Earthquake Engineering & Structural Dynamics最新文献

The steel plate shear walls (SPSWs) have been proven effective in reinforced concrete frames (RCFs) as a lateral force-resistant structure. Despite of advancements, accurately predicting the ultimate shear capacity of RCFs with SPSWs remains challenging using current simplified models. Additionally, the flexural capacity design procedure for the boundary elements (beams and columns) in previous studies of RCF-SPSWs involved intricate iterative procedures, hindering its widespread implementation. To address the two issues, this paper investigates the pushover responses and the plate-frame interaction (PFI) of an RCF-SPSWs system using theoretical and numerical methods. There are three main contributions. First, a theoretical model of ultimate shear capacity for RCF-SPSWs is proposed, which can also be used to predict shear contributions of boundary frames in RCF-SPSWs. Calculation errors for ultimate shear capacity of RCF-SPSWs and shear contribution from the boundary frame are only 3.7% and 6.7% respectively, which are reduced dramatically compared with the traditional model. A simplified schematic diagram for the global collapse mechanism (uniform distribution of plastic hinges within a structure) of RCF-SPSWs is developed to facilitate the calculation of internal work and reaction forces. Secondly, a flexural capacity design method for the boundary elements to avoid in-span plastic hinges is proposed. The proposed method enables the achievement of direct estimation of the flexural demands that could trigger a global collapse mechanism, all without intricate iterative procedures. The applicability of current assumptions for the design of steel boundary frame in RCF-SPSWs system is discussed, and engineering suggestions are provided to ensure safer and more economic designs. Comparison results confirmed the applicability of the proposed design method, which can be adopted to achieve the global collapse mechanism for RCF-SPSW system. Thirdly, impacts of yielding panel actions on the flexural capacity of boundary elements of RCF-SPSWs are clarified. Comparison results demonstrated that adding SPSWs to an RCF alters the axial force on boundary elements and significantly impacts the flexural capacity. A design suggestion is made to emphasize the importance of avoiding the balanced failure of boundary elements. The proposed theoretical model can be used to economize the cross-section of boundary elements in RCF-SPSWs system under seismic loads due to accurate prediction of their shear contribution; the proposed flexural capacity design method can achieve a global collapse mechanism, and thus the structural safety and energy dissipation capacity are improved; moreover, the building design efficiency is also improved due to avoidance of intricate iterative procedures.

To manage structural responses under various external forces, the increasing incorporation of seismic isolation and supplementary damping systems in modern civil engineering necessitates post-installation performance assessments. The challenge of accurately inferring system information from these complex dynamical structures, especially with limited sensor deployment, could be significant. From the perspective of solving inverse problems, this challenge hinges on constructing an input-output mapping that assures unique solutions, achievable through theoretical observability or symmetry analysis. We introduce a unified algorithm framework designed to accommodate various definitions of Lie derivatives, specifically for observability and symmetry analysis in dynamic systems with affine, non-affine, and unknown inputs—capabilities not fully achieved in previous studies. We demonstrate its application across typical dynamic scenarios, including both linear and nonlinear examples. We also present a numerical example featuring complex isolation systems with limited sensor layouts, illustrating how uniform convergence can be achieved in estimating all system states when an observable input-output mapping is utilized. Furthermore, an experimental example employing shaking table tests showcases the potential complications that arise when an unobservable input-output mapping is used.

Tuned Mass Dampers (TMDs) are commonly used passive control devices in practical engineering applications. However, motion-limiting stoppers are usually installed to control the excessive TMD displacement due to the building space limitation, resulting in piecewise nonlinearity and detuning of TMD. This paper studies the influence of elastic motion-limiting stoppers on the optimal design of TMDs through a piecewise stiffness TMD (PSTMD) model. Performance of a PSTMD with classical design is first investigated and proven to be ineffective. To optimize the PSTMD parameters, the motion of PSTMD is decoupled from the controlled structure, and the frequency response equation of PSTMD is obtained analytically through the averaging method. Subsequently, the solution of the optimal design frequency for PSTMD is transformed into the solution of the jump frequency in the frequency response equation. With the optimal frequency of PSTMDs, the optimal damping and control performance of PSTMDs are discussed and analyzed compared with classical linear design, which fully showcases the effectiveness of the novel design method. Finally, the effectiveness of the novel design method is verified using a nine-story benchmark frame structure, and the results demonstrate that the control performance of the optimal PSTMD can be improved by nearly $��10�\%�$ under specific seismic excitation, compared to the PSTMD with classical linear method. In summary, the novel design method can effectively take into account the influence of piecewise nonlinearity caused by elastic motion-limiting stoppers and improve the optimal control performance of TMD in a more realistic engineering environment.

The UC San Diego large high-performance outdoor shake table (LHPOST), which was commissioned on October 1, 2004 as a shared-use experimental facility of the National Science Foundation (NSF) Network for Earthquake Engineering Simulation (NEES) program, was upgraded from its original one degree-of-freedom (LHPOST) to a six-degree-of-freedom configuration (LHPOST6) between October 2019 and April 2022. A mechanics-based numerical model of the LHPOST6 able to capture the dynamics of the upgraded 6-DOF shake table system under bare table condition is presented in this paper. The model includes: (i) a rigid body kinematic model that relates the platen motion to the motions of the components attached to the platen, (ii) a hydraulic dynamic model that calculates the hydraulic actuator forces based on all fourth-stage servovalve spool positions, (iii) a hold-down strut model that determines the pull-down forces produced by the three hold-down struts, (iv) Bouc-Wen models utilized to represent the dissipative forces in the shake table system, and (v) a rigid body dynamic model borrowed from robotic analysis governing the translational and rotational motions of the platen subjected to the forces from the various components attached to the platen. Extensive validation against experimental data shows excellent agreement for tri-axial and six-axial earthquake shake table tests. This validated model can be coupled with finite element models of test specimens to study the interaction between the shake table system and the specimens, and it offers potential for enhancing motion tracking performance through off-line controller tuning or advanced control algorithm development.

The slope of a linear approximation of a probabilistic seismic hazard curve, when it is represented in the log-log scale, is a key parameter for seismic risk assessment based on closed-form solutions, and other applications. On the other hand, it is observed that different hazard models can provide, at the same site, comparable ground shaking, yet appreciably different slopes for the same exceedance return period. Moreover, the slope at a given return period can increase or decrease from low- to high-hazardous sites, depending on the models the probabilistic seismic hazard analysis (PSHA) is based on. In the study, the sensitivity of the slope to the main model components involved in PSHA was explored, that is: the earthquake rate, the magnitude and source-to-site distance distributions, and the value of the residual of ground motion models (GMM). With reference to a generic site, affected by an ideal seismic source zone, where magnitude follows the Gutenberg-Richter (G-R) relationship, it was found that the local slope of hazard curve increases with the following factors in descending order of importance: (i) increasing distance from the source; (ii) decreasing maximum magnitude and increasing $�b$-value of the G-R model; (iii) increasing rate of earthquakes of interest; (iv) increasing residual of the GMM. These results help explain the systematic differences in hazard curve slopes found in three authoritative hazard models for Italy, and the related impact on simplified risk assessment.

Machine-learning models play a crucial role in structural seismic risk assessment and facilitate decision-making by analyzing complex data patterns. However, the dynamic nature of real-world data introduces challenges, particularly data drift, which can significantly affect model performance. This adversely affects machine-learning models intended to aid emergency responders and disaster recovery teams. This study primarily focused on assessing the impact of column corrosion-induced data drift on the performance of machine-learning models for seismic risk assessment of bridges. The machine-learning model performance was evaluated with and without considering the impact of corrosion. The results revealed a significant decrease in prediction accuracy when the data drift effect was not considered. To address this challenge, this study proposes integrating principal component analysis-based anomaly detection to enhance the model performance. The optimized model considering drift demonstrates significant improvements in accuracy across corroded bridges aged 25, 50, and 75 years, with accuracy rates increasing from 90%, 85%, and 81% to 98%, 97%, and 96%, respectively.

The study presents experimental and numerical results on two-dimensional and three-dimensional full-scale exterior non-seismically designed (NSD) reinforced concrete (RC) beam-column joint subassemblies subjected to quasi-static cyclic lateral loading. The tests were augmented by detailed 3D finite element modeling to obtain further information about the joint behavior. Through these systematic investigations and their detailed evaluation, clear conclusions could be drawn on the effect of transverse beams and slab on the overall seismic behavior of beam-column joints, where the joint core was devoid of transverse reinforcement. It was found that the presence of transverse beams enhanced both the ultimate joint shear strength and joint shear strength at first joint cracking. The crack development in concrete revealed that the diagonal joint shear cracks extended from the joint core into the transverse beams. The slab participation under flexure, when acting in tension, decreased with increase in drift due to intervening loss in joint stiffness, which was inconsistent with the observations in subassemblies where the joints were confined with transverse reinforcement. It was found that the inclined cracking in the transverse beams was caused due to joint shear stresses and aggravated due to torsional stresses when a slab was present. Normalized joint shear stress and principal tensile stress values were evaluated for first joint shear cracking and ultimate joint shear strength. These values may be useful for the seismic assessment of non-seismically designed beam-column joints with transverse beams and slab.

The seismic risk mitigation plans are vital since vulnerable structures are prone to partial or total collapse under the effect of future major earthquake events. Therefore, vulnerable structures in large building stocks should be determined using robust and accurate methods to prevent loss of lives and property. In the current state-of-the-art, the risk states (i.e., whether risky or not) of structures completely depend on the experience of the reconnaissance team of engineers, which could not result in standardized decisions. In this study, machine learning has been integrated into the decision-making algorithm to classify more precise and reliable seismic risk states of masonry buildings, categorizing them into up to four risk categories. For this purpose, a large database, including 12 features and detailed seismic risk analysis results of 4356 masonry buildings, is formed. Firstly, the input variables are preprocessed using feature engineering methods. Then, several machine learning algorithms are utilized to produce a network to estimate the risk state of masonry buildings in association with the risk states obtained from the detailed analysis results. As a result of the analysis of these algorithms, the correct prediction percentages for the testing database of the proposed method for two, three, and four risk states classification are predicted as approximately 87.5%, 86.6%, and 79.0%, respectively. This new approach makes it possible to produce risk color maps of large building stocks and reduce the number of buildings that require immediate action.

Identifying near-fault pulse-like ground motions from extensive ground motion databases holds paramount importance, as it provides a pivotal foundation for further inquiries into this specific type of ground motions, including the modeling of such stochastic processes as well as thorough analysis of their potential impact on structures and infrastructure systems. Currently, a diverse array of quantitative methods for identifying pulse-like ground motions have emerged, all of which demonstrate good accuracy within their respective research scopes. However, due to the limitations of each individual method in identifying specific cases, these diverse approaches often yield inconsistent results for certain ground motion records, posing a significant challenge in establishing a reliable classification criterion that relies solely on a single identification method. To address this issue, the present study adopts a multifaceted approach. Instead of improving a single time-frequency analysis-based identification method, it carefully conducts a selection of seven baseline methods through a systematic overview of the field. By leveraging the analytic hierarchy process (AHP), a comprehensive categorization method is developed that integrates the strengths of each approach, resulting in a more robust and credible classification criterion. According to the devised category indicator, ground motions can be classified into four categories: Category A comprises definitively pulse-like ground motions; Category B comprises apparently pulse-like ground motions; Category C consists of probably pulse-like ground motions; and Category D encompasses ground motions unlikely to exhibit pulse-like characteristics. It provides a more elaborate classification beyond the binary distinction of pulse-like and non-pulse-like ground motions associated with traditional onefold classification methods. For validation purposes, a basic dataset comprising near-fault ground motion records from the NGA-West 2 database has been utilized. To verify the comprehensive categorization method, two datasets of pulse-like ground motion records suggested by FEMA and PEER and one dataset of ground motion records collected during the 1999 Chi-Chi earthquake are addressed. Numerical examples illustrate the remarkable effectiveness of the proposed method in identifying near-fault pulse-like ground motions based on their varying degrees of pulse-like characteristics.

Ultra-high voltage (UHV) converter stations are critical nodes in power grids. This paper proposes a probabilistic framework for assessing and mitigating the seismic risk of UHV converter station systems to enhance the seismic performance of the grid. First, a Bayesian network model for the system functionality of UHV converter stations was established based on the enumeration of equipment failure scenarios. Conditional probability tables (CPTs) were used to represent the causal relationship among subsystems and system functionality. Inference calculations were conducted using Bayes’ theorem. Then, the definition of system seismic loss risk distribution was proposed to assess the seismic risk of the system over its entire lifespan. The feasibility of this framework was validated using a specific UHV converter station, yielding analytical solutions for the probability distribution of system functionality and seismic vulnerability curves. Additionally, the cost-effectiveness of several risk mitigation strategies was assessed. A cost-benefit analysis was performed from the perspectives of both the expected loss of a single earthquake and the life-cycle cost. The framework comprehensively considered the constraints imposed by series, parallel, and bypass control devices on the system's functionality. It was revealed that the seismic loss risk for UHV converter stations exhibits a characteristic of low probability but high loss.