• 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.

• Computers and Concrete
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• v.32 no.5
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• Steel and Composite Structures
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• v.22 no.2
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• pp.449-472
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• 2016

This paper studies the seismic behavior of reinforced concrete (RC) walls with encased cold-formed and thin-walled (CFTW) steel truss, which can be used as an alternative to the conventional RC walls or steel reinforced concrete (SRC) composite walls for high-rise buildings in high seismic regions. Seven one-fourth scaled RC wall specimens with encased CFTW steel truss were designed, manufactured and tested to failure under reversed cyclic lateral load and constant axial load. The test parameters were the axial load ratio, configuration and volumetric steel ratio of encased web brace. The behaviors of the test specimens, including damage formation, failure mode, hysteretic curves, stiffness degradation, ductility and energy dissipation, were examined. Test results indicate that the encased web braces can effectively improve the ductility and energy dissipation capacity of RC walls. The steel angles are more suitable to be used as the web brace than the latticed batten plates in enhancing the ductility and energy dissipation. Higher axial load ratio is beneficial to lateral load capacity, but can result in reduced ductility and energy dissipation capacity. A volumetric ratio about 0.25% of encased web brace is believed cost-effective in ensuring satisfactory seismic performance of RC walls. The axial load ratio should not exceed the maximum level, about 0.20 for the nominal value or about 0.50 for the design value. Numerical analyses were performed to predict the backbone curves of the specimens and calculation formula from the Chinese Code for Design of Composite Structures was used to predict the maximum lateral load capacity. The comparison shows good agreement between the test and predicted results.