Search Results (591)

The 2020 southern Puerto Rico earthquake sequence highlighted the severe seismic vulnerability of informally constructed two-story reinforced concrete (RC) houses. This study examines the failure mechanisms of these structures and assesses the effectiveness of first-floor RC shear-wall retrofitting. Nonlinear pushover and dynamic time–history analyses were performed using fiber-based distributed plasticity models for RC frames and nonlinear macro-elements for second-floor masonry infills, which introduced a significant inter-story stiffness imbalance. A bi-directional seismic input was applied using spectrally matched, near-fault pulse-like ground motions. The findings for the as-built structures showed that stiffness mismatches between stories, along with substantial strength and stiffness differences between orthogonal axes, resulted in concentrated plastic deformations and displacement-driven failures in the first story—consistent with damage observed during the 2020 earthquakes. Retrofitting the first floor with RC shear walls notably improved the performance, doubling the lateral load capacity and enhancing the overall stiffness. However, the retrofitted structures still exhibited a concentration of inelastic action—albeit with lower demands—shifted to the second floor, indicating potential for further optimization. Full article

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable. Full article

On 22 May 2012, a magnitude Mw 5.6 earthquake struck the Pernik region of western Bulgaria, causing structural damage in nearby cities, including Sofia. This study assesses the seismic response of a six-story reinforced concrete building in central Sofia, utilizing real accelerogram data recorded at the basement (SGL1) and sixth floor (SGL2) levels during the earthquake. Using the Kanai–Yoshizawa (KY) model, the study estimates inter-story motion and assesses amplification effects across the structure. Analysis of peak ground acceleration (PGA), velocity (PGV), displacement (PGD), and spectral ratios reveals significant dynamic amplification of peak ground acceleration and displacement on the sixth floor, indicating flexible and dynamic behavior, as well as potential resonance effects. The analysis combines three spectral techniques—Horizontal-to-Vertical Spectral Ratio (H/V), Floor Spectral Ratio (FSR), and the Random Decrement Method (RDM)—to determine the building’s dynamic characteristics, including natural frequency and damping ratio. The results indicate a dominant vibration frequency of approximately 2.2 Hz and damping ratios ranging from 3.6% to 6.5%, which is consistent with the typical damping ratios of mid-rise concrete buildings. The findings underscore the significance of soil–structure interaction (SSI), particularly in sedimentary basins like the Sofia Graben, where localized geological effects influence seismic amplification. By integrating accelerometric data with advanced spectral techniques, this research can enhance ongoing site-specific monitoring and seismic design practices, contributing to the refinement of earthquake engineering methodologies for mitigating seismic risk in earthquake-prone urban areas. Full article

The widespread application of large underground water tank structures in urban areas necessitates reliable design guidelines to ensure their safety as critical infrastructure. This paper investigated the seismic response of large underground water tank structures considering fluid–structure interaction (FSI). Coupled Eulerian–Lagrangian (CEL) was employed to analyze the highly nonlinear FSI caused by intense fluid sloshing during earthquakes. The patterns of fluid sloshing amplitude observed from the finite element model were summarized based on analyses of fluid velocity, hydrodynamic stress components, and overall kinetic energy. In addition, the seismic response of the water tank structure was thoroughly assessed and compared with the simulation results of the empty tank structure. The results indicate that significant fluid sloshing occurs within the structure under seismic excitation. The amplitude of fluid sloshing increases horizontally from the center toward the edges of the structure, corresponding to higher hydrodynamic loads at the side area of the structure. By comparing the analysis results of the water tank structure with and without water, it was concluded that FSI is the primary cause of structural damage during an earthquake. The hydrodynamic loads on the roof, diversion walls, and external walls lead to significant localized damage. Full article

Aftershocks are inevitable phenomena following a mainshock, especially after a major earthquake. However, the cumulative damage caused by aftershocks and its impact on structural performance evaluation has only recently received significant attention. This study explores the effects of mainshock–aftershock (MS–AS) sequences, including multiple consecutive aftershocks, acting on 3D steel moment-resisting frame structures. Following nonlinear time history analysis, several fundamental variables such as residual interstory drift, maximum displacement, plastic hinge formation, and base shear are evaluated to examine cumulative damage. In this context, the findings depicted in terms of aftershocks play a significant role in exacerbating plastic deformations and damage accumulation in steel moment frames. Subsequently, to mitigate cumulative damage on steel moment frames, retrofitting strategies were implemented. Retrofitting strategies effectively reduce cumulative damage and improve seismic resilience under multiple earthquake events. This research highlights the limitations of single-event seismic assessments and the need to incorporate sequential earthquake effects in design and retrofit practices. Furthermore, it provides new insights into mitigating further damage by retrofitting existing structures under multiple earthquakes. Full article

Modeling mainshock–aftershock ground motions is essential for seismic risk assessment, especially in regions experiencing frequent earthquakes. Recent studies have often employed Copula-based joint distributions or machine learning techniques to simulate the statistical dependency between mainshock and aftershock parameters. While effective at capturing nonlinear correlations, these methods are typically black box in nature, data-dependent, and difficult to generalize across tectonic settings. More importantly, they tend to focus solely on marginal or joint parameter correlations, which implicitly treat mainshocks and aftershocks as independent stochastic processes, thereby overlooking their inherent spectral interaction. To address these limitations, this study proposes an explicit and parameterized modeling framework based on the evolutionary power spectral density (EPSD) of random ground motions. Using the magnitude difference between a mainshock and an aftershock as the control variable, we derive attenuation relationships for the amplitude, frequency content, and duration. A coherence function model is further developed from real seismic records, treating the mainshock–aftershock pair as a vector-valued stochastic process and thus enabling a more accurate representation of their spectral dependence. Coherence analysis shows that the function remains relatively stable between 0.3 and 0.6 across the 0–30 Rad/s frequency range. Validation results indicate that the simulated response spectra align closely with recorded spectra, achieving R² values exceeding 0.90 and 0.91. To demonstrate the model’s applicability, a case study is conducted on a representative frame structure to evaluate seismic vulnerability and economic loss. As the mainshock PGA increases from 0.2 g to 1.2 g, the structure progresses from slight damage to complete collapse, with loss rates saturating near 1.0 g. These findings underscore the engineering importance of incorporating mainshock–aftershock spectral interaction in seismic damage and risk modeling, offering a transparent and transferable tool for future seismic resilience assessments. Full article

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone. Full article

This paper presents an advanced seismic performance evaluation of reinforced concrete (RC) seismically isolated frame structures under the conditions of rare earthquakes. By employing an elastic–plastic analysis in conjunction with a nonlinear multi-degree-of-freedom model, this study innovatively assesses the incremental dynamic vulnerability of isolated structures. A novel equivalent linearization method is introduced for both single- and two-degree-of-freedom isolation structures, providing a simplified yet accurate means of predicting seismic responses. The reliability of the modified Takeda hysteretic model is verified through comparative analysis with experimental data, providing a solid foundation for the research. Furthermore, a multi-degree-of-freedom shear model is employed for rapid elastic–plastic analysis, validated against finite element software, resulting in an impressive 85% reduction in computation time while maintaining high accuracy. The fragility analysis reveals the staggered upward trend in the vulnerability of the upper structure and isolation layer, highlighting the importance of comprehensive damage control to enhance overall seismic performance. Full article

Accurate seismic performance assessment of flexure-shear critical reinforced concrete (RC) columns necessitates precise hysteresis modeling that captures their distinct cyclic characteristics—particularly pronounced strength degradation, stiffness deterioration, and pinching effects. However, existing hysteresis models for such circular RC columns fail to comprehensively characterize these coupled cyclic degradation mechanisms under repeated loading. This study develops a novel hysteresis model explicitly incorporating three key mechanisms: (1) directionally asymmetric strength degradation weighted by hysteretic energy, (2) cycle-dependent pinching governed by damage accumulation paths, and (3) amplitude-driven stiffness degradation decoupled from cycle count, calibrated and validated using 14 column tests from the Pacific Earthquake Engineering Research Center (PEER) structural performance database. Key findings reveal that significant strength degradation primarily manifests during initial loading cycles but subsequently stabilizes. Unloading stiffness degradation demonstrates negligible dependency on cycle number. Pinching effects progressively intensify with cyclic advancement. The model provides a physically rigorous framework for simulating seismic deterioration, significantly improving flexure-shear failure prediction accuracy, while parametric analysis confirms its potential adaptability beyond tested scenarios. However, applicability remains confined to specific parameter ranges with reliability decreasing near boundaries due to sparse data. Deliberate database expansion for edge cases is essential for broader generalization. Full article

This paper aims to investigate the feasibility of machine learning methods for the vulnerability assessment of buildings and structures. Traditionally, the seismic performance of buildings and structures is determined through a non-linear time–history analysis, which is an accurate but time-consuming process. As an alternative, structural responses of buildings under earthquakes can be obtained using well-trained machine learning models. In the current study, machine learning models for the damage classification of RC buildings are developed using the datasets generated from numerous incremental dynamic analyses. A variety of earthquake and structural parameters are considered as input parameters, while damage levels based on the maximum inter-story drift ratio are selected as the output. The performance and effectiveness of several machine learning algorithms, including ensemble methods and artificial neural networks, are investigated. The importance of different input parameters is studied. The results reveal that well-prepared machine learning models are also capable of predicting damage levels with an adequate level of accuracy and minimal computational effort. In this study, the XGBoost method generally outperforms the other algorithms, with the highest accuracy and generalizability. Simplified prediction models are also developed for preliminary estimation using the selected input parameters for practical usage. Full article

This paper evaluates the time-dependent fragility and post-earthquake residual seismic performance of existing steel frame columns in offshore atmospheric environments. Based on experimental research, the seismic failure mechanism and deterioration laws of the seismic behavior of corroded steel frame columns were revealed. A finite element analysis (FEA) method for steel frame columns, which considers corrosion damage and ductile metal damage criteria, is developed and validated. A parametric analysis in terms of service age and design parameters is conducted. Considering the impact of environmental erosion and aging, a classification criterion for damage states for existing steel frame columns is proposed, and the theoretical characterization of each damage state is provided based on the moment-rotation skeleton curves. Based on the test and numerical analysis results, probability distributions of the fragility function parameters (median and logarithmic standard deviation) are constructed. The evolution laws of the fragility parameters with increasing service age under each damage state are determined, and a time-dependent fragility model for existing steel frame columns in offshore atmospheric environments is presented through regression analysis. At a drift ratio of 4%, the probability of complete damage to columns with 40, 50, 60, and 70-year service ages increased by 18.1%, 45.3%, 79.2%, and 124.5%, respectively, compared with columns within a 30-year service age. Based on the developed FEA models and the damage class of existing columns, the influence of characteristic variables (service age, design parameters, and damage level) on the residual seismic capacity of earthquake-damaged columns, namely the seismic resistance that can be maintained even after suffering earthquake damage, is revealed. Using the particle swarm optimization back-propagation neural network (PSO-BPNN) model, nonlinear mapping relationships between the characteristic variables and residual seismic capacity are constructed, thereby proposing a residual seismic performance evaluation model for existing multi-aged steel frame columns in an offshore atmospheric environment. Combined with the damage probability matrix of the time-dependent fragility, the expected values of the residual seismic capacity of existing multi-aged steel frame columns at a given drift ratio are obtained directly in a probabilistic sense. The results of this study lay the foundation for resistance to sequential earthquakes and post-earthquake functional recovery and reconstruction, and provide theoretical support for the full life-cycle seismic resilience assessment of existing steel structures in earthquake-prone areas. Full article

Given the critical role of electrical cabinets in the post-earthquake recovery and emergency response of nuclear power plants (NPPs), a comprehensive assessment of their seismic performance is essential to ensure operational safety. This study analyzed seismic behavior by fabricating an electrical cabinet model based on the dynamic characteristics and field surveys of equipment installed in a Korean-type NPP. A shaking table test with simultaneous tri-axial excitation was conducted, incrementally increasing the seismic motion until damage was observed. A numerical model was then developed based on the experimental results, followed by a seismic response analysis and comparison of results. The findings verified that assuming fixed anchorage conditions in the numerical model may significantly overestimate seismic performance, as it fails to account for the nonlinear behavior of the anchorage system, as well as the superposition between global and local modes caused by cabinet rocking and impact under strong seismic loading. Furthermore, damage and impact at the anchorage amplified acceleration responses, significantly affecting the high-frequency range and the vertical behavior, leading to substantial amplification of the in-cabinet response spectrum. Full article

Urban water supply pipelines play a critical role in ensuring the continuous delivery of water, and their failure during earthquakes can result in significant societal disruptions. This study proposes a seismic damage prediction method for urban buried water supply pipelines affected by seismic wave propagation, grounded in empirical data from past earthquake events. The method integrates key influencing factors, including pipeline material, diameter, joint type, age, and soil corrosivity. To enhance its practical applicability and address the challenge of quantifying soil corrosivity, a simplified classification approach is introduced. The proposed model is validated using observed pipeline damage data from the 2008 Wenchuan earthquake, with predicted results showing relatively good agreement with actual failure patterns, thereby demonstrating the model’s reliability for seismic risk assessment. Furthermore, the model is applied to assess potential earthquake-induced damage to buried pipelines in the city center of Ganzhou, and the corresponding results are presented. The findings support earthquake risk mitigation and the protection of urban infrastructure, while also providing valuable guidance for the replacement of aging pipelines and the enhancement of urban disaster resilience. Full article

Background and Objectives: On 6 February 2023, two catastrophic earthquakes struck southeastern Türkiye, affecting over 13 million individuals and causing widespread destruction. While the physical damage was immediate, the psychological consequences—particularly posttraumatic stress disorder (PTSD) and depression—have proven long-lasting. This study aimed to evaluate the severity and course of PTSD symptoms among survivors and to examine the effectiveness of a telepsychiatry-based mental health intervention in a post-disaster setting. Materials and Methods: This naturalistic, observational study included 153 adult participants from the affected regions who underwent at least two telepsychiatry sessions between the first and sixth month post-disaster. Initial screening was conducted using the General Health Questionnaire (GHQ-12), and individuals scoring ≥ 13 were further assessed with the PTSD Checklist—Civilian Version (PCL-C) and the Beck Depression Inventory (BDI). Follow-up evaluations and pharmacological or psychoeducational interventions were offered as clinically indicated. Results: At the one-month follow-up, 94.4% of participants met the threshold for PTSD symptoms (PCL-C > 22) and 77.6% had severe depressive symptoms (BDI > 30). By the sixth month, PTSD symptoms had significantly decreased (mean PCL-C score reduced from 42.47 ± 12.22 to 33.02 ± 12.23, p < 0.001). Greater symptom reduction was associated with higher educational attainment and perceived social support, while prior trauma predicted poorer outcomes. Depression severity emerged as the strongest predictor of chronic PTSD. Conclusions: This study highlights the psychological burden following the 2023 earthquakes in Türkiye and demonstrates the feasibility and potential effectiveness of telepsychiatry in disaster mental health care. Integrating digital mental health services into disaster response systems may help reach vulnerable populations and improve long-term psychological recovery. Full article

The structural reliability of bottom-fixed offshore wind turbines is generally influenced by the dispersion of and variability in soil properties, which affect their ultimate capacity, serviceability, and both the short- and long-term fatigue. During an earthquake, the soil–pile system is subjected to intense cyclic loads that can lead to stiffness and strength degradation, typically captured through cyclic soil models. Calibration of soil parameter variability is fundamental for reliable structural assessments of wind turbine integrity. In this study, a method to generate randomness of the parameters affecting cyclic soil degradation models is proposed. Fatigue parameters are quantified through random cyclic undrained triaxial tests conducted using the Discrete Element Method. Deterministic simulations are first performed based on experimental results from the Liquefaction Experiments and Analysis Project for validation. Subsequently, variability in the initial particle size distribution functions is introduced to generate random soil samples, and triaxial simulations are repeated to quantify the dispersion of soil fatigue parameters. The proposed procedure is then applied through Monte Carlo simulations on the IEA 15-MW reference wind turbine, which is subjected to both short- and long-duration earthquakes. The results demonstrate the significant impact of soil degradation on the bending moment envelope, as well as the effect of soil uncertainty on tower fatigue, assessed using the damage equivalent load approach. Full article