Earthquake Engineering & Structural Dynamics最新文献

Reinforced concrete (RC) bridges are designed to remain safe and functional for their lifetime, during which the impacts of aging may result in performance degradation. Steel bar corrosion is one of the most common causes of structural performance degradation in RC structures subjected to earthquakes in seismic-prone areas. Therefore, to ensure the adequate seismic performance of RC bridges over the course of their life, it is necessary to investigate the effect of corrosion on seismic performance prediction. To this end, this research work uses the recently developed tools for seismic performance assessment, including advanced finite element (FE) modeling strategies for corroded RC structures. The newly developed advanced FE modeling strategy can capture the corrosion impact on bonding between steel bars and surrounding concrete, as well as the vulnerability of steel bars to buckling, in addition to other effects on the steel bar cross-sectional area, cover concrete spalling, and confinement level for core concrete. Using these newly developed strategies, the seismic performance of an RC bridge, impacted by corrosion over the course of its life, is examined in a probabilistic framework. In particular, it has been demonstrated that the conventional FE modeling approach, which neglects the corrosion-affected bond-slip and steel bar buckling, would lead to underestimated seismic risk for corroded RC bridges, specifically the seismic risk associated with the post-peak behavior.

This paper investigates the ability of machine learning (ML) to characterise the response of rocking structures when subjected to recorded earthquakes. In particular, it uses the structural parameters of a rigid block and strong ground motion characteristics to train two random forest (RF) models. The first model predicts whether a block, given that it initiates rocking motion, overturns or undergoes safe rocking, and identifies the main variables, i.e., structural and ground motion features, that govern such classification. Provided no overturning occurs, the second RF model predicts the peak rocking rotation of a block under ground motion records. Importantly, this study also employs interpretable ML techniques (such as partial dependence plots and SHAP additive explanations) to identify causal relationships between strong ground motion parameters and rocking response. The analysis shows that under high-intensity earthquakes, the peak ground velocity (PGV) governs the overturning of a rocking block. For earthquakes of moderate intensity, overturning becomes a more interactive phenomenon where the PGV, frequency/period and duration characteristics of the seismic signal contribute. Finally, this research shows that high safe rocking amplitude is also interactive, with velocity, displacement, (mean) frequency/period, and duration characteristics of the ground excitation playing a pivotal role.

Real-time hybrid simulation (RTHS) is an experimental testing methodology that divides a structural system into an analytical and an experimental substructure. The analytical substructure is modeled numerically, and the experimental substructure is modeled physically in the laboratory. The two substructures are kinematically linked together at their interface degrees of freedom, and the coupled equations of motion are solved in real-time to obtain the response of the complete system. A key challenge in applying RTHS to large or complex structures is the limited availability of physical devices, which makes it difficult to represent all required experimental components simultaneously. The present study addresses this challenge by introducing Online Cyber-Physical Neural Network (OCP-NN) models–neural network-based models of physical devices that are integrated in real-time with the experimental substructure during an RTHS. The OCP-NN framework leverages real-time data from a single physical device (i.e., the experimental substructure) to replicate its behavior at other locations in the system, thereby significantly reducing the need for multiple physical devices. The proposed method is demonstrated through RTHS of a two-story reinforced concrete frame subjected to seismic excitation and equipped with Banded Rotary Friction Dampers (BRFDs) in each story. BRFDs are challenging to model numerically due to their complex behavior which includes backlash, stick-slip phenomena, and inherent device dynamics. Consequently, BRFDs were selected to demonstrate the proposed framework. In the RTHS, one BRFD is modeled physically by the experimental substructure, while the other is represented by the OCP-NN model. The results indicate that the OCP-NN model can accurately capture the behavior of the device in real-time. This approach offers a practical solution for improving RTHS of complex structural systems with limited experimental resources.