• Computers and Concrete
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• v.31 no.5
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• pp.385-394
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• 2023

Displacement is an important element for evaluating the stability and failure mechanism of hydraulic structures. Digital image correlation (DIC) is a useful technique to measure a three-dimensional displacement field using two cameras without any contact with test material. The objective of this study is to evaluate the behavior of stacked geotextile tubes using the DIC technique. Geotextile tubes are stacked to build a small-scale temporary dam model to exclude water from a specific area. The horizontal and vertical displacements of four stacked geotextile tubes are monitored using a dual camera system according to the upstream water level. The geotextile tubes are prepared with two different fill materials. For each dam model, the interface layers between upper and lower geotextile tubes are either unreinforced or reinforced with a cementitious binder. The displacement of stacked geotextile tubes is measured to analyze the behavior of geotextile tubes. Experimental results show that as upstream water level increases, horizontal and vertical displacements at each layer of geotextile tubes initially increase with water level, and then remain almost constant until the subsequent water level. The displacement of stacked geotextile tubes depends on the type of fill material and interfacial reinforcement with a cementitious binder. Thus, the proposed DIC technique can be effectively used to evaluate the behavior of a hydraulic structure, which consists of geotextile tubes.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.6
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• pp.205-212
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• 2006

In recent investigations, reinforced concrete beams strengthened with Carbon Fiber Sheets (CFS) subjected to fatigue loading were reported to be failed at the ends of CFS by its debonding. U-shaped CFS were attached to both ends of the CFS when fatigue tests on strengthened beams were conducted to delay and/or prevent fatigue failures of adhesive interface. The experimental parameters of this study were the usage of anchorage at the ends of CFS, the number of CFS layers, and the applied load levels of 60%~90% of the static ultimate load obtained from the static tests. The failure modes and the load cycle-deflection relations were observed and discussed from the experimental results. Those results also showed that the U-shaped anchoring system changes the fatigue failure modes and influences greatly on the fatigue capacity of the strengthened beams.