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1,055 results for “Bridge”
Chapel of Our Lady on the Bridge
Chapel of Our Lady on the Bridge, Rotherham (South Yorkshire, UK). The chapel and bridge over the River Don were built in 1483. Bridge chapels were common in Medieval times, and were used by travellers to pray for a safe journey. This is the best preserved of only four which survive in England, and the only one that still has a service of Holy Communion (every Tuesday at 11am). https://historicengland.org.uk/listing/the-list/list-entry/1191884 The model was created from 695 photographs. Source: Objaverse 1.0 / Sketchfab
Bow Creek Iron Bridge Abutment
The remaining abutment of a bridge over the Bow Creek / River Lea on the east bank, just south of East India Dock Road bridge. Date: 1896. There is no sign of the abutment on the opposite side of the river. https://footprintsoflondon.com/2014/03/the-ruins-of-london-industrial-ruins/ 547 photos taken in May 2021 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Roman Bridge Piercebridge
Roman Bridge, Deer Street, Piercebridge. These are remains of a bridge which once led to Piercebridge Roman Fort. The bridge consisted of at least 5 piers but the northern end has not been discovered despite an intensive search. As the river course moved northwards the southern end became supported by debris left by river floods and the piers were robbed and replaced by a causeway in the early 2nd century AD, dating evidence for which is a coin of Hadrian and early 2nd century pottery. This site is now in the care of English Heritage (2011). Source: Objaverse 1.0 / Sketchfab
St Magnus the Martyr London Bridge Model
A scale model of one of the versions of London Bridge (from around 1400) on display in the church of St Magnus the Martyr on Lower Thames Street, London. Date: 1987 This is a quick scan with the photos taken through the glass display cabinet. Hence the model is a little noisy, particularly at the ends. https://en.wikipedia.org/wiki/St_Magnus-the-Martyr Ian Visits blog post: https://www.ianvisits.co.uk/blog/2012/09/30/a-scale-model-of-old-london-bridge/ 320 photos taken (through glass) in July 2019 with a Sony a6000 and processed in Agisoft Metashape. Source: Objaverse 1.0 / Sketchfab
Parrot Anafi Bridge Test
This was simply a test to see if the Anafi could map a structure like a bridge. It's not a very professional model and there is a fair amount of noise as well as lack of top detail. Anyway, testing continues and not a bad drone for this type of inspection work. Source: Objaverse 1.0 / Sketchfab
North Bridge remains
These timber posts in the riverbed represent a catalogue of ill-fated bridge building. The Whitby-Pickering railway was carried over the Murk Esk river here from 1845. Nobody knows for sure what the original bridge looked like but it was washed away in floods and replaced by a timber structure, which was again damaged in the 1930s (see photo below). Now all that remains are the timber posts from this replacement bridge, two of which are displayed alongside the track (see photo below). The old railway now forms the [Rail Trail](https://www.northyorkmoors.org.uk/looking-after/landofiron/explore) footpath and a modern timber footbridge carries walkers over the river. Picture from 1930s of further damage to timber bridge. c. Whitby Museum/C M Doncaster  Bridge support posts, with steel tips for driving into the river  Source: Objaverse 1.0 / Sketchfab
Data Archive from the MX3D Bridge in Amsterdam
<h1><strong>Data Archive from the MX3D Bridge in Amsterdam</strong></h1> <p>This DOI refers to the data collected from the <a href="https://www.thefabricator.com/thefabricator/article/additive/testing-the-worlds-first-3d-printed-metal-bridge">3d-printed bridge from MX3D in Amsterdam</a> from ~June 2021 – 6th July 2023. Contained within this DOI is a set of metadata that outlines the specifics of the sensors that were used to collect the data.</p> <h2><strong>Breakdown of (zipped) directory structure</strong></h2> <ol> <li> <p><strong>"Calibration_Info" Folder</strong>: This directory includes the manufacturer's sensor datasheets and houses two subdirectories:</p> <ul> <li>"CXTA01-T" and "CXL04GP3-R-AL", each containing calibration factor files specific to inclinometers and accelerometers, respectively.</li> </ul> </li> <li> <p><strong>"26102020 MX3D Bridge Sensor System" Spreadsheet (.xlsx)</strong>: This spreadsheet enumerates the sensors, cataloging critical details such as measurement direction, location, and associated data modules.</p> </li> <li> <p><strong>"Sensor Layout" PDF (.pdf)</strong>: A document depicting the layout of sensors on the MX3D bridge, with "North" and "South" annotations pertaining to the bridge's orientation in Amsterdam.</p> </li> <li> <p><strong>"Sensor Layout with Surroundings" PDF (.pdf)</strong>: An enhanced version of the Sensor Layout document, this PDF includes additional annotations regarding the surrounding bars to provide context to the sensor locations.</p> </li> </ol>
Bridging the conservation and development trade-off?: A working landscape critique of a conservancy in the Maasai Mara
<p>The recent call to halt biodiversity loss by protecting half the planet has been hotly contested because of the extent to which people might be excluded from these landscapes. It is clear that incorporating landscapes that implicitly work for indigenous people is vital to achieving any sustainable targets.We examine an attempt to balance the trade-offs between conservation and development in Enonkishu Conservancy in the Maasai Mara, using a working landscape approach. Mobile livestock production strategies are theoretically consistent with wildlife-based activities and can present a win-win solution for both conservation and development. We explore the success and failings of Enonkishu's evolving attempts to achieve this: addressing the criticism of the conservation sector that it fails to learn from its mistakes. We found that Enonkishu has had considerable positive conservation outcomes, preventing the continued encroachment of farmland, maintaining and improving rangeland health relative to the surrounding area, while maintaining diverse and large populations of wildlife and livestock. The learning from certain ventures, that failed, particularly on livestock, has created institutions and governance that, while still evolving, are more robust and relevant for conservancy members, by being fluid and inclusive. Diverse revenue streams (beyond tourism, including a residential estate, livestock venture and philanthropy) enabled Enonkishu to withstand the pressures of Covid-19. Livestock is crucial for defining the vision of the conservancy, and the institutions and governance that underpin it.</p>
Wind Value: First Conference 2022, Blade Bridge Concept to Reality, Kieran Ruane, Video
<p>Video of 33 mins 1 second on The Blade Bridge - From Concept to Reality, by Kieran Ruane. Describing the design of the world's second bridge made from used wind turbine blades, installed on the Youghal Midleton Greenway, Co Cork, Ireland</p>
Wind Value: First Conference 2022, LCSA of a Pedestrian Bridge made from Wind Blades, Angie Nagle (Paul Leahy presented), Video
<p>Video of 12 mins 46 seconds, on Life Cycle Sustainability Assessment (LCSA) of a Pedestrian Bridge made from Discarded Wind Blades, written by Angie Nagle and presented by Paul Leahy, her supervisor.</p>
IN02020 Sivalinga base near the Aryaghat bridge (translation)
<p>IN02020 Sivalinga base near the Aryaghat bridge (translation)</p>
Detection of Structural Components in Point Clouds of Existing RC Bridges
<p><em>The cost and effort </em><em>of</em><em> modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule-based classification to create labelled cuboids and cylinders from point clouds. While these methods work well in synthetic datasets or idealized cases, they encounter huge challenges when dealing with real-world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this paper, we propose a novel top-down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges over-segments into individually labelled point clusters. The results of 10 real-world bridge point cloud experiments indicate that our method achieves an average detection precision of 98.8%. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labelled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labelled point clusters.</em></p>
BRIDGES singletons annotated with local genomic features
<p>This dataset consists of a tarball archive containing 8,192 tab-delimited files (one per 7-mer sequence motif). Each file contains information about the status or value of 15 different genomic features at every possible site in hg19, centered at the 7-mer sequence motif indicated in the filename (or the reverse complement of that motif; e.g. <code>ACGATGC_annotated.txt</code> includes information for sites at 5’-ACG<strong>A</strong>TGC-3’ <em>and</em> sites at 5’-GCA<strong>T</strong>CGT-3’ motifs).</p> <p>Each file contains the following columns:</p> <ul> <li> <p><strong>AT_CG</strong> [indicator if site carries an A>C or T>G singleton (1) or not (0) in the BRIDGES data]</p> </li> <li> <p><strong>AT_GC</strong> [indicator if site carries an A>G or T>C singleton (1) or not (0) in the BRIDGES data]</p> </li> <li> <p><strong>AT_TA</strong> [indicator if site carries an A>T or T>A singleton (1) or not (0) in the BRIDGES data]</p> </li> <li> <p><strong>GC_AT</strong> [indicator if site carries a G>A or C>T singleton (1) or not (0) in the BRIDGES data]</p> </li> <li> <p><strong>GC_CG</strong> [indicator if site carries a G>C or C>G singleton (1) or not (0) in the BRIDGES data]</p> </li> <li> <p><strong>GC_TA</strong> [indicator if site carries a G>T or C>A singleton (1) or not (0) in the BRIDGES data]</p> </li> <li> <p><strong>DP</strong> [average depth of coverage at site]</p> </li> <li> <p><strong>H3K4me1</strong> [indicator if site is within a H3K4me1 broad peak (1) or not (0)]</p> </li> <li> <p><strong>H3K4me3</strong> [indicator if site is within a H3K4me3 broad peak (1) or not (0)]</p> </li> <li> <p><strong>H3K9ac</strong> [indicator if site is within a H3K9ac broad peak (1) or not (0)]</p> </li> <li> <p><strong>H3K9me3</strong> [indicator if site is within a H3K9me3 broad peak (1) or not (0)]</p> </li> <li> <p><strong>H3K27ac</strong> [indicator if site is within a H3K27ac broad peak (1) or not (0)]</p> </li> <li> <p><strong>H3K27me3</strong> [indicator if site is within a H3K27me3 broad peak (1) or not (0)]</p> </li> <li> <p><strong>H3K36me3</strong> [indicator if site is within a H3K36me3 broad peak (1) or not (0)]</p> </li> <li> <p><strong>EXON</strong> [indicator if site is within an exon (1) or not (0)]</p> </li> <li> <p><strong>CpGI</strong> [indicator if site is within a CpG island (1) or not (0)]</p> </li> <li> <p><strong>RR</strong> [average recombination rate in the 10kb window centered at the site]</p> </li> <li> <p><strong>LAMIN</strong> [indicator if site is within an Lamin-Associated Domain (1) or not (0)]</p> </li> <li> <p><strong>DHS</strong> [indicator if site is within a DNase Hypersensitive region (1) or not (0)]</p> </li> <li> <p><strong>TIME</strong> [average recombination rate in the 10kb window centered at the site]</p> </li> <li> <p><strong>GC</strong> [average GC content in the 10kb window centered at the site]</p> </li> </ul> <p>Note that the chromosome and position of each site has been removed to protect sample privacy.</p> <p>Each file is then passed to an R script (available at <a href="https://github.com/carjed/smaug-genetics">https://github.com/carjed/smaug-genetics</a>) to estimate the effects each feature on the relative mutation rate using a logistic regression model (e.g., <code>AT_GC ~ DP + ... + GC</code>). Each of the features used is available from data in the public domain; the provenance of these features is described in the associated paper, and additional scripts for processing the feature data can be found at at <a href="https://github.com/carjed/smaug-genetics">https://github.com/carjed/smaug-genetics</a>.</p> <p> </p> <p>The BRIDGES whole-genome sequencing study is described at <a href="https://doi.org/10.1101/108290">https://doi.org/10.1101/108290</a></p>
Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics
<p>Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics</p>
CODEBRIM: COncrete DEfect BRidge IMage Dataset
<p><strong>CODEBRIM: COncrete DEfect BRidge IMage Dataset</strong> for multi-target multi-class concrete defect classification in computer vision and machine learning.</p> <p>Dataset as presented and detailed in our CVPR 2019 publication: <a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Mundt_Meta-Learning_Convolutional_Neural_Architectures_for_Multi-Target_Concrete_Defect_Classification_With_CVPR_2019_paper.html">http://openaccess.thecvf.com/content_CVPR_2019/html/Mundt_Meta-Learning_Convolutional_Neural_Architectures_for_Multi-Target_Concrete_Defect_Classification_With_CVPR_2019_paper.html</a> or <a href="https://arxiv.org/abs/1904.08486">https://arxiv.org/abs/1904.08486</a> . If you make use of the dataset <strong>please cite it as follows</strong>:</p> <p><strong>"Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh. <em>Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset</em>. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019"</strong></p> <p>We offer a supplementary GitHub repository with code to reproduce the paper and data loaders: <a href="https://github.com/ccc-frankfurt/meta-learning-CODEBRIM">https://github.com/ccc-frankfurt/meta-learning-CODEBRIM</a></p> <p>For ease of use we provide the dataset in multiple different versions.</p> <p>Files contained:<br> * CODEBRIM_original_images: contains the original full-resolution images and bounding box annotations<br> * CODEBRIM_cropped_dataset: contains the extracted crops/patches with corresponding class labels from the bounding boxes <br> * CODEBRIM_classification_dataset: contains the cropped patches with corresponding class labels split into training, validation and test sets for machine learning<br> * CODEBRIM_classification_balanced_dataset: similar to "CODEBRIM_classification_dataset" but with the exact replication of training images to balance the dataset in order to reproduce results obtained in the paper. </p>
Development, Training, Education, and Implementation of Low-Cost Sensing Technologies for Bridge Structural Health Monitoring (SHM)
<p>Corresponding data set for Tran-SET Project No. 17STUNM02. Abstract of the final report is stated below for reference:</p> <p>"Transportation infrastructure needs continuous monitoring. However, traditional inspections cost money and are conducted visually. New technologies for bridge monitoring are expensive and complex. This project involved developing cost-effective sensor technologies that can be applied towards the maintenance of railroad bridges by recording reference-free transverse displacement. More specifically, this project developed new applications of new technologies (Arduino, wireless smart sensors, drones, Hololens) and promoted workforce development with an emphasis on outreach of high-school students. This project was carried out in three main phases: (1) development and validation of technologies, (2) education and outreach to students, and (3) outreach to industry consisting in one professional workshop. The findings from the first phase showed that the data gathered by these new low-cost sensing systems were comparable to the data collected using traditional sensors. Researchers collected the findings of the second phase of the project through surveys conducted from Middle school, High school and college students during and after outreach activities. these surveys showed that many of the participant students got more interested in the use of new technologies after getting familiar with them. Finally, researchers collected the findings of the third phase of the project through a workshop collecting the interest and challenges of the owners of railroad infrastructure. The top interest of railroad owners is to explore the use of new technologies to increase safety in the field. The conclusions of this research include prioritization on developing low-cost technologies that can measure simple parameters in the field of interest to existing inspectors."</p>
Bridge Deck Overlays Using Ultra-High Performance Concrete
<p>Corresponding data set for Tran-SET Project No. 17CNMS01. Abstract of the final report is stated below for reference:</p> <p>"This study investigated the use of a locally produced ultra-high performance concrete (UHPC) as an alternative to typical overlay materials. Several bond strength tests including slant-shear, splitting tension, and direct tension tests were performed to assess the bond strength between UHPC and normal strength concrete (NSC) substrate with varying surface textures. Tests were also conducted to assess the early-age and longer-term shrinkage behavior and coefficient of thermal expansion of the UHPC as well as rapid chloride permeability testing. Good bond between UHPC and NSC substrate was observed even with inadequate surface texture. Combined shrinkage and thermal effects were investigated for NSC slabs overlaid with the non-proprietary UHPC by analyzing five slab-overlay sections. Each slab-overlay had a single parameter varied to isolate the effects of thickness of the NSC substrate, substrate reinforcement ratio, and exposure conditions. Increased steel reinforcement and thickness of the NSC substrate were observed to reduce the effect of UHPC overlay shrinkage. The final major experiment was to overlay a full-scale channel girder to assess the response of a high-performance concrete, pre-stressed bridge girder with a 1-in. (25-mm) UHPC overlay to flexural loading. The girder was subjected to 1000 load-unload cycles to specified service load conditions. Cyclic loading was applied both before and after application of the UHPC overlay to provide a comparison of global behavior and performance of the girder and overlay. Finally, the girder with overlay was loaded to failure to investigate post-cracking and ultimate behavior of the composite member. Little to no visible distress was observed in the overlay until loads were applied that were significantly greater than expected under normal service conditions. The results indicated that the non-proprietary UHPC has the potential to serve as an overlay material as long as proper measures are used to prepare the substrate surface and ensure a high quality bond with the existing deck."</p>
Sustainability-Based Long-Term Management of Bridges under Multi-Hazard Exposure
<p>Corresponding data set for Tran-SET Project No. 17STOKS01. Abstract of the final report is stated below for reference:</p> <p>"Bridges are under deterioration due to various mechanical and environmental stressors. Hydraulic-related hazards (e.g., flood and scour), aggressive environmental conditions, and seismic events (e.g., earthquake) are recognized as the most significant threats to the safety of bridges. In traditional risk assessment methods for structures susceptible to damage due to floods and other natural hazards (e.g., corrosion and seismic events), future hazard predictions are conducted using historic return periods and climate records. However, recent increase in flood intensity in central-southern states indicate that future hazard occurrence rate may not necessarily follow past trends. Accordingly, current design, assessment, and management methodologies should adapt to these changes in order to ensure the satisfactory performance of bridges under the combined or cumulative action of hazards. This project addresses this need by presenting a framework for risk quantification and optimum management of bridges susceptible to damage due to floods, flood induced scour, and other gradual deterioration mechanism (e.g., corrosion and fatigue). Downscaled climate data, adopted from the global climate models, are employed to predict future flood hazard at a given location. Probabilistic simulation is used to quantify the time-dependent failure probability, which subsequently helps quantify the long-term sustainability through the systematic integration of economic, social, and environmental metrics associated with bridge failures. These profiles can be next used to obtain optimum interventions required to extend the service life while maintaining the structural performance above prescribed thresholds."</p>
Coastal Bridges under Hurricane Stresses along the Texas and Louisiana Coast
<p>Corresponding data set for Tran-SET Project No. 17STTSA02. Abstract of the final report is stated below for reference:</p> <p>"The main objective of this study was to develop a high-resolution model capable of simulating the response of bridge structures to hydrodynamic loads for hurricane design conditions (i.e. surge height, wave height, and frequency) expected in the Texas-Louisiana coast. The model relied on Coupled Eulerian-Lagrangian techniques (CEL) where solids are simulated with Lagrangian meshes, while fluids are simulated using Eulerian meshes, and was calibrated using historical data from wave impact laboratory tests. Two high resolution models were created, the first of a tsunami wave impact test conducted at Oregon State University and the second of a bridge located in the Gulf Coast that was heavily impacted by hurricane Katrina. The tsunami wave impact model showed that the CEL technique could provide accurate estimates of wave elevation, water velocity, and wall reaction recorded during the tests at Oregon State University. The bridge model was used to calculate bridge support demands for a representative combination of storm surge, wave velocity and wave length."</p>
Bridge Inspecting with Unmanned Aerial Vehicles R&D
<p>Corresponding data set for Tran-SET Project No. 17STLSU11. Abstract of the final report is stated below for reference:</p> <p>"The project achieves through research including literature, on site interviews, and experimentation: 1) a recommendation for a UAV-based system to practically assist in routine bridge inspection work in the State of Louisiana, 2) the identification and description of advantages, disadvantages, and limitations in the use of UAVs for routing bridge inspection work in Louisiana, and 3) provided recommendations for future work. The Yuneec H520 aircraft and its E90 camera are recommended, as is the need for a boat to be included as part of the system. The recommended system has advantages in reaching portions of the bridge that are difficult to reach by human inspectors and includes sufficient image resolution to assist the bridge inspection process. A disadvantage though, is that of the overburden of regulations both from the FAA and for getting permission to inspect a bridge using a UAV. These regulations my render negligible, any gains in efficiency perceived in the use of UAVs for bridge inspection. Also, the UAV is described by the project as an assistance tool for the manual bridge inspection process and cannot replace the needed work of bridge inspectors, as it has limitations. For example, the UAV cannot perform inspections beneath the bridge deck since it may lose its GPS navigation reference. Likewise, it cannot see beneath the surface to tell of concrete components have subsurface cracks or timbers might be hollow. These tests are still the domain of manual bridge inspection. The project provided recommendations with respect to changes in how inspections should be done using the UAV, i.e. in the pre-inspection phase, needed field studies using the UAV, needed economics alternative-tradeoffs studies, and recommendations for augmenting the aircraft and its instruments. The Second phase, i.e. the Implementation Phase, will utilize the information and educational fruits of the technical research phase for tutorials, seminars and to facilitate feedback surveys with engineering firms, the LADOTD, engineering societies, and students."</p>
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