Seismic design and subassemblage tests of buckling-restrained braced RC frames with shear connector gusset connections

Bai, Jiulin; Chen, Huiming; Zhao, Jian; Liu, Minghui; Jin, Shan

doi:10.1016/j.engstruct.2021.112018

Cited by 82 publications

(10 citation statements)

References 43 publications

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“…Table 2 specifies its mix proportions. Six 150 mm cubes of that ready-mix were poured and cured under same conditions as the column specimens, and their compressive strengths were measured on the test day (Bai et al, 2021).…”

Section: Materials Propertiesmentioning

confidence: 99%

An experimental study of the axial compression performance of two-part precast concrete columns

Wei

2021

Advances in Structural Engineering

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Recycled lump concrete (RLC) made with demolished concrete lumps (DCLs) and fresh concrete (FC) provides a solution for effective waste concrete recycling. To promote the development of precast RLC structures, this study tested a new type of connection for precast concrete columns: connecting the upper and the lower halves of columns with bent longitudinal reinforcements and structural adhesive. In this work the behavior of precast RLC columns with the new connection was studied under axial compression. The axial compressive strength of nine two-part columns was tested. The effects of the degree of bending in the longitudinal reinforcement, the replacement ratio of DCLs and the stirrup spacing were investigated. Tests showed that: (1) the failure mode of precast concrete columns is different from that of cast-in-place columns; (2) when the strength of the waste concrete is close to that of the fresh material, there is no significant difference in the axial compression performance of either precast or cast-in-place columns; (3) the bent longitudinal reinforcement causes the axial load bearing capacity of precast concrete columns to be 4.2%–12.3% lower than that of a similar cast-in-place column; (4) reducing the stirrup spacing has little effect on a precast column’s axial load bearing capacity and ductility; (5) when using Chinese and American codes to predict the axial load bearing capacity of the column, the predicted value should be multiplied by a reduction factor.

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Section: Materials Propertiesmentioning

confidence: 99%

An experimental study of the axial compression performance of two-part precast concrete columns

Wei

2021

Advances in Structural Engineering

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“…Existing approaches to predict the seismic response of an RC structural component include physical experiments and numerical modeling. Physical experiments are regarded as the most reliable approach and are typically performed by displacement-controlled quasi-static cyclic loading or dynamic shake table tests (Bai et al, 2021). However, due to the extensive costs associated with large-scale structural testing, experimental validation is not always feasible.…”

Section: Introductionmentioning

confidence: 99%

Data-driven seismic response prediction of structural components

Luo

Paal

2021

Earthquake Spectra

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Lateral stiffness of structural components, such as reinforced concrete (RC) columns, plays an important role in resisting the lateral earthquake loads. The lateral stiffness relates the lateral force to the lateral deformation, having a critical effect on the accuracy of the lateral seismic response predictions. The classical methods (e.g. fiber beam–column model) to estimate the lateral stiffness require calculations from section, element, and structural levels, which is time-consuming. Moreover, the shear deformation and bond-slip effect may also need to be included to more accurately calculate the lateral stiffness, which further increases the modeling difficulties and the computational cost. To reduce the computational time and enhance the accuracy of the predictions, this article proposes a novel data-driven method to predict the laterally seismic response based on the estimated lateral stiffness. The proposed method integrates the machine learning (ML) approach with the hysteretic model, where ML is used to compute the parameters that govern the nonlinear properties of the lateral response of target structural components directly from a training set composed of experimental data (i.e. data-driven procedure) and the hysteretic model is used to directly output the lateral stiffness based on the computed parameters and then to perform the seismic analysis. We apply the proposed method to predict the lateral seismic response of various types of RC columns subjected to cyclic loading and ground motions. We present the detailed model formulation for the application, including the developments of a modified hysteretic model, a hybrid optimization algorithm, and two data-driven seismic response solvers. The results predicted by the proposed method are compared with those obtained by classical methods with the experimental data serving as the ground truth, showing that the proposed method significantly outperforms the classical methods in both generalized prediction capabilities and computational efficiency.

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“…Structural damage will lead to changes in the physical properties of the structure, and these changes are often reflected in the monitoring sequence [6,7]. In the previous evaluation of structural safety performance, most of its data were related to time [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Post-Processing of High Formwork Monitoring Data Based on the Back Propagation Neural Networks Model and the Autoregressive—Moving-Average Model

Yang

Yao

2021

Symmetry

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Many high formwork systems are currently equipped with health monitoring systems, and the analysis of the data obtained can determine whether high formwork is a hazard. Therefore, the post-processing of monitoring data has become an issue of widespread concern. In this paper, we discussed the fitting effect of the symmetrical high formwork monitoring data using the autoregressive–moving-average (ARMA) model and the back propagation neural networks (BPNN) combined model to process. In the actual project, the symmetry of the high formwork system allows the analysis of local monitoring results to be well extended to the whole. For the establishment of the ARMA model, the accurate judgment of the model order has a significant impact. In this paper, back propagation neural networks (BPNN) are used to simulate the ARMA process. The order of the ARMA model is estimated by determining the optimal neural network structure, which is suitable for linear or nonlinear sequences. We validated this approach from the ARMA model data simulated in Monte Carlo and compared it with the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The length of the sequence, the coefficients and the order of the ARMA model are considered as factors that influence the judgment effect. Under different conditions, the BPNN always shows an accuracy rate of more than 90%, while the BIC only has a higher accuracy rate when the model order is low and the judgment efficiency of the AIC is below 50%. Finally, the proposed method successfully modeled the stress sequence and obtained the stress change trend. Compared with AIC and BIC, the efficiency of the processing time series is increased by about 50% when an order is obtained by BPNN.

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Seismic design and subassemblage tests of buckling-restrained braced RC frames with shear connector gusset connections

Cited by 82 publications

References 43 publications

An experimental study of the axial compression performance of two-part precast concrete columns

An experimental study of the axial compression performance of two-part precast concrete columns

Data-driven seismic response prediction of structural components

Post-Processing of High Formwork Monitoring Data Based on the Back Propagation Neural Networks Model and the Autoregressive—Moving-Average Model

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