• Title/Summary/Keyword: Concrete bridges

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A Study on the Estimation of Prestress Losses in Prestressed Concrete Box Girder Bridges (프리스트레스트 콘크리트 박스 거더 교량의 프리스트레스 손실 추정에 관한 연구)

  • Oh, Byung-Hwan;Yang, In-Hwan;Kim, Ji-Sang
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.5 no.2
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    • pp.111-120
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    • 2001

  • This paper aims at estimating instantaneous prestress losses by measuring the actual prestress forces in prestressed concrete (PSC) box girder bridges. Measurement were taken to study initial prestress losses such as friction losses and slip losses. A new strain gauge system was developed to measure strains in internal tendons. The system was installed on a total of 20 tendons in a PSC box girder bridges. The variation of prestress forces were monitored during prestressing tendon and after prestress transfer. The prestress losses are also calculated including friction losses and slip losses. The measured data were compared with the theoretical values. The result shows that the measured prestress forces agree well with the theoretical values. It is shown that prestress force of each strand in the same tendon is a bit different. This study also shows that prestress losses of continuity tendons during prestress transfer are significantly different each other, which results from the variety of buttress location and tendon profile. The present study provides realistic information on the estimation of actual prestress forces and losses in PSC box girder bridges.

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Autonomous smart sensor nodes for global and local damage detection of prestressed concrete bridges based on accelerations and impedance measurements

  • Park, Jae-Hyung;Kim, Jeong-Tae;Hong, Dong-Soo;Mascarenas, David;Lynch, Jerome Peter
    • Smart Structures and Systems
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    • v.6 no.5_6
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    • pp.711-730
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    • 2010

  • This study presents the design of autonomous smart sensor nodes for damage monitoring of tendons and girders in prestressed concrete (PSC) bridges. To achieve the objective, the following approaches are implemented. Firstly, acceleration-based and impedance-based smart sensor nodes are designed for global and local structural health monitoring (SHM). Secondly, global and local SHM methods which are suitable for damage monitoring of tendons and girders in PSC bridges are selected to alarm damage occurrence, to locate damage and to estimate severity of damage. Thirdly, an autonomous SHM scheme is designed for PSC bridges by implementing the selected SHM methods. Operation logics of the SHM methods are programmed based on the concept of the decentralized sensor network. Finally, the performance of the proposed system is experimentally evaluated for a lab-scaled PSC girder model for which a set of damage scenarios are experimentally monitored by the developed smart sensor nodes.

  • https://doi.org/10.12989/sss.2010.6.5_6.711 인용 KSCI

Predicting strength and strain of circular concrete cross-sections confined with FRP under axial compression by utilizing artificial neural networks

  • Yaman S. S. Al-Kamaki;Abdulhameed A. Yaseen;Mezgeen S. Ahmed;Razaq Ferhadi;Mand K. Askar
    • Computers and Concrete
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    • v.34 no.1
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    • pp.93-122
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    • 2024

  • One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.

  • https://doi.org/10.12989/cac.2024.34.1.093 인용