Seismic Performance of Precast, Pretensioned, and Cast-in-Place Bridges: Shake Table Test Comparison

Mantawy, Islam M.; Thonstad, Travis; Sanders, David H.; Stanton, John F.; Eberhard, Marc O.

doi:10.1061/(asce)be.1943-5592.0000934

Cited by 35 publications

(23 citation statements)

References 5 publications

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“…To prevent any possible slippage, the longitudinal bars of the cage could be partially embedded in the UHPC shell and shared with column concrete which will enhance the bond between the shell and the conventional concrete ( 3 ). Another possible solution for this issue would be the use of the UHPC shell with corrugation in the inner surface similar to the corrugation in socket connections ( 18 – 20 ).…”

Section: Discussionmentioning

confidence: 99%

Cyclic Test of Concrete Bridge Column Utilizing Ultra-High Performance Concrete Shell

Caluk

Mantawy

Azizinamini

2020

Transportation Research Record: Journal of the Transportation R

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Ultra-high performance concrete (UHPC) is a durable material that can be used in constructing new and unique structural elements. This research utilizes UHPC to construct prefabricated shells that act as stay-in-place forms for bridge columns and eliminate the use of traditional formwork. These innovative structural elements reduce the on-site construction time, improve the structural performance of the column, and act as a protective layer in aggressive environments. Generally, during the construction process, the prefabricated UHPC shell is placed around the column reinforcement, which is fabricated using conventional methods. To connect the UHPC shell and column reinforcement with the footing and footing dowels, a step made of UHPC is utilized. The UHPC step connection is designed to shift the plastic hinge away from the column-to-footing interface. In the next stage, normal concrete is cast inside the shell, forming a concrete-filled UHPC shell. The final stage of construction involves placing and connecting a prefabricated cap-beam using the same UHPC step connection. The column specimen was tested under constant axial load and incremental lateral load. In this test, the UHPC shell cracked on the north side at a drift ratio of 3%; however, the column had a significant capacity and behaved similarly to a conventional reinforced concrete column during higher cycles of drift ratios. The test was completed after the column had reached a drift ratio of 7.5% when the first bar ruptured. No damage occurred in the footing and UHPC step which proved that the design was successful in shifting the plastic hinge away from the column-to-footing interface.

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Section: Discussionmentioning

confidence: 99%

Cyclic Test of Concrete Bridge Column Utilizing Ultra-High Performance Concrete Shell

Caluk

Mantawy

Azizinamini

2020

Transportation Research Record: Journal of the Transportation R

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“…This data collection provided an opportunity to apply machine learning techniques for SHM. A detailed presentation of the system is provided in Thonstad et al 22 ; comparison between the rocking bridge system with conventional concrete bridge system is presented in Mantawy et al 23 ; damage (fracture) identification of longitudinal reinforcement is presented in Mantawy et al 24 ; and numerical modeling calibration for the tested bridge is presented in Mantawy et al 25 Readers are advised to read the abovementioned papers for more insights; however, the following sub‐sections intend to provide necessary background on the tested specimen, ground motions, and identified damages.…”

Section: Rocking Bridge Specimenmentioning

confidence: 99%

“…This paper presents the use of time-series data (accelerations and displacements) collected from highperformance bridge system utilizing pretensioned rocking columns during a shake-table experiment at the University of Nevada, Reno [22][23][24][25] to monitor the health of the bridge during sequential ground motions. To overcome the drawbacks of methods utilizing 1,21 time-series data in training machine learning models especially the small data size, the paper utilizes various techniques to encode time-series data into images to feed the input layer of CNN models which are very effective tools in computer vision.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network based structural health monitoring for rocking bridge system by encoding time‐series into images

Mantawy

2021

Structural Contr & Hlth

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Summary Structural health monitoring of infrastructure especially bridges plays a vital role in post‐earthquake recovery. Coupling emerging techniques in machine learning with structural health monitoring can provide unprecedented tools for damage detection and identification. This paper explores the use of time‐series acceleration or displacement data collected from a shake‐table experiment of a two‐span bridge utilizing pretensioned rocking columns to predict the damage state of each bridge bent, where the major identified damage was the fracture of the longitudinal bars. To overcome the limitation of small data size collected during the shake‐table test that hindered the use of artificial neural networks and recurrent neural networks, the time‐series data were encoded into images using three methods Gramian angular summation field, Gramian angular difference field, and Markov transition field. Then, the encoded images were used as an input for convolutional neural network models. Three different data entries for the input layers were used including encoded images from recorded accelerations, drift ratios, and both. Two training/testing scenarios were proposed to test the efficacy of the convolutional neural networks. Convolutional neural network models trained on Markov transition field encoded images from acceleration performed with 100% accuracy during the training phase and more than 94% for the testing phase.

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“…In this context researchers [1][2][3][4][5][6][7][8][9][10] have developed concepts were precast elements are connected via ungrouted tendons and extra elements that work as dampers. The connections are dry, minimizing the on-site construction time.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Response of a Rigid Slab Supported by Four Rigid Cylinrical Rocking and Wobbling Columns

Rajah¹,

Bachmann²,

Vassiliou³

et al. 2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Seismic Performance of Precast, Pretensioned, and Cast-in-Place Bridges: Shake Table Test Comparison

Cited by 35 publications

References 5 publications

Cyclic Test of Concrete Bridge Column Utilizing Ultra-High Performance Concrete Shell

Cyclic Test of Concrete Bridge Column Utilizing Ultra-High Performance Concrete Shell

Convolutional neural network based structural health monitoring for rocking bridge system by encoding time‐series into images

Dynamic Response of a Rigid Slab Supported by Four Rigid Cylinrical Rocking and Wobbling Columns

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