Experimental study and optimal design of <scp>UHPC</scp> T‐shaped beam

Deng, Shuwen; Huang, Zhiming; Yan, Banfu; Shen, Lian; Huang, Zheng; Wang, Yi

doi:10.1002/suco.202300068

Structural Concrete

2024

DOI: 10.1002/suco.202300068

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Experimental study and optimal design of UHPC T‐shaped beam

Shuwen Deng,

Zhiming Huang,

Banfu Yan

et al.

Abstract: In this paper, the ordinary reinforced UHPC T‐shaped beam, steel plate reinforced UHPC T‐shaped beam and prestressed UHPC T‐shaped beam were compared by conducting the flexure test. The vertical displacement, crack development, strain change, and crack resistance were discussed. The steel plate reinforced UHPC T‐shaped beam shown better cracking resistance and mechanical properties. Based on the research, the multi‐scale finite element model of the steel plate reinforced UHPC T‐shaped beam was established, and… Show more

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Cited by 2 publications

References 27 publications

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Strain distribution prediction in UHPC beams using deep learning model

Zhang,

Chen,

Zheng

et al. 2024

Structural Concrete

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Over the last three decades, ultra‐high‐performance concrete (UHPC) has emerged as a highly innovative cementitious engineering material. This paper utilizes a distributed fiber optic sensor based on optical frequency domain reflectometry (OFDR) combined with the deep learning model to monitor and predict the strain distribution in UHPC T‐beams subjected to two point bending experiment. These sensors are deployed on the side surfaces of the UHPC T‐beams to capture the strain distribution when subjected to vertical loads, facilitating the determination of crack locations based on strain distribution peaks. While time series modeling is widely used in civil engineering to monitor potential damage using sensor data, its application in tracking and predicting known cracks is less explored. To address this gap, a Long Short‐Term Memory (LSTM) neural network model is developed to forecast the increase in the peak value of the strain distribution prior to structural damage, thus predicting crack initiation. The accuracy of the proposed LSTM model in predicting the UHPC strain distribution was thoroughly investigated. The results demonstrate excellent agreement between the predicted strain distributions and those detected by the ODISI 6000 monitoring system. The root‐mean‐square errors of the model‐predicted strains are generally below 10 με, with an average coefficient of determination (R2) reaching up to 98%, indicating a high degree of accuracy.

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Strain distribution prediction in UHPC beams using deep learning model

Zhang,

Chen,

Zheng

et al. 2024

Structural Concrete

View full text Add to dashboard Cite

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Ductility‐oriented design of UHPC T‐beams: Mechanical model and design recommendations

Alsomiri,

Liu,

Wang

et al. 2024

Structural Concrete

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Ultrahigh performance concrete (UHPC) has recently become a major focus garnering substantial attention due to its remarkable mechanical properties. UHPC has been increasingly employed across diverse projects in the construction industry. However, the structural ductility of UHPC members has yet to be fully established and is often compromised by the manifestation of the crack localization phenomenon. This paper presents the flexural test results of five T‐beams and introduces a model for predicting the flexural capacity and failure modes of UHPC T‐beams. The model employs the curvature ductility index as a measurement for evaluating and ensuring the member's ductility. The results show that the flexural behavior of UHPC T‐beams can be characterized by four key points representing cracking, reinforcement yielding, crack localization, and post‐localization capacity. The validity of the model is substantiated by experimental data from this study and complemented by test data collected from the literature. The proposed model is then employed to derive ductility‐oriented design limits, including minimum and maximum reinforcement ratios and minimum localization strain capacity. Finally, the paper summarizes the design recommendations and provides a classification of section conditions, reinforcement limits, localization strain limits, adequate ductility range, and the feasible ductile design range.

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Experimental study and optimal design of UHPC T‐shaped beam

Cited by 2 publications

References 27 publications

Strain distribution prediction in UHPC beams using deep learning model

Strain distribution prediction in UHPC beams using deep learning model

Ductility‐oriented design of UHPC T‐beams: Mechanical model and design recommendations

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