amber bridges the cryptic extant Xenonomia and a rich fossil record
<p>Until the advent of phylogenomics, the atypical morphology of extant representatives of the insect orders <em>Grylloblattodea</em> (ice crawlers) and <em>Mantophasmatodea </em>(gladiators) had confounding effects on efforts to resolve their placement within Polyneoptera. This recent research has unequivocally shown that these species‐poor groups are closely related and form the clade Xenonomia. Nonetheless, divergence dates of these groups remain poorly constrained, and their evolutionary history debated, as the few well‐identified fossils, characterized by a suite of morphological features similar to that of extant forms, are comparatively young. Notably, the extant forms of both groups are wingless, whereas most of the pre‐Cretaceous insect fossil record is composed of winged insects, which represents a major shortcoming of the taxonomy. Here, we present new specimens embedded in Early Cretaceous amber from Myanmar and belonging to the recently described species <em>Aristovia daniili</em>. The abundant material and pristine preservation allowed a detailed documentation of the morphology of the species, including critical head features. Combined with a morphological data set encompassing all Polyneoptera, these new data unequivocally demonstrate that <em>A. daniili</em> is a winged stem <em>Grylloblattodea</em>. This discovery demonstrates that winglessness was acquired independently in <em>Grylloblattodea </em>and <em>Mantophasmatodea</em>. Concurrently, wing apomorphic traits shared by the new fossil and earlier fossils demonstrate that a large subset of the former "Protorthoptera" assemblage, representing a third of all known insect species in some Permian localities, are genuine representatives of Xenonomia. Data from the fossil record depict a distinctive evolutionary trajectory, with the group being both highly diverse and abundant during the Permian but experiencing a severe decline from the Triassic onwards.</p>
Projeto BRIDGE - Vídeo de apresentação
<p>Vídeo de apresentação do projeto BRIDGE.</p>
Proglacial lake and river temperatures at Bridge Glacier, BC, Canada
<p>Data sets and scripts used in the analysis for the following article:</p> <p>Pelto B, Browning B, Bird L, Moyer A, Moore RD. 2024. Lake surface and downstream river temperature response to the retreat of a lake-terminating glacier. <em>Hydrological Processes</em>.</p>
Hysteresis Performance and Design Optimization of Rubber Concrete Anti-collision Layer of Concrete Bridge Piers
<p>Static and hysteresis experiments were implemented firstly to obtain the compressive strength, elastic modulus and energy dissipation factor of rubber concrete. This study explores the static and dynamic properties of rubber concrete.</p>
Data from: Numerical modelling of bridges in 2D shallow water flow simulations
<p>This repository includes the experimental dataset colleted in the Hydraulics Laboratory of the University of Zaragoza in 2014.</p> <p>The experiments were carried out with different bridge configurations in a straight flume for both steady and transient flow regimes.</p> <p>The data were published originally in:</p> <p>Ratia, H., Murillo, J. and García-Navarro, P. (2014), Numerical modelling of bridges in 2D shallow water flow simulations. Int. J. Numer. Meth. Fluids 75, pp. 250-272. <a href="https://doi.org/10.1002/fld.3892">https://doi.org/10.1002/fld.3892</a></p> <p>This dataset has been made available online to the research community thanks to the support of the project PID2022-137334NB-I00 funded by MCIN/AEI/10.13039/<br>501100011033 and by “ERDF/EU”.</p>
NuBe-DBBM: Numerical Benchmark for Drive-By Bridge Monitoring methods
<p>This repository contains an extensive dataset of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. In addition, the dataset includes results for different road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field of drive-by bridge monitoring.</p> <p>The dataset is made of a collection of individual files, each containing results and information about single vehicle crossing events. The dataset provides results for different dimensions of the problem, which are: monitoring scenario (DSA, DSB), bridge spans (B09, B015, B21, B27, B33, B39), damage location (DL25, DL50), damage magnitude (DM00, DM020, DM40), vehicle model (V1, V2, V5), road profile (P00, PA1, PA2), and event number (E0001, E0002, …, E0800). In total, the dataset contains 518 400 separate files conveniently categorized into a system of subfolders. Each file contains the simulated responses from a 2D representation of the vehicle-bridge interaction problem in Matlab environment. The files here are in <em>.mat</em> format. Refer to the document <em>ReadMe.pdf</em> for extended explanations about the filing structure and file contents. In addition, an extended description of the dataset and numerical modelling can be found in the associated journal publication listed below.</p> <p>Cantero D, Sarwar Z, Malekjafarian A, Corbally R, Makki Alamdari M, Cheema P, Aggarwal J, Noh HY, Liu J. Numerical benchmark for road bridge damage detection from passing vehicles responses applied to four data-driven methods. Archives of Civil and Mechanical Engineering, Vol. 24, Article number 190, 2024.</p> <p>DOI: <a href="https://doi.org/10.1007/s43452-024-01001-9">https://doi.org/10.1007/s43452-024-01001-9</a></p>
GeoDAR: Georeferenced global Dams And Reservoirs dataset for bridging attributes and geolocations
<p>Documented March 19, 2023</p> <p><strong>!!NEW!!!</strong></p> <p><strong>GeoDAR reservoirs were registered to the drainage network! </strong>Please see the auxiliary data "<a href="../records/7750736">GeoDAR-TopoCat</a>" at <a href="../records/7750736">https://zenodo.org/records/7750736</a>. "GeoDAR-TopoCat" contains the <strong>drainage topology</strong> (reaches and upstream/downstream relationships) and catchment boundary for each reservoir in GeoDAR, based on the algorithm used for Lake-TopoCat (doi:10.5194/essd-15-3483-2023).</p> <p> </p> <p>Documented April 1, 2022</p> <p><strong>Citation</strong></p> <p>Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869–1899, 2022, https://doi.org/10.5194/essd-14-1869-2022.</p> <p>Please cite the reference above (which was fully peer-reviewed), NOT the preprint version. Thank you.</p> <p> </p> <p><strong>Contact</strong></p> <p>Dr. Jida Wang, jidawang@ksu.edu, gdbruins@ucla.edu</p> <p> </p> <p><strong>Data description and components</strong></p> <p>Data folder “<strong>GeoDAR_v10_v11</strong>” (.zip) contains two consecutive, peer-reviewed versions (<strong>v1.0</strong> and <strong>v1.1</strong>) of the Georeferenced global Dams And Reservoirs (GeoDAR) dataset:</p> <ul> <li><strong>GeoDAR_v10_dams</strong> (in both shapefile format and the comma-separated values (csv) format): GeoDAR version 1.0, including 22,560 dam points georeferenced based on the World Register of Dams (WRD), the International Commission on Large Dams (ICOLD; <a href="https://www.icold-cigb.org">https://www.icold-cigb.org</a>; last access on March 13th, 2019).</li> <li><strong>GeoDAR_v11_dams</strong> (in both shapefile and csv): GeoDAR version 1.1 dam points, including 24,783 dams which harmonized GeoDAR_v10_dams and the Global Reservoir and Dam Database (GRanD) v1.3 (Lehner et al., 2011).</li> <li><strong>GeoDAR_v11_reservoirs</strong> (in shapefile): GeoDAR version 1.1 reservoirs, including 21,515 reservoir polygons retrieved by associating GeoDAR_v11_dams with GRanD v1.3 reservoirs, HydroLAKES v1.0 (Messager et al., 2016), and the UCLA Circa 2015 Lake Inventory (Sheng et al., 2016). The reservoir retrieval follows a one-to-one relationship between dams and reservoirs.</li> </ul> <p>As by-products of GeoDAR harmonization, folder “GeoDAR_v10_v11” also contains:</p> <ul> <li><strong>GRanD_v13_issues.csv</strong>: This file contains the original records of all 7,320 dam points in GRanD v1.3, with 94 of them marked by our identified issues and suggested corrections. These 94 records are placed at the beginning of this table. They include 89 records showing possible georeferencing and/or attribute errors, and another 5 records documented as subsumed or replaced. Our added fields start from column BG and include: <ul> <li>“Issue”: main issue(s) of this record</li> <li>“Description”: more detailed explanation of the issue</li> <li>“Lat_corrected”: suggested correction for latitude (if any) in decimal degree</li> <li>“Lon_corrected”: suggested correction for longitude (if any) in decimal degree</li> <li>“Correction_source”: correction source(s)</li> <li>“Harmonized”: whether this GRanD dam was harmonized in GeoDAR v1.1 and the reason.</li> </ul> </li> <li><strong>Wada_et_al_2017_harmonized.csv</strong>: This csv file contains the original records of all 139 georeferenced large dams/reservoirs in Wada et al. (2017; doi:10.1007/s10712-016-9399-6), with our revised storage capacities and spatial coordinates for data harmonization. Our added fields start from column E and include: <ul> <li>Revised_capacity_km3: Our revised reservoir storage capacity in cubic kilometers used for harmonization</li> <li>Revised_lat: Revised latitude in decimal degree</li> <li>Revised_lon: Revised longitude in decimal degree</li> <li>Verification_notes: Description of the issues, verification sources, and other information used for harmonization.</li> </ul> </li> </ul> <p> </p> <p><strong>Attribute description</strong></p> <table> <tbody> <tr> <td> <p><strong>Attribute</strong></p> </td> <td> <p><strong>Description and values</strong></p> </td> </tr> <tr> <td> <p>v1.0 dams (file name: GeoDAR_v10_dams; format: comma-separated values (csv) and point shapefile)</p> </td> </tr> <tr> <td> <p><em>id_v10</em></p> </td> <td> <p>Dam ID for GeoDAR version 1.0 (type: integer). Note this is not the same as the International Code in ICOLD WRD but is linked to the International Code via encryption.</p> </td> </tr> <tr> <td> <p><em>lat</em></p> </td> <td> <p>Latitude of the dam point in decimal degree (type: float) based on datum World Geodetic System (WGS) 1984.</p> </td> </tr> <tr> <td> <p><em>lon </em></p> </td> <td> <p>Longitude of the dam point in decimal degree (type: float) on WGS 1984.</p> </td> </tr> <tr> <td> <p><em>geo_mtd</em></p> </td> <td> <p>Georeferencing method (type: text). Unique values include “geo-matching CanVec”, “geo-matching LRD”, “geo-matching MARS”, “geo-matching NID”, “geo-matching ODC”, “geo-matching ODM”, “geo-matching RSB”, “geocoding (Google Maps)”, and “Wada et al. (2017)”. Refer to Table 2 in Wang et al. (2022) for abbreviations.</p> </td> </tr> <tr> <td> <p><em>qa_rank</em></p> </td> <td> <p>Quality assurance (QA) ranking (type: text). Unique values include “M1”, “M2”, “M3”, “C1”, “C2”, “C3”, “C4”, and “C5”. The QA ranking provides a general measure for our georeferencing quality. Refer to Supplementary Tables S1 and S3 in Wang et al. (2022) for more explanation.</p> </td> </tr> <tr> <td> <p><em>rv_mcm</em></p> </td> <td> <p>Reservoir storage capacity in million cubic meters (type: float). Values are only available for large dams in Wada et al. (2017). Capacity values of other WRD records are not released due to ICOLD’s proprietary restriction. Also see Table S4 in Wang et al. (2022).</p> </td> </tr> <tr> <td> <p><em>val_scn</em></p> </td> <td> <p>Validation result (type: text). Unique values include “correct”, “register”, “mismatch”, “misplacement”, and “Google Maps”. Refer to Table 4 in Wang et al. (2022) for explanation.</p> </td> </tr> <tr> <td> <p><em>val_src</em></p> </td> <td> <p>Primary validation source (type: text). Values include “CanVec”, “Google Maps”, “JDF”, “LRD”, “MARS”, “NID”, “NPCGIS”, “NRLD”, “ODC”, “ODM”, “RSB”, and “Wada et al. (2017)”. Refer to Table 2 in Wang et al. (2022) for abbreviations.</p> </td> </tr> <tr> <td> <p><em>qc</em></p> </td> <td> <p>Roles and name initials of co-authors/participants during data quality control (QC) and validation. Name initials are given to each assigned dam or region and are listed generally in chronological order for each role. Collation and harmonization of large dams in Wada et al. (2017) (see Table S4 in Wang et al. (2022)) were performed by JW, and this information is not repeated in the <em>qc</em> attribute for a reduced file size. Although we tried to track the name initials thoroughly, the lists may not be always exhaustive, and other undocumented adjustments and corrections were most likely performed by JW.</p> </td> </tr> <tr> <td> <p>v1.1 dams (file name: GeoDAR_v11_dams; format: comma-separated values (csv) and point shapefile)</p> </td> </tr> <tr> <td> <p><em>id_v11</em></p> </td> <td> <p>Dam ID for GeoDAR version 1.1 (type: integer). Note this is not the same as the International Code in ICOLD WRD but is linked to the International Code via encryption.</p> </td> </tr> <tr> <td> <p><em>id_v10</em></p> </td> <td> <p>v1.0 ID of this dam/reservoir (as in <em>id_v10</em>) if it is also included in v1.0 (type: integer).</p> </td> </tr> <tr> <td> <p><em>id_grd_v13</em></p> </td> <td> <p>GRanD ID of this dam if also included in GRanD v1.3 (type: integer).</p> </td> </tr> <tr> <td> <p><em>lat</em></p> </td> <td> <p>Latitude of the dam point in decimal degree (type: float) on WGS 1984. Value may be different from that in v1.0.</p> </td> </tr> <tr> <td> <p><em>lon </em></p> </td> <td> <p>Longitude of the dam point in decimal degree (type: float) on WGS 1984. Value may be different from that in v1.0.</p> </td> </tr> <tr> <td> <p><em>geo_mtd</em></p> </td> <td> <p>Same as the value of <em>geo_mtd</em> in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>qa_rank</em></p> </td> <td> <p>Same as the value of <em>qa_rank</em> in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>val_scn</em></p> </td> <td> <p>Same as the value of <em>val_scn</em> in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>val_src</em></p> </td> <td> <p>Same as the value of <em>val_src</em> in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>rv_mcm_v10</em></p> </td> <td> <p>Same as the value of <em>rv_mcm </em>in v1.0 if this dam is included in v1.0.</p> </td> </tr> <tr> <td> <p><em>rv_mcm_v11</em></p> </td> <td> <p>Reservoir storage capacity in million cubic meters (type: float). Due to ICOLD’s proprietary restriction, provided values are limited to dams in Wada et al. (2017) and GRanD v1.3. If a dam is in both Wada et al. (2017) and GRanD v1.3, the value from the latter (if valid) takes precedence.</p> </td> </tr> <tr> <td> <p><em>har_src</em></p> </td> <td> <p>Source(s) to harmonize the dam points. Unique values include “GeoDAR v1.0 alone”, “GRanD v1.3 and GeoDAR 1.0”, “GRanD v1.3 and other ICOLD”, and “GRanD v1.3 alone”. Refer to Table 1 in Wang et al. (2022) for more details.</p> </td> </tr> <tr> <td> <p><em>pnt_src</em></p> </td> <td> <p>Source(s) of the dam point spatial coordinates. Unique values include “GeoDAR v1.0”, “original GRanD”, “adjusted GRanD” (meaning the original dam point location in GRanD has been adjusted to improve the accuracy), and “corrected GRanD” (meaning the original point in GRanD was misplaced and has been corrected). Also see Table S5 in Wang et al. (2022).</p> </td> </tr> <tr> <td> <p><em>qc</em></p> </td> <td> <p>Roles and name initials of co-authors/participants during data QC, validation, and other manual operations. Name initials are given to each assigned dam or region and are listed generally in chronological order for each role. Correction of GRanD (see Table S5 in Wang et al. (2022)) and reservoir polygon QC were performed by JW, and this information is not repeated in the <em>qc</em> attribute to reduce the file size. Although we tried to track the name initials thoroughly, the lists may not be always exhaustive, and other undocumented adjustments and corrections were most likely performed by JW.</p> </td> </tr> <tr> <td> <p>v1.1 reservoirs (file name: GeoDAR_v11_reservoirs; format: polygon shapefile)</p> </td> </tr> <tr> <td> <p><em>plg_src</em></p> </td> <td> <p>Source of the retrieved reservoir polygon (type: text). Unique values include “GRanD v1.3”, “HydroLAKES v1.0”, and “UCLA Circa 2015”. Refer to Table 1 in Wang et al. (2022) for more details.</p> </td> </tr> <tr> <td> <p><em>plg_a_km2</em></p> </td> <td> <p>Area of the retrieved reservoir polygon in square kilometres (calculated based on the cylindrical equal area projection on datum WGS 1984).</p> </td> </tr> <tr> <td> <p><em>All other attributes in v1.1 dams.</em></p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data and code availability</strong></p> <p>GeoDAR v1.0 (dam points) and v1.1 (both dam points and reservoir polygons) are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (<a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a>). </p> <p>Any user who would like to link GeoDAR features to the proprietary WRD attributes the user has purchased in advance from ICOLD should contact the corresponding author JW.</p> <p>Python scripts for geo-matching, geocoding, and reservoir assignment are available at <a href="https://github.com/surf-hydro/georeferencing-ICOLD-dams-and-reservoirs">https://github.com/surf-hydro/georeferencing-ICOLD-dams-and-reservoirs</a>. We request users who adapt or use the scripts to cite Wang et al. (2022).</p> <p>We also request users to cite Wang et al. (2022) if they use our identified issues or suggested corrections for GRanD v1.3 (as provided in “GRanD_v13_issues.csv”).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>GeoDAR v1.0 and v1.1 contain knowledge derived from ICOLD WRD (<a href="https://www.icold-cigb.org/GB/world_register/acknowledgements_wrd.asp">https://www.icold-cigb.org/GB/world_register/acknowledgements_wrd.asp</a>) but release no original values of the proprietary WRD attributes (except the storage capacities of a few large dams used to verify/correct Wada et al. (2017); see Table S4 in Wang et al. (2022)). The production and dissemination of GeoDAR abide by ICOLD’s legal policies (<a href="https://www.icold-cigb.org/GB/legal.asp">https://www.icold-cigb.org/GB/legal.asp</a>) and were approved by ICOLD’s Central Office.</p> <p>GeoDAR v1.0 represents an initial effort of georeferencing WRD at the global scale. The resultant dam distribution may be skewed towards regions where georeferencing sources are more abundant, and therefore, may not accurately reflect the distribution of all WRD records. The authors are not responsible for any consequence arising from this limitation.</p> <p>GeoDAR v1.1 absorbed most of the spatial features (i.e., dam point coordinates and reservoir polygons) in GRanD v1.3. To acknowledge the originality of GRanD, we request users to cite Lehner et al. (2011) if they only use the subset of GeoDAR v1.1 from GRanD alone. If the user adopts the spatial coordinates we corrected for GRanD (see “GRanD_v13_issues.csv”), we recommend users citing Wang et al. (2022) as well.</p> <p>The source of each spatial feature in GeoDAR v1.1 is specified in the attributes “har_src” and “pnt_src” for dam points and the attribute “plg_src” for reservoir polygons. For any questions about data citation, please contact the corresponding author JW.</p> <p>Authors of this paper claim no responsibility or liability for any consequences related to the use, citation, or dissemination of GeoDAR.</p> <p> </p> <p><strong>Other notes</strong></p> <p>We provide another auxiliary folder “<strong>GeoDAR_beta_peer_review</strong>” (.zip), which stores the versions of GeoDAR before the completion of peer review with ESSD. We here keep these earlier GeoDAR versions on file, but since improvements and corrections were made during the peer review process, we do NOT recommend any application of these earlier versions. Instead, please use the fully peer-reviewed versions in folder “<strong>GeoDAR_v10_v11</strong>”. </p> <p>Please also see the readme files in each of the folders. </p>
Study On Hybrid Model Combining Super Learner And Physics-based Models For SHM In Bridges Using Low-cost BWIM
<p>The main objective of this project is to study and develop hybrid models of BWIM physics-based models incorporates with SHM inspection used by artificial intelligence (AI) techniques to predict structural damage. This study presents a comprehensive assessment of FE simulations leveraging contact method verified by vehicle-bridge interaction(VBI) theory and uses machine learning (ML) techniques to identify and predict structural damages from the structural response automatically. Bridges are a fundamental part of infrastructure management. The main challenge that we face is the aging of these transportation infrastructures without a tool to perform accurate structural assessments in a real-time manner. Unfortunately, this topic is still not completely developed due to the lack of study upon BWIM simulations designed with several different severity damages. The Contact method, a new approach to simulating moving-vehicle motion in BWIM simulation, is to carry out actual structural response verified by the VBI with comprehensive parameter studies. In order to simulation the reality complex condition of the bridge, the FE model is designed by four different classes of damages with three different damage locations applied with two different load conditions (e.g., static load and moving load). The responses collected from FE simulation are used for structural damage prediction leveraging the ML damage prediction model. Among ML methods, the XGBoost with assembly decision tree shows the most reliable results. The results in this project indicate that structural damage prediction can be achieved by using the ML technique and BWIM structural response, which provides high accuracy of damage prediction.</p>
Bridge Cracks Monitoring: Detection, Measurement, and Comparison using Augmented Reality
<p>Crack occurrence and propagation are among critical factors that affect the performance and lifespan of civil infrastructures such as bridges. Consequently, numerous crack detection and measurement methods have been proposed and developed in the recent decades in the areas of Structural Health Monitoring and non-destructive testing. Many novel technologies have emerged with the potential to overcome the limitations of the presented techniques of crack detection and characterization. Crack detection and characterization method used in this research lies in supplementing human visual inspection capabilities in a systematic manner through an appropriate level of automation. The Augmented Reality (AR) tool developed in this project allows a user to perform tasks in a real-world environment while visually receiving supplementary 3D computer-generated information to support the tasks. More specifically, we developed a crack detection/characterization tool in this research and deployed it in Microsoft HoloLens smart glasses. This AR tool provides the user with automatic data collection capability through AR headset camera and is a means of hands-free data sharing for inspectors while conducting their normal inspection. We conducted several laboratory and field experiments by which we evaluated the effectiveness of the developed crack detection and measurement system. The result confirm that the AR tool devised in this project has the potential to help the inspection process in terms of time, comfort and accuracy.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.