Search Results (3)

The widely used Bouc–Wen–Baber–Noori (BWBN) hysteresis model, although effective in simulating hysteresis behaviors, does not account for variations in the pinching region of hysteretic behaviors. This can negatively impact the accuracy of the BWBN model in simulating structural responses and damage mechanisms in structures such as reinforced concrete (RC) and timber, which exhibit highly pinched hysteresis behavior when damaged by earthquakes. This paper introduces a BWBN model with varying pinching region characteristics (BWBN-VP model) which can degrade pinching stiffness and increase pinching effects under seismic loads. Unlike the original BWBN model using constant pinching stiffness ($k_p$), this modified new model, inspired by real-world structural damage, improves structural damage detection, identifiability, and analysis in real-world scenarios. Model validation uses experimental data from three RC column tests with different failure modes and hysteresis loop shapes, resulting in an ~0.98 correlation coefficient between the experimental and simulated responses. Further validation uses real-world seismic data from a six-story RC building and achieves an average correlation of ~0.97 with a minor 2.5% difference in the peak restoring forces compared to direct measurements. The proposed BWBN-VP model also accurately and realistically captures damage to both the elastic and pinching stiffness values of the building, with an average difference of ~4%. Results confirm that the BWBN-VP model, compared to the original, more accurately predicts hysteretic responses, especially in Shear Failure (SF) modes. Therefore, the BWBN-VP model, superior in simulating highly pinched behaviors in RC and timber structures, would be an advanced tool for resilient seismic design and Structural Health Monitoring (SHM). Full article

A restoring-force model is a versatile mathematical model that can describe the relationship between the restoring force and the deformation obtained from a large number of experiments. Over the past few decades, a large body of work on the development of restoring-force models has been reported in the literature. Under high intensity cyclic loadings or seismic excitations, reinforced concrete (RC) structures undergo a wide range of hysteretic deteriorations such as strength, stiffness and pinching degradations. These characteristic behaviors can be described by the multi-parameter Bouc-Wen-Baber-Noori (BWBN) model, which offers a wide range of applicability. This model has been applied for the response prediction and modeling restoring-force behavior in structural and mechanical engineering systems, by adjusting the distribution range of this model’s parameters. However, a major difficulty in utilizing the multi-parameter BWBN model is the parameters’ identification. In this paper, a deep neural network model is used to estimate the hysteresis parameters of the BWBN model. This model is one of the most versatile and widely used general hysteresis models that can describe the hysteretic behavior of RC columns. The experimental data of the RC columns used in this paper are collected from the database of the Pacific Earthquake Engineering Research Center (PEER). Firstly, the hysteretic loop obtained from a physical experiment is described by the BWBN model, and the parameters of the BWBN model are identified via a genetic optimization algorithm. Then a neural network is established by a backpropagation (BP) algorithm for associating the identified BWBN model parameters with physical parameters of the RC column. Finally, the regression analysis of the identified parameters is carried out to obtain the regression characteristics of the RC columns. The trained neural network model can directly identify the parameters of BWBN model based on the physical parameters of RC columns, and is effective and computationally efficient for multi-parameter BWBN model identification. The proposed approach overcomes the difficult problem of identifying the parameters of BWBN model and provides a promising approach for a wider application of this multi-parameter hysteresis model. Full article

An innovative symmetrical hysteresis model for reinforced concrete (RC) rectangular hollow columns is presented. The Bouc–Wen–Baber–Noori (BWBN) model was selected to depict the inelastic restoring forces and was improved by introducing a coefficient to describe the relationship between stiffness degradation and peak displacement. Sensitivity analysis was conducted at the local and global levels to clarify the importance of each parameter in the improved BWBN model. As such, a hybrid intelligence algorithm named PSOGSA was employed to identify the parameters of the BWBN model utilizing quasi-static tests of 16 hollow columns. The empirical formulas were regressed to bridge the connection between the BWBN model and design parameters of hollow columns. The results showed that the hysteresis curves of the improved BWBN model calibrated by the PSOGSA agreed well with the measured loops. In addition, the accuracy of the empirical prediction method of hysteretic parameters was checked through comparison with other hollow members. The calibrated improved BWBN model produced more precise hysteretic responses for RC hollow columns, since the peak and residual performance levels were simultaneously considered. Full article