Bulletin of Earthquake Engineering最新文献

Albania, located in the seismically active Balkan region, has a significant stock of old masonry buildings still in use for residential and public purposes, making them highly vulnerable to seismic events. This study presents a quantitative assessment of the seismic vulnerability of these structures using a hybrid methodology that integrates mechanics-based numerical modeling with empirically derived damage states. Structural models of seventeen representative building typologies are prepared via the macro-element approach, with material properties calibrated from an extensive program of experimental tests. Nonlinear static (pushover) analyses are then performed to generate fragility curves. The results reveal a stark contrast in vulnerability across different construction eras. Pre-1963 buildings (Typology A) exhibit high fragility, with median spectral displacement for complete damage as low as 0.38 cm, whereas post-1978 buildings (Typology C), designed under modern seismic codes, demonstrate significantly higher capacity, with damage thresholds reaching up to 4.39 cm. A key finding is the pronounced directional vulnerability in older typologies, which is substantially mitigated in modern designs. The derived fragility curves provide critical, quantitative insights into the seismic vulnerability of Albania’s masonry building stock, offering a robust basis for risk assessment and prioritizing retrofitting strategies.

This study proposes a framework for the rapid assessment of seismic fragility and risk of reinforced concrete (RC) circular bridge piers affected by corrosion. The methodology integrates a novel computer vision (CV) algorithm to enhance visual inspections for corrosion level identification, combined with a probabilistic approach to seismic fragility analysis. The aim of the methodology is to quantify the impact of corrosion-induced deterioration on structural performance, expressed as an increment in terms of seismic risk. The first part of the framework consists of defining a custom convolutional neural network able to automatically predict the corrosion severity class starting from a metric-photographic survey. The proposed network incorporates attention mechanisms and color space transformations to ensure robust performance under varying image conditions. The output is used within a probabilistic-based structural modelling and analysis framework, which allows to derive seismic performance of the considered bridge pier typology. On the modelling side, a specific fiber-based approach was employed, in order to account for non-uniform cross-sectional corrosion and current deterioration condition. The results are returned in terms of seismic fragility and risk metrics for quantifying the reduction of seismic performance with respect to the initial conditions. The framework was tested on a real-life case-study exhibiting non-uniform cross-sectional base corrosion, and subsequently, additional scenarios considering full-section base corrosion at varying severity levels were investigated. The outcomes of this study demonstrate the potentialities of artificial intelligence in improving the current practices in the field of seismic assessment of aging RC infrastructures.

Damage to masonry infill walls can result in substantial property loss and pose a serious risk to human safety. The behaviour of masonry walls under seismic load and the corresponding consequences must be effectively characterized for reliable damage estimation. Some drift-based fragility functions have been proposed based on experimental datasets in literature; however, the high dispersion and inconsistencies of findings hinder their reliability in damage assessment. Therefore, this paper aims to derive a reliable fragility curve for reinforced concrete infilled frames using numerical analysis. The analysis models were designed based on different infill parameters, including type of panel, infill strength, wall thickness, and aspect ratio. The cracking and crushing of concrete and masonry were simulated using the total strain crack model. The findings were validated against several experimental studies in the literature. A new damage state definition based on visible cracks ratio was proposed. The results indicate that damage capacity, crack pattern, and failure mode are strongly related to wall properties. It was also observed that infill strength and the presence of opening have a significant influence on the fragility of masonry infill.

In a recent work, we tested the ability to compute earthquake parameters (location and magnitude) using citizen testimonies collected by the European-Mediterranean Seismological Centre (EMSC). Each intensity estimated by individual non-professional users of the LastQuake smartphone application is indicated as an individual data point (IDP). Each IDP is archived by EMSC with a time stamp, allowing the calculation of the time delay from the earthquake origin time. To use IDPs as classic intensities, i.e. macroseismic data points (MDPs), identifying damage at the scale of towns or cities, they must be grouped into spatial clusters, which are then processed by the BOXER code to locate and size global earthquakes. A retrospective analysis on a dataset of more than 15,000 events collected over the past 10 years shows that the procedure can provide reliable parameters and that the results depend on the geographical area and improve over time and as the number of available IDPs/MDPs increases. The key question is whether early IDPs/MDPs can quickly provide reliable parameters (location and magnitude) for users and stakeholders (e.g. the civil protection agencies). Using clustering methods that statistically provide, on average, the best agreement with instrumental data, we tested some predefined time intervals within which to group the available IDPs into MDPs. We then applied the BOXER code to these MDPs, evaluating the agreement with the final instrumental parameters. Results confirm that reliability increases with the number and distribution of MDPs, strictly dependent on the number and distribution of available IDPs. This retrospective analysis demonstrates the effectiveness of the approach and its potential to quickly provide parameters for future real-time applications. The method may offer a reliable and rapid tool to support emergency response, improving as more IDPs/MDPs are collected.

Seismic vulnerability modelling requires methodologies that account for changes in design practices over time and the inherent variability within building portfolios, including the differences in geometry, materials, and construction quality. Conventional models use different assessment approaches, classification systems, and representations of seismic loading and are often developed using a limited number of archetypal structural models to characterise an entire building class. As a result, these models tend to oversimplify individual building response, often fail to reflect building-to-building variability adequately, and do not account for multiple sources of uncertainty. To overcome these limitations, a collaborative and unified simulated design (SimDesign) framework for buildings has recently been introduced under the Built Environment Data (BED) initiative alongside an open-source Python implementation. Following the simulated design process, the framework generates numerical models in OpenSees for non-linear analyses, facilitating the development of vulnerability models for RC buildings. Leveraging its collaborative nature, this article presents the first country-specific extension of the framework for reinforced concrete (RC) frame buildings in Türkiye. More specifically, the historical and modern Turkish seismic design regulations are examined in detail, and specific design rules are integrated along with available statistical data on construction practices. Example applications were also conducted to assess the structural capacities associated with each implemented Turkish design class through non-linear pushover and dynamic analyses. The analysis outcomes revealed a consistent improvement in lateral force and ductility capacity over time, closely aligned with progressive enhancements in seismic code provisions and construction practices. Ultimately, this work has the potential to support more accurate seismic vulnerability modelling, which improves risk assessments and aids effective mitigation strategies for enhanced disaster resilience in the country.

The phenomenon of soil liquefaction poses a considerable geotechnical challenge, profoundly impacting the structural behavior of bridges following seismic occurrences. This study delves into the repercussions of soil liquefaction on integral abutment bridges (IAB) subjected to both near-fault and far-fault ground motions (GM). The analyses, utilizing the OpenSees computational framework, incorporated p-y springs to model the effects of soil liquefaction. Nonlinear time history analyses were conducted on a series of enhanced Winkler based models of single-span IABs exposed to 10 recorded near-fault and far-fault GMs. Centrifuge experiments proved the model’s capacity to assess pile foundations under seismic loads. The results indicated that liquefaction increased the maximum displacement and permanent displacement of abutments and piles by over 90% and decreased the maximum pile bending moment by over 60%, compared to IABs in non-liquefied soil. The distribution patterns of the bending moment and displacement along the pile depth changed, and the pile bottom constraints (fixed or pinned) significantly impacted the pile responses compared to IABs in non-liquefied soil. The response of abutments and piles increased under near-fault ground motions for IABs in non-liquefied soil, but the effects became less obvious for IABs in liquefied soil. Therefore, when the IAB is located in liquefied soil, it is advised to assume pinned constraints for the pile bottoms, which is more conservative, and far-fault GMs may pose greater challenges than those originating from near-fault GMs.

The quantitative relationship and underlying mechanism between earthquake-induced porewater pressure (PWP) changes and ground motion characteristics remains an unresolved issue in earthquake engineering. This study aims to quantify this relationship by analyzing downhole array data from two sites, which includes ground motion measurements and high sampling rate PWP records. Both time-domain and frequency-domain analyses were conducted to characterize PWP oscillation under various shaking conditions. The results revealed a strong correlation between the PWP fluctuation and vertical ground motion in terms of their amplitudes and frequency contents. This is because vertical motions, mainly composed of compression waves, induce instantaneous contraction or dilation in soil. Consequently, two analytical models grounded in wave propagation and poroelastic theory, and validated through field observations, were proposed to estimate peak PWP changes using ground motion parameters.

Contemporary pavement analysis research seeks to enhance previous methodologies by applying an intricate 3D Finite Element Method (FEM) model within a multi-physics framework. The primary aim is to more precisely depict soil behaviour utilizing Pasternak foundations, which enhance the basic Winkler models. This methodology facilitates an exhaustive examination of the dynamic variables influencing jointed reinforced concrete pavement (JRCP) at airports subjected to multidirectional seismic stresses. The reliability of these FEM JRCP models is confirmed by comparison with previous research and a mesh convergence analysis. Upon Validation, the model evaluates the influence of the sub-base modulus, subgrade modulus, and concrete grade of the pavement slab on its dynamic performance. This analysis utilizes seismic data from the Uttarkashi earthquake (magnitude M 6.6) as the input for ground motion modelling. A statistical sensitivity analysis was used to establish generic design principles for airport JRCP pavements. The research indicated that increased concrete stiffness results in enhanced transverse displacement of the pavement during seismic stress. Moreover, superior-grade concrete can withstand elevated Von Mises stresses before fracturing. A more substantial sub-base was determined to reduce transverse displacement, thereby enhancing lateral support during seismic events. Ultimately, augmenting the subgrade stiffness was demonstrated to reduce deflections and to improve load distribution, hence diminishing the Von Mises stress in the concrete slab.

This study proposes a comprehensive data-driven framework for quantifying seismic fragility and performance degradation in historical masonry pagodas. Leveraging shaking table tests on 1:8 scale models of the Small Wild Goose Pagoda in both intact and damage-inclined states, a high-resolution dataset ( > 1000 samples) was constructed, capturing key seismic responses—peak acceleration, displacement, and interstory drift—under multidirectional and multi-intensity ground motions (PGA: 0·15 g–0·60 g). A multi-model machine learning pipeline combining Random Forest, XGBoost achieved high predictive accuracy (R² > 0.90), while SHAP-based interpretability analysis identified ground motion intensity, story height, and damage state as dominant predictors. Fragility curves revealed a pronounced vulnerability increase for the damaged model, with a leftward shift in exceedance probabilities. A multi-task CatBoost model with split-conformal calibration provided reliable uncertainty-aware predictions, supporting risk-informed decision-making. Further, a binary classifier (AUC = 0.928) enabled effective post-earthquake condition identification, while response ratio analysis quantified residual capacity loss, particularly in interstory drift. The proposed interpretable and uncertainty-quantified framework advances seismic risk assessment and resilience planning for heritage masonry structures.

Column-supported grain silos are vital for food security but are notoriously vulnerable to earthquakes. A robust framework for assessing their seismic risk is lacking, especially one that accounts for the coupled effects of inter-silo interaction in group configurations, stored material dynamics, and the distinct hazard of pulse-like near-fault ground motions. To address this, we developed a high-fidelity finite element modeling approach, with its dynamic characteristics rigorously validated against shaking table experiments and simplified analytical models. A large-scale probabilistic vulnerability assessment was then performed using Incremental Dynamic Analysis (IDA), with the elastic-plastic column drift serving as the primary damage indicator. Our findings establish that ground motion pulsatility and storage level are the primary drivers of seismic risk. Pulse-like motions consistently increase failure probability; at a design-level PGA of 0·4 g, the collapse risk for a fully loaded row silo rises from 46% (non-pulse) to 52% (pulse). The fully loaded state is unequivocally the most hazardous condition due to amplified inertial forces and dynamic material pressures. Interestingly, the interconnected nature of row silos presents a state-dependent trade-off, offering marginal performance benefits when empty but exacerbating vulnerability compared to single silos when fully loaded. This research provides a new systematic fragility comparison for single versus group silos, delivering validated models for performance-based design and underscoring the urgent need to revise seismic codes to explicitly account for the severe, quantifiable threats posed by near-fault pulses and operational storage levels.