Beam-Column Joint Model for Nonlinear Analysis of Non-Seismically Detailed Reinforced Concrete Frame

Ning, Chao‐Lie; Yu, Bo; Li, Bing

doi:10.1080/13632469.2015.1104759

Cited by 27 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of DE algorithm for parameter identification of BWBN model is relatively mature, details can be found in Ning et al. 19 In this work, DE mainly serves as the identification results contrast method. The process and flowchart of identification are briefly presented below, and a flowchart of DE algorithm procedures to identify the parameters of BWBN model is shown in Figure 6.…”

Section: Solving the Differential Equation And Parameter Identificationmentioning

confidence: 99%

A multi-objective optimization algorithm for Bouc–Wen–Baber–Noori model to identify reinforced concrete columns failing in different modes

Noori

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part L:

View full text Add to dashboard Cite

Concrete columns are the most important load-bearing components in civil structures. The potential damage in reinforced concrete (RC) columns could be categorized into three different failure modes: flexural shear (FS) failure, flexural-failure (FF), and shear failure (SF). The corresponding hysteresis loops for each mode differ significantly. Therefore, a multi-parameter hysteretic restoring force model is needed to describe the hysteretic energy dissipation phenomenon and behavior. Identification of the optimal parameter values of a multi-parameter hysteresis model of RC columns under different failure modes is essential in the evaluation of structural inelastic seismic performance. In this paper, a multi-objective optimization algorithm called NSGA-II is employed to identify the parameters of Bouc–Wen–Baber–Noori model (BWBN) hysteresis model, this model has been used for describing the response and modelling restoring force behavior in several structural and mechanical engineering systems, that can fully describe the hysteretic restoring force characteristics of RC columns. An objective function for the restoring force is proposed to identify the parameters of BWBN model. In order to ensure the accuracy of identification, based on the sensitivity analysis, the parameters distribution law of RC columns in different failure modes is obtained. Furthermore, the reference values under different failure modes are proposed. The results presented in this paper will significantly reduce the calculation of subsequent identification. Twelve groups of experimental data are randomly selected to verify the feasibility of the above algorithm. It is demonstrated that using the multi-objective optimization algorithm leads to better identification accuracy with minimum prior experience. Performance of the algorithm is verified using simulated and experimental data. The experimental data of the RC columns were collected from the database of the Pacific Earthquake Engineering Research Center.

show abstract

Section: Solving the Differential Equation And Parameter Identificationmentioning

confidence: 99%

A multi-objective optimization algorithm for Bouc–Wen–Baber–Noori model to identify reinforced concrete columns failing in different modes

Noori

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part L:

View full text Add to dashboard Cite

show abstract

“…In this paper, the nonlinear hysteresis system was simulated by OpenSees platform. In order to simulate the pinching and degradation characteristics simultaneously, based on the work of Ning et al, the modified BWBN model with 13 parameters mentioned above was added to OpenSees uniaxial material library named ModiBWBN. The SDoF system model shown in Figure was developed in OpenSees using a zero‐length element with uniaxial material properties.…”

Section: Verification Of the Proposed Identification Algorithmmentioning

confidence: 99%

Identification of nonlinear hysteretic systems using sequence model‐based optimization

Jiang

Wen

Han

et al. 2020

Struct Control Health Monit

View full text Add to dashboard Cite

Summary Identification of nonlinear hysteretic systems has practical significance for the prediction of structural response. This paper develops a novel parameter identification method for nonlinear hysteretic systems. The Bouc–Wen model and its improved models are used to parametrically characterize the structural systems. Model parameters are identified using a given load‐displacement trajectory. The proposed method is developed under the framework of the sequence model optimization, which provides a broader search domain and needs fewer iterations. Firstly, a series of sensitivity analyses were conducted to investigate the effects of the Bouc–Wen parameters on the hysteresis behavior and to guide the identification process. Subsequently, based on the framework of the sequence model optimization, a novel strategy for identifying the model parameters of the nonlinear hysteresis system was proposed. Finally, the effectiveness and accuracy of the proposed algorithm were verified by the experimental data and numerical simulation results. The results indicate that the identified numerical model can accurately capture the strength and stiffness degradation and the pinching effect of the structural system. As a result, the predicted hysteretic trajectories are well agreed with the actual structural responses. The case studies demonstrate that the proposed method is sufficiently accurate and computationally efficient for the nonlinear hysteretic systems.

show abstract

“…Figure 6 shows the procedure of parameter calibration using the DE algorithm. Detailed description about the DE algorithm can be found in Ning et al (2016) and Yu et al (2016). Among them, the fitness function, which is used to measure the closeness between prediction and measurement, is defined by…”

Section: Parameters Calibrationmentioning

confidence: 99%

Biaxial hysteretic model for the characterization of quasi-static testing of shear-critical reinforced concrete columns

Ning

2018

Advances in Structural Engineering

Self Cite

View full text Add to dashboard Cite

Given that the biaxial hysteretic model of shear-critical reinforced concrete columns is usually developed by incorporating with the single-degree-of-freedom system to write its expression in differential equation of motion control, it is unfeasible to match with the experimental data of the biaxial quasi-static testing in terms of force-displacement relationship. Therefore, a simple and effective biaxial hysteretic model is proposed in this study based on the Bouc–Wen–Baber–Noori model for characterizing the biaxial quasi-static testing of shear-critical reinforced concrete columns. An incremental numerical algorithm is then proposed to solve the pair of coupled differential equations based on the backward Euler discretization method and Newton–Raphson iteration scheme. Moreover, the influence of critical model parameters affecting the typical hysteretic characteristics of shear-critical reinforced concrete columns, such as strength deterioration, stiffness degradation, pinching phenomena, and biaxial coupling effect, is investigated, respectively, to demonstrate the capability of the biaxial hysteretic model developed. Finally, a parameter calibration procedure using the differential evolution algorithm is introduced to calibrate the magnitude of involved model parameters by comparing with the experimental data of the biaxial quasi-static testing. According to the investigation, it is found that the developed biaxial hysteretic model can reasonably describe the typical hysteretic characteristics of shear-critical reinforced concrete columns under biaxial quasi-static excitation. Following the proposed parameter calibration procedure, the biaxial hysteretic model developed produces the biaxial hysteretic loops of shear-critical reinforced concrete columns, which is in good agreement with the experimental data once the magnitude of involved model parameters is calibrated.

show abstract

Beam-Column Joint Model for Nonlinear Analysis of Non-Seismically Detailed Reinforced Concrete Frame

Cited by 27 publications

References 21 publications

A multi-objective optimization algorithm for Bouc–Wen–Baber–Noori model to identify reinforced concrete columns failing in different modes

A multi-objective optimization algorithm for Bouc–Wen–Baber–Noori model to identify reinforced concrete columns failing in different modes

Identification of nonlinear hysteretic systems using sequence model‐based optimization

Biaxial hysteretic model for the characterization of quasi-static testing of shear-critical reinforced concrete columns

Contact Info

Product

Resources

About