FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2999695716> ?p ?o ?g. }

• W2999695716 endingPage "571" @default.
• W2999695716 startingPage "547" @default.
• W2999695716 abstract "Abstract Bridges are critical components of highways ensuring traffic can efficiently travel over obstructions such as bodies of water, valleys, and other roads. Ensuring bridges are in sound structural condition is essential for safe and efficient highway operations. Structural health monitoring (SHM) systems designed to measure bridge responses have been developed to quantitatively track the health of bridges. More recently, SHM systems have also begun to integrate measurement of vehicular loads that create the responses measured. However, precise correlation of traffic loads to bridge responses remains a costly and technically difficult strategy. To address existing technical limitations, a cyber-physical system (CPS) framework is proposed to track truck loads in a highway corridor, to trigger SHM systems to record bridge responses, and to automate the linking of bridge response data with truck weights collected by weigh-in-motion (WIM) stations installed along the corridor but not collocated with the bridges. To link truck weights to bridge responses, computer vision methods based on convolutional neural networks (CNN) are used to automate the detection and reidentification of trucks using traffic cameras. The single-stage CNN object detector YOLO is trained using a customized dataset to identify trucks from camera images at each instrumentation site; high precision is obtained with the YOLO detector exceeding 95% average precision (AP) for an intersection over union (IOU) threshold of 0.75. To reidentify the same truck at different locations in the corridor, this study adopts a CNN-based encoder trained via a triplet network and a mutual nearest neighbor strategy using feature vectors extracted from images at each measurement location. The proposed reidentification method is implemented in the CPS cloud environment and obtains a F1-score of 0.97. The study also explores the triggering of bridge monitoring systems based on visual detection of trucks by a traffic camera installed upstream to the bridges. The triggering strategy proves to be highly efficient with 99% of the triggered data collection cycles capturing truck events at each bridge. To validate, the CPS architecture is implemented on a 20-mile highway corridor that has a WIM station already installed; four traffic cameras and two bridge SHM systems are installed along the corridor and integrated with a CPS architecture hosted on the cloud. In total, over 10,000 trucks are observed at all measurement locations over one year allowing peak bridge responses to be correlated to both measured truck weights and to one another." @default.
• W2999695716 created "2020-01-23" @default.
• W2999695716 creator A5008053913 @default.
• W2999695716 creator A5036167561 @default.
• W2999695716 creator A5072823108 @default.
• W2999695716 creator A5081466855 @default.
• W2999695716 date "2020-02-01" @default.
• W2999695716 modified "2023-10-17" @default.
• W2999695716 title "Cyber-physical system architecture for automating the mapping of truck loads to bridge behavior using computer vision in connected highway corridors" @default.
• W2999695716 cites W1844128389 @default.
• W2999695716 cites W1955055330 @default.
• W2999695716 cites W1976239060 @default.
• W2999695716 cites W1993161892 @default.
• W2999695716 cites W1993267579 @default.
• W2999695716 cites W2006708108 @default.
• W2999695716 cites W2013015707 @default.
• W2999695716 cites W2018888714 @default.
• W2999695716 cites W2025528231 @default.
• W2999695716 cites W2027885542 @default.
• W2999695716 cites W2031489346 @default.
• W2999695716 cites W2064510059 @default.
• W2999695716 cites W2087377053 @default.
• W2999695716 cites W2097616340 @default.
• W2999695716 cites W2100076348 @default.
• W2999695716 cites W2104771463 @default.
• W2999695716 cites W2117539524 @default.
• W2999695716 cites W2135846084 @default.
• W2999695716 cites W2138626366 @default.
• W2999695716 cites W2145023731 @default.
• W2999695716 cites W2149713648 @default.
• W2999695716 cites W2151956385 @default.
• W2999695716 cites W2168202080 @default.
• W2999695716 cites W2204513093 @default.
• W2999695716 cites W2310367590 @default.
• W2999695716 cites W2408443567 @default.
• W2999695716 cites W2565639579 @default.
• W2999695716 cites W2621144451 @default.
• W2999695716 cites W2682939364 @default.
• W2999695716 cites W2906257585 @default.
• W2999695716 cites W2963037989 @default.
• W2999695716 cites W2982770724 @default.
• W2999695716 cites W639708223 @default.
• W2999695716 doi "https://doi.org/10.1016/j.trc.2019.11.024" @default.
• W2999695716 hasPublicationYear "2020" @default.
• W2999695716 type Work @default.
• W2999695716 sameAs 2999695716 @default.
• W2999695716 citedByCount "27" @default.
• W2999695716 countsByYear W29996957162020 @default.
• W2999695716 countsByYear W29996957162021 @default.
• W2999695716 countsByYear W29996957162022 @default.
• W2999695716 countsByYear W29996957162023 @default.
• W2999695716 crossrefType "journal-article" @default.
• W2999695716 hasAuthorship W2999695716A5008053913 @default.
• W2999695716 hasAuthorship W2999695716A5036167561 @default.
• W2999695716 hasAuthorship W2999695716A5072823108 @default.
• W2999695716 hasAuthorship W2999695716A5081466855 @default.
• W2999695716 hasBestOaLocation W29996957161 @default.
• W2999695716 hasConcept C100776233 @default.
• W2999695716 hasConcept C111919701 @default.
• W2999695716 hasConcept C123657996 @default.
• W2999695716 hasConcept C126322002 @default.
• W2999695716 hasConcept C127413603 @default.
• W2999695716 hasConcept C166957645 @default.
• W2999695716 hasConcept C171146098 @default.
• W2999695716 hasConcept C179768478 @default.
• W2999695716 hasConcept C205649164 @default.
• W2999695716 hasConcept C22212356 @default.
• W2999695716 hasConcept C41008148 @default.
• W2999695716 hasConcept C52121051 @default.
• W2999695716 hasConcept C71924100 @default.
• W2999695716 hasConceptScore W2999695716C100776233 @default.
• W2999695716 hasConceptScore W2999695716C111919701 @default.
• W2999695716 hasConceptScore W2999695716C123657996 @default.
• W2999695716 hasConceptScore W2999695716C126322002 @default.
• W2999695716 hasConceptScore W2999695716C127413603 @default.
• W2999695716 hasConceptScore W2999695716C166957645 @default.
• W2999695716 hasConceptScore W2999695716C171146098 @default.
• W2999695716 hasConceptScore W2999695716C179768478 @default.
• W2999695716 hasConceptScore W2999695716C205649164 @default.
• W2999695716 hasConceptScore W2999695716C22212356 @default.
• W2999695716 hasConceptScore W2999695716C41008148 @default.
• W2999695716 hasConceptScore W2999695716C52121051 @default.
• W2999695716 hasConceptScore W2999695716C71924100 @default.
• W2999695716 hasFunder F4320306076 @default.
• W2999695716 hasLocation W29996957161 @default.
• W2999695716 hasOpenAccess W2999695716 @default.
• W2999695716 hasPrimaryLocation W29996957161 @default.
• W2999695716 hasRelatedWork W2031585355 @default.
• W2999695716 hasRelatedWork W2050049080 @default.
• W2999695716 hasRelatedWork W2051034899 @default.
• W2999695716 hasRelatedWork W2058391315 @default.
• W2999695716 hasRelatedWork W2104904330 @default.
• W2999695716 hasRelatedWork W2243403153 @default.
• W2999695716 hasRelatedWork W2347792353 @default.
• W2999695716 hasRelatedWork W2377276133 @default.
• W2999695716 hasRelatedWork W577552581 @default.
• W2999695716 hasRelatedWork W632098868 @default.
• W2999695716 hasVolume "111" @default.

• next