Search Results (23)

The analysis of earthquake-induced damage scenarios at the urban scale is a fundamental tool for seismic risk assessment and mitigation and the management of urbanized areas exposed to seismic hazards. This paper presents a methodology for simulating earthquake damage scenarios over large urban territories that explicitly accounts for the cumulative effects of seismic sequences. The proposed approach models the progressive accumulation of structural damage and the resulting evolution of building vulnerability under repeated seismic loading. From a complex systems perspective, the methodology describes urban areas as collections of buildings whose vulnerability evolves through memory-dependent processes. Under this framework, the final damage scenario emerges from the cumulative effects of the entire seismic history rather than from the contribution of individual earthquakes considered in isolation. The study extends previous work by the authors, in which instrumentally derived macroseismic intensity maps were integrated with observed building damage data from the 2009 L’Aquila seismic sequence. The results demonstrated that the methodology could successfully reproduce the spatial distribution of observed damage when considering not only the mainshock but also all seismic events exceeding a selected magnitude threshold. In this contribution, new developments of the calibration procedure are presented, together with applications to the 2013 Garfagnana-Lunigiana and the 2016–2017 Central Italy seismic sequences. Through a comparative analysis of these case studies, the influence of different seismic sequence characteristics and building stock features on damage evolution is investigated. The results provide further insight into the capabilities and limitations of the proposed methodology, highlighting its potential as a tool for interpreting post-earthquake damage patterns and supporting seismic risk assessment and mitigation strategies. Full article

In large-scale seismic scenarios, the operational continuity of evacuation shelters is frequently compromised by limited governmental capacity, necessitating the strategic integration of Disaster Relief Volunteers (DRVs). Traditional assessments often rely on coarse regional aggregates, overlooking the critical spatial mismatch between volunteer availability and localized demand. In this study, a high-resolution, spatially explicit framework was developed to evaluate urban operational resilience in New Taipei City. Utilizing the TERIA platform, a nocturnal magnitude 6.8 Shanjiao Fault rupture was simulated, identifying that approximately 460,000 individuals would be affected, with shelter demand reaching 300,000—double the current capacity. Service areas were delineated using ArcGIS Network Analyst (Dijkstra’s algorithm) based on an 800 m walking threshold, further refined by a secondary assignment rule to redistribute “shadow demand” from peripheral populations. Quantitative analysis of the newly introduced “DRV shortfall” metric reveals that 74% of shelters (148/200) face concurrent spatial and manpower saturation. Notably, 42 analysis units lack resident volunteers entirely, with the most severe shortfall magnitude reaching 361 DRVs at a single “high-risk convergence node”. These results uncover a profound deficiency in urban disaster resilience driven by significant spatial mismatch. This research contributes a three-fold advancement: (i) the high-resolution coupling of volunteer residential data with dynamic demand patterns; (ii) the formalization of the DRV shortfall as a standardized metric for resource adequacy; and (iii) the formulation of a strategic policy framework for multi-shelter activation sequencing, “Support Hub” designation, and resource synchronization in hyper-dense urban environments. Full article

Accurate landslide identification is crucial for enhancing emergency response capabilities during destructive geological hazards. Although deep-learning-based semantic segmentation has demonstrated effectiveness, substantial variations in landslide scales and environmental similarities continue to challenge existing methods. This paper systematically constructs a new co-seismic landslide dataset for the Yarlung Zangbo River basin based on the 2017 Nyingchi earthquake, effectively filling a critical regional data gap. This paper proposes CETransUNet (coordinate attention and edge-guided attention transformer UNet), a novel landslide detection model that integrates ResNet and Transformer architectures. Specifically, a coordinate attention (CA) module is introduced within the skip connections between the encoder and decoder. This module encodes positional information along both horizontal and vertical spatial directions and dynamically re-weights the feature maps, thereby effectively suppressing background noise caused by semantic gaps and enhancing the model’s ability to localize landslide regions. Additionally, an edge-guided attention (EGA) module is incorporated into the decoder. This module extracts explicit edge priors from the input image using a Laplacian operator and imposes geometric constraints on the predictions via a boundary reverse attention mechanism, thereby significantly alleviating boundary ambiguity and morphological distortion of landslides. Evaluations across datasets from the Yarlung Zangbo River, Iburi-Tobu, and Bijie regions demonstrate that CETransUNet significantly outperforms state-of-the-art models—including TransUNet, SegFormer, and SwinUNet—in terms of IoU, MIoU, and F1-score. Overall, through the synergistic optimization of the coordinate attention and edge-guided attention modules, the CETransUNet model achieves synchronous enhancement of boundary integrity and geometric precision in complex scenarios, providing a reliable technical solution for large-scale intelligent landslide identification. Full article

We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as a computational cost mitigation approach. Given modern seismic acquisition systems, the data (as an input for FWI) required for an industrial-strength case is in the teraflop level of storage; therefore, solving complex subsurface cases or exploring multiple scenarios with FWI becomes prohibitive. The proposed method utilizes a deep neural network with a binarized sensing layer that learns by compressed learning seismic acquisition layouts from a large corpus of subsurface models. Thus, given a large seismic data set to invert, the trained network selects a smaller subset of the data, then by using representation learning, an autoencoder computes latent representations of the shot gathers, followed by K-means clustering of the latent representations to further select the most relevant shot gathers for FWI. This approach can effectively be seen as a hierarchical selection. The proposed approach consistently outperforms random data sampling, even when utilizing only 10% of the data for 2D FWI, and these results pave the way to accelerating FWI in large scale 3D inversion. Full article

Based on the dual requirements of building energy efficiency and construction industrialization, along with the development of high-strength, high thermal resistance (low thermal conductivity) foamed concrete (HLFC), this study proposes a new prefabricated high-strength foamed concrete thermal self-insulating shear wall system (called HFSW shear wall) suitable for multi-story buildings, which could address the core shortcomings of existing organic insulation materials in buildings, such as poor fire resistance and short life cycles. Concerning the research gap in the flexural performance of this wall type, this study conducted seismic tests on two full-scale wall models and systematically analyzed the fundamental performance parameters under quasi-static loading, including bending failure phenomena, load-bearing capacity, stiffness degradation, energy dissipation capacity, and ductility. The results show that HFSW walls with large shear span ratios generally exhibit typical bending failure characteristics. However, due to the relatively low material strength, extensive development of shear and flexural–shear cracks occurs, leading to minimal differences in typical seismic performance indicators compared to shear-dominated failure scenarios in traditional shear walls (indicating significant flexural–shear coupling effects). Finally, a finite element model was used to simulate the wall capacity under various parameters, including axial compression ratio, wall thickness, and longitudinal reinforcement in edge columns. Based on the validated and calibrated finite element results, and in accordance with the wall failure mode as well as the load transfer mechanism, a calculation model for the flexural strength of HFSW shear walls was established to guide design and engineering application, achieving a theoretical calculation accuracy of 0.97. The research findings provide meaningful guidance for the design and application of this wall system. Full article

Simulation of damage scenarios is an important tool for seismic risk mitigation. While a detailed analysis of each building would be preferable to assess their vulnerability to seismic hazard, simplified yet robust methodologies are necessary at a large urban scale to overcome computational costs or data unavailability. Moreover, most damage assessments simulate single seismic shocks, though in many real sequences, with a series of aftershocks following the mainshocks, it is observed that buildings endure damage accumulation, which increases their vulnerability over time. The present study builds on a recently developed methodology for simulating urban-scale damage scenarios across seismic sequences, explicitly accounting for damage accumulation and the evolution of vulnerability. In particular, the availability of a dataset reporting the damage observed in the L’Aquila area (Italy) during the severe earthquake sequence of 2009, in combination with the georeferenced maps representing the spatial distribution of the ground motion, allows for the calibration of the methodology through the comparison between the simulations’ results and the sequence’s real data. Although calibrated on the L’Aquila dataset, the proposed procedure could also be applied to different urban areas, with both real and synthetic seismic sequences, enabling the forecasting of damage scenarios to support the development of effective strategies for seismic risk mitigation. Full article

Multiple uncertainties in traffic demand fluctuations and infrastructure vulnerability during seismic events pose significant challenges for the resilience assessment of highway transportation networks (HTNs). While Monte Carlo simulation remains the dominant approach for uncertainty propagation, its high computational cost limits its scalability, particularly in metropolitan-scale networks. This study proposes an EQResNet framework for accelerated post-earthquake resilience assessment of HTNs. The model integrates network topology, interregional traffic demand, and roadway characteristics into a streamlined deep neural network architecture. A comprehensive surrogate modeling strategy is developed to replace conventional traffic simulation modules, including highway status realization, shortest path computation, and traffic flow assignment. Combined with seismic fragility models and recovery functions for regional bridges, the framework captures the dynamic evolution of HTN functionality following seismic events. A multi-dimensional resilience evaluation system is also established to quantify network performance from emergency response and recovery perspectives. A case study on the Sioux Falls network under probabilistic earthquake scenarios demonstrates the effectiveness of the proposed method, achieving 95% prediction accuracy while reducing computational time by 90% compared to traditional numerical simulations. The results highlight the framework’s potential as a scalable, efficient, and reliable tool for large-scale post-disaster transportation system analysis. Full article

Urban disaster risks are escalating, and university campus emergency shelters (UCESs) are key to alleviating the supply–demand imbalance in emergency shelter services (ESSs) within high-density central urban areas. However, existing studies lacked the measurement of UCES service effectiveness from a regional supply–demand perspective, limiting the ability to guide planning practices. Therefore, we focused on the capacity of UCESs to improve regional supply–demand relationships and developed a service effectiveness evaluation framework for UCESs in the central urban area of Tianjin under an earthquake scenario. We identified emergency shelter spaces within the campuses and developed a campus–city collaborative shelter capacity model to determine their service supply capacity. Then we quantified regional service demand driven by seismic risk. Finally, we quantified the service effectiveness of each UCES by constructing a service effectiveness evaluation model. Results showed that (1) the total shelter capacity and service coverage of 13 UCESs accounted for approximately 32.1% of the central district’s population and 67.5% of its land area, indicating their strong potential to provide large-scale ESSs. (2) Average seismic risk values ranged from 0.200 to 0.260, exhibiting the characteristic of being higher in the south and lower in the north. (3) Service effectiveness was classified into three levels—higher (1.150–1.257), medium (0.957–0.988), and lower (0.842–0.932)—corresponding to planning interventions that can be implemented based on them. This study aims to reveal differences between different UCESs to improve regional supply–demand relationships by evaluating their service effectiveness and supporting refined emergency management and planning decisions. Full article

This study investigates the influence of bedrock material conditions on the seismic behavior of the Niedzica earth dam in southern Poland. It examines the dam’s dynamic response to a real seismic event—the 2004 Podhale earthquake—and evaluates how different foundation conditions affect structural performance under spatially varying ground motions. A spatially varying ground motion excitation model was developed, incorporating both wave coherence loss and wave passage effects. Seismic data was collected from three monitoring stations: two located in fractured bedrock beneath the dam and one installed in the surrounding intact Carpathian flysch. From these recordings, two key spatiotemporal parameters were experimentally determined: the seismic wave velocity and the spatial scale parameter (α), which reflects the degree of signal incoherence. For the fractured bedrock beneath the dam, the wave velocity was 2800 m/s and α = 0.43; for the undisturbed flysch, it was 3540 m/s and α = 0.82. A detailed 3D finite element model of the dam was developed in ABAQUS and subjected to time history analyses under three excitation scenarios: (1) uniform input, (2) non-uniform input with coherence loss, and (3) non-uniform input including both coherence loss and wave passage effects. The results show that the dam’s seismic response is highly sensitive to the choice of spatiotemporal parameters. Using generalized values from the flysch reduced predicted shear stresses by up to 16% compared to uniform excitation. However, when the precise parameters for the fractured bedrock were applied, the reductions increased to as much as 24%. This change in response is attributed to the higher incoherence of seismic waves in fractured material, which causes greater desynchronization of ground motion across the dam’s foundation. Even small-scale geological differences—when properly reflected in the spatiotemporal model—can significantly influence seismic safety evaluations of large-scale structures. Ultimately, shifting from regional to site-specific parameters enables a more realistic assessment of dynamic stress distribution. Full article

The paper innovatively studies the impact of dual randomness of structural parameters and seismic excitation on the seismic reliability of highway pile–slab structures using the probability density evolution method. A nonlinear stochastic dynamic model was established through the platform, integrating, for the first time, the randomness of concrete material properties and seismic motion variability. The main findings include the following: Under deterministic seismic input, the displacement angle fluctuation range caused by structural parameter randomness is ±3%, and reliability decreases from 100% to 65.26%. For seismic excitation randomness, compared to structural parameter randomness, reliability at the 3.3% threshold decreases by 7.99%, reaching 92.01%. Dual randomness amplifies the variability of structural response, reducing reliability to 86.38% and 62%, with a maximum difference of 20.5% compared to single-factor scenarios. Compared to the Monte Carlo method, probability density evolution shows significant advantages in computational accuracy and efficiency for large-scale systems, revealing enhanced discreteness and irregularity under combined randomness. This study emphasizes the necessity of addressing dual randomness in seismic design, advancing probabilistic seismic assessment methods for complex engineering systems, thereby aiding the design phase in enhancing facility safety and providing scientific basis for improved design specifications. Full article

Damage assessment methods fall into contact and non-contact approaches. Contact methods, like physical measurements, material sampling, and ultrasonic testing, provide detailed data but are time-consuming and require specialized equipment. In contrast, non-contact methods assess damage remotely, allowing for faster, safer, and large-scale evaluations, especially useful in post-disaster scenarios. However, there are currently no standardized non-contact methods for assessing damage levels in confined masonry walls after damaging seismic events in Peru. On the other hand, an experimental database of cyclic loading tests on confined masonry walls is available, supporting numerical simulations with calibrated mathematical models to estimate damage levels. This research extends the application of this database by analyzing the crack pattern imagery from the tested walls and correlating it with the lateral deformation (drift) to identify the damage levels. A high-accuracy crack measurement technique was developed, combining a convolutional neural network to generate a binary crack mask and a binary search algorithm to extract polylines and convert them into length measurements, achieving a detection accuracy of 78%. The measured crack patterns were normalized into an index, which was then correlated with the amplitude of the lateral deformation in each hysteretic loop. Finally, a relationship was established between drift and the damage level index. These findings contribute to the development of a rapid, non-contact damage assessment method for confined masonry walls in seismic-prone regions. Full article

In early 2024, the largest full-scale exercise (FSE) for earthquakes and related geohazards in Greece was implemented in Crete Island (southern Greece). Crete is characterized by intense seismicity with historical and recent destructive earthquakes with considerable impact on both the natural and built environment and subsequently on the population. The uniqueness of this FSE lies in the creation and coordination of a multi-agency, multijurisdictional, and multidisciplinary environment in which a multitude of central, regional, and local stakeholders and a large percentage of the total population of Crete actively participated. This paper constitutes a descriptive study focusing on the main steps of the exercise management cycle comprising planning, implementation, and evaluation of the FSE. Furthermore, emphasis is given on its purpose and objectives, its main events and subsequent incidents, the participants and their roles, as well as the material developed and distributed to the participants. Through this study, the implemented actions for increasing preparedness of the Civil Protection mechanism in case of earthquakes and related geohazards are highlighted aiming to inform the scientific community and operational staff and to contribute to the seismic risk reduction of regions worldwide with similar seismotectonic and demographic characteristics with Crete. Furthermore, suggestions are made for the integration of multi-hazard episodes in the FSE scenario in order that the Civil Protection authorities will be prepared to handle the synergy of hazards of different types that may arise during a post-earthquake period that create compounding challenges during the emergency response and further increase recovery time. Full article

Many landslides triggered by earthquakes have caused a countless loss of life and property, therefore, it is very important to predict landslide hazards accurately. In this work, regional seismic landslide data were obtained via a field survey, remote sensing interpretation, and data collection, and a multilevel physical and mechanical parameter system for seismic landslide hazard assessment was established; this system included a landslide inventory, loose accumulation layers, and geological units, enabling higher accuracy in the data. The Newmark displacement model with a modified correlation coefficient was used to assess the regional seismic landslide hazard in four scenarios (a = 0.1, 0.2, 0.3, 0.4) to study the influence of the landslide hazard at different peak ground accelerations. Moreover, the information value model was used to modify the calculated results to improve their accuracy in the assessment. By assessing the potential seismic landslide hazard in Shimian County in the upper reaches of the Yangtze River, the regional landslide distribution and pattern at different peak ground accelerations were obtained. The results show that with decreasing parameter accuracy in the system, the importance of the landslide inventory increases. When the peak ground acceleration is a = 0.3, which can be defined as a high hazard grade, in which the landslide area demonstrates a large-scale sharp increase, a devastating hazard threshold is reached. As the peak ground acceleration increases, the factor controlling landslides transforms from the landslide inventory to the slope, which reflects the reasonableness of the parameters in the system. The input parameters were regarded as important factors for efficiently increasing the accuracy of the results of the Newmark displacement model in the discussion. Full article

Most machine learning (ML) studies on predicting co-seismic landslides have relied on Peak Ground Acceleration (PGA). The PGA of the ground strongly correlates with the relative position and azimuth of the seismogenic faults. Using the co-seismic landslide records of the 2008 Wenchuan earthquake, we show that the ML model using the distances and azimuths from the epicenter to sites performs better than the PGA model regarding accuracy and actual prediction results. The distances and azimuths are more accessible than the PGA because obtaining accurate and realistic large-scale PGAs is difficult. Considering their computational efficiency and cost-effectiveness, the ML models utilizing distances and azimuths from the epicenter to the sites as inputs could be an alternative to PGA-based models for evaluating the impact of co-seismic landslides. Notably, these models prove advantageous in near-real-time scenarios and settings requiring high spatial resolution, and provide favorable assistance in achieving the goal of sustainable development of society. Full article

As buildings become taller and slenderer, managing their vibrational response and mitigating it pose significant challenges in design. Tuned liquid dampers (TLDs) are liquid (usually water)-filled tanks that can mitigate structural vibrations by leveraging the sloshing motion of the contained fluid. However, the dynamic behavior of TLDs and their interaction with structures is complex. While most research on TLDs has focused on mitigating wind-induced vibrations, less attention has been paid to their seismic control of structural responses. Moreover, existing literature on the experimental research involving TLDs mostly pertains to small-scale models. This study aims to experimentally explore the effectiveness of large-scale TLDs in mitigating vibrations in both linear and nonlinear structures under seismic loads. A real-time hybrid simulation is employed as the experimental method, where only the TLD is physically constructed and tested, while the rest of the system is simulated numerically in a coupled manner, allowing for obtaining the dynamic response of the structure equipped with the TLD in real time. This approach offers the flexibility to significantly scale up the TLD size for physical testing while exploring various TLD-structure scenarios by numerically adjusting the structural properties within the simulation. Full article