Search Results (344)

Building damage and collapse are the main sources of serious casualties and financial losses during earthquakes, which are among the most destructive natural disasters that endanger human life and property. Therefore, quick and precise post-earthquake building damage assessment is essential for risk assessment and emergency action. Convolutional neural networks (CNNs) primarily concentrate on local features and frequently ignore global contextual information within and across buildings, despite the fact that deep learning-based techniques allow automated damage identification. Transformer-based approaches, on the other hand, are good at capturing global dependencies, but their large memory and processing costs restrict their usefulness. As a result, existing networks still struggle to achieve an effective balance between accuracy and efficiency. To address this issue, this study proposes a lightweight and efficient network for post-earthquake building damage assessment. Specifically, we develop a two-stage method based on EfficientViM with an encoder–decoder architecture. In the encoder, Mamba is introduced to extract multi-scale change features with long-range dependencies, leveraging the state space model to preserve global modeling capability while significantly reducing computational complexity. In the decoder, two lightweight modules are designed to further enhance discriminative capability and computational efficiency. The network finally outputs building localization and pixel-level building damage, respectively. Experiments were conducted on four earthquake events from the BRIGHT dataset using a three-for-training and one-for-testing cross-event rotation evaluation strategy. The results demonstrate that LEViM-Net requires only 30.94 M parameters and 27.10 G FLOPs. In addition, for the Türkiye earthquake event, the proposed method achieves an F1 score of 80.49%, an overall accuracy (OA) of 88.17%, and a mean intersection over union (mIoU) of 49.73%. The proposed model enables efficient remote-sensing-based mapping of macroscopic and image-visible building damage, providing timely support for early-stage emergency response. Full article

Conventional structural design frameworks assess natural hazards as statistically independent phenomena, a practice that can lead to significant underestimation of risk for structures subjected to sequential or concurrent hazards. The generation of probabilistic fragility functions under such cascading loads, particularly for post-fire seismic events, presents a computational barrier for standard non-linear dynamic analysis. To address this barrier, this study introduces a comprehensive computational framework centered on a physics-constrained neural network (PCNN) to serve as a high-fidelity surrogate model. The framework first uses a non-linear 12-degree-of-freedom structural model to generate a baseline dataset of collapse times under post-fire, concurrent wind-earthquake loading via the computationally efficient endurance time (ET) method, confirming that wind effects are negligible under ambient conditions and that the framework correctly identifies this hazard hierarchy without prior labeling, while fire and seismic parameters dominate. This dataset is subsequently used to train the PCNN, which is validated to achieve exceptional predictive accuracy ($R^2=$ 0.991), performing on par with a state-of-the-art Random Forest model while enforcing physical constraints. A feature importance analysis confirmed that structural collapse is dominated by fire intensity (≈55%) and initial structural period (≈45%). The validated PCNN is then applied to demonstrate the framework’s capability, rapidly generating fragility curves that quantify the catastrophic effect of fire on seismic resilience. This analysis reveals that a severe 800 °C localized fire reduces the structure’s median collapse capacity by 94.7%, thereby establishing the proposed framework as a successful template for tackling complex, non-linear problems in multi-hazard engineering. Full article

Existing structural health monitoring of LNG (liquefied natural gas) liquid storage tanks is strictly constrained by explosion-proof safety and engineering conditions, making it impractical to achieve full-domain coverage through dense sensor deployment. How to achieve effective coverage of structural seismic weak parts under limited measuring point conditions is the core issue for monitoring scheme optimization. This paper takes a practical large full-containment LNG storage tank project as the research object and proposes a targeted sensor deployment method based on finite element seismic response analysis: identifying structural seismic weak parts through refined finite element modeling and seismic response analysis, thereby achieving coverage of critical regions and improved monitoring efficiency under limited sensor constraints. The research approach is as follows: a finite element model of the LNG storage tank is established using ADINA software and verified through modal analysis combined with on-site ambient vibration testing, ensuring the accuracy and engineering applicability of numerical simulation. Typical seismic records including El Centro, Tangshan, and TAFT are selected, and seismic response analysis of the tank is carried out, clarifying the displacement response laws under different seismic waves and identifying the junctions of dome roof and tank wall, buttress columns and tank wall, and the upper and local areas of the tank wall as structural seismic weak parts. Based on the characteristics of these parts and on-site explosion-proof conditions, a four-measuring-point targeted monitoring sensor deployment scheme is formulated and applied in engineering. This research constructs a structural health monitoring method for LNG storage tanks featuring “structural model verification–weak part identification–monitoring scheme customization,” providing a new approach for tank monitoring under explosion-proof safety constraints and partially addressing the limitations of traditional empirical deployment methods. This study establishes a technical path covering the full cycle of routine operation, seismic response, and post-earthquake assessment, providing methodological support for the structural health monitoring of LNG storage tanks, and its core concepts can also serve as a reference for the structural health monitoring of similar large-scale thin-walled storage tanks. Full article

Regional coseismic landslide hazard assessment is important for disaster risk reduction and sustainable development in seismically active mountainous regions. Existing Newmark displacement prediction models exhibit systematic bias when applied to Southwest China due to the region’s distinctive seismotectonic and topographic characteristics. This study addresses this limitation by systematically evaluating and recalibrating seven established models using 591 horizontal strong-motion records from nine significant regional earthquakes (2007–2022). Among the recalibrated versions, the Yiğit2020 framework performed best but showed potential for further improvement. Analysis revealed a stable log-linear correlation between peak ground velocity (PGV) and Newmark displacement, with an average of 0.78 under different critical acceleration levels. By incorporating a log PGV term, a new model was developed, achieving improved performance with an R² of 0.92 and a standard deviation (σ) of 0.30. Validation results further showed that the new model reduced the mean relative error from 74.22% to 66.43% and the median relative error from 53.83% to 38.90%, compared with the recalibrated Yiğit2020 model. In a case study of the 2022 Luding Ms 6.8 earthquake, the proposed model yielded the highest landslide discrimination capability (AUC = 0.687), outperforming other models (AUC = 0.600–0.636). These results support more reliable regional hazard zoning and rapid post-earthquake risk identification, thereby contributing to sustainable land-use planning, infrastructure resilience, and disaster risk reduction in seismically active mountainous regions. Full article

Steel–concrete composite components exhibit significant advantages, including reliable mechanical performance, rapid construction, cost efficiency, and low environmental impact. Existing studies on their seismic behavior have mainly focused on developing novel connection forms and enhancing joint zone strength, while systematic investigations into the post-earthquake axial compression behavior and failure mechanisms of composite joints remain limited. To address this gap, this study investigates the mechanical performance of steel–concrete composite components under strong seismic and post-earthquake conditions. Seismic damage characteristics are first analyzed based on representative case studies of conventional steel–concrete columns. Subsequently, low-cycle reversed loading tests followed by post-earthquake axial compression tests are conducted on seven beam–column joints with varying damage levels, and the damage evolution and seismic performance of joint zones under different structural configurations are systematically evaluated. In addition, the seismic performance of steel–concrete composite shear walls is further validated. The results provide a scientific basis for the seismic design, post-earthquake assessment, and repair of steel–concrete composite structures. Full article

Induced seismicity is a recognized challenge in hot dry rock (HDR) geothermal development. Based on a systematic review of previous studies on induced seismicity mechanisms, characteristics, risk assessment, and mitigation measures, we compiled publicly available data from 13 HDR projects worldwide. Our statistical analysis shows that felt earthquakes occurred in 62% of the projects, and a post-injection “tailing effect” was observed in 54% of the projects. The spatial influence range of induced seismicity is typically within 2 km of the injection well, and the duration of the “Kaiser effect” varies from months to years depending on local conditions. A cross-site comparison of injection parameters suggests that the maximum wellhead pressure may be a more useful indicator than injected volume for estimating the largest possible earthquake magnitude, especially when comparing different tectonic settings. Furthermore, we examine the applicability and limitations of b-value trends, seismogenic indices, and existing maximum magnitude prediction models in seismic risk assessment. Dynamic adjustment of injection parameters based on real-time risk indicators, combined with safer injection schemes, may represent an important research direction for improving the conventional traffic light system. These findings provide a data-driven basis for site-specific safety management of HDR development. Full article

On 28 March 2025, a Mw 7.7 earthquake struck central Myanmar, where rapid mapping of early impacts is crucial for post-earthquake assessment and emergency response. Existing nighttime light studies often emphasize single-scale brightness loss, with limited characterization of azimuthal differences within intensity zones and their coupling with population/building exposure, although these factors are essential for explaining spatially uneven earthquake impacts and for improving the interpretation of nighttime light loss patterns. This study integrates daily VIIRS nighttime lights (500 m) with USGS intensity and population/building density to build an intensity–azimuth framework with six directional sectors, quantify pre-/post-earthquake changes at county, patch, and pixel scales, apply bivariate LISA to detect local coupling patterns, and validate against CEMS Rapid Mapping. The results show clear scale complementarity: county aggregation robustly delineates the macro impact extent but smooths internal contrasts; pixel analysis captures fragmented disturbances yet is noise-sensitive; patch-based mapping best aligns with built-up areas at 500 m resolution and shows higher agreement with CEMS in well-lit urban areas. Azimuth–intensity patterns indicate more concentrated NTL reduction in north–south high-intensity zones (NTL = −0.53–−15.67 nW·cm⁻²·sr⁻¹), with local rebounds in some east–west sectors. The framework provides interpretable support for rapid loss assessment and priority-based resource allocation. Full article

The purpose of this paper is to investigate the seismic damage performance levels of reinforced concrete (RC) external beam–column joints exhibiting beam flexural failure mode based on acoustic emission (AE) characteristics. To achieve this purpose, two specimens of RC external beam–column joints with beam flexural failure mode were tested under constant axial compression at the column and low-cyclic lateral loading at the end of the beam. During the tests, six AE-based indicators—namely AE hit ($H_{\mathrm{AE}}$), AE energy ($E_{\mathrm{AE}}$), AE count ($C_{\mathrm{AE}}$), amplitude ($A_{\mathrm{AE}}$), rise time (RT), and peak frequency ($f_{\mathrm{p}}$)—were measured using the PCI-2 Acoustic Emission System equipped with R6$\alpha$ piezoelectric sensors. In addition, five damage performance levels, i.e., no damage, minor damage, medium damage, serious damage, and collapse, were proposed based on the analysis of AE monitoring results. After calibration, the fiber finite element method was used to conduct a numerical simulation of 432 joints subjected to lateral loading. An empirical expression for the material parameter of the Park–Ang damage model was presented based on simulated results. Suggested five damage performance levels were used together with a response databank from the numerical analysis to obtain the limit damage values. This work provides a quantitative AE-based framework for seismic damage assessment of RC external beam–column joints with beam flexural failure mode, which can inform performance-based seismic design and post-earthquake safety evaluation. Full article

Earthquake-induced landslide reactivation poses a sustained hazard for years following major seismic events, yet operational prediction remains constrained by heterogeneous multi-modal data, sparse supervision, and the absence of uncertainty-aware frameworks. This paper presents PIMSEL, a physically guided multi-modal semi-supervised framework for post-seismic landslide reactivation risk assessment. PIMSEL integrates satellite-derived morphological features, precipitation time series, and seismic hazard attributes through four components: entropy-regularized optimal transport for cross-modal semantic alignment without paired supervision; causally constrained hierarchical fusion enforcing domain-consistent modal weighting; scenario-based prototype mutation for semi-supervised learning from sparse expert annotations; and prototype-anchored variational graph clustering that simultaneously stratifies landslides into HIGH, MEDIUM, and LOW risk tiers and produces decomposed aleatoric and epistemic uncertainty estimates for operational triage. The HIGH risk tier operationally corresponds to predicted reactivation, validated against 598 documented reactivation events across 7482 co-seismic landslides from three Sichuan Province earthquake sequences: the 2013 Lushan (Mw 7.0), 2017 Jiuzhaigou (Mw 7.0), and 2022 Luding (Mw 6.8) events. PIMSEL achieves 82.5% reactivation recall and 66.4% precision, outperforming twelve baselines across clustering quality, classification, and uncertainty calibration metrics. Ablation studies confirm that optimal transport alignment contributes the largest individual performance gain. Current limitations include quarterly assessment frequency and dependence on optical imagery under cloud cover, which future integration of real-time meteorological triggers and SAR data should address. Full article

On 6 February 2023, Türkiye was struck by two devastating earthquakes with moment magnitudes of 7.8 and 7.6, causing severe damage to numerous tall reinforced concrete buildings and emphasizing the need for improved seismic design strategies. This study investigates the seismic response of a representative high-rise reinforced concrete building by systematically varying the shear wall area-to-floor area ratio, a key parameter directly influencing lateral stiffness and overall stability. Utilizing a solid modeling approach and incorporating three-directional seismic records, this research provides detailed insights into displacement behavior beyond conventional frame-based analyses. Focusing on Adana, a major urban center with a significant concentration of tall buildings and notable seismic risk, three design scenarios with shear wall ratios of 1.14%, 1.54%, and 2.1% were examined. The results demonstrate that increasing the shear wall cross-sectional area compared to the building plan area significantly reduces lateral and vertical displacements, with the most pronounced improvement observed when moving from 1.14% to 1.54%. Further increase to 2.1% provides additional enhancement in seismic performance. This study suggests that adopting a minimum shear wall area-to-floor area ratio of at least 2% along each principal direction (resulting in a total combined ratio of approximately 4% for the building) can substantially improve seismic resilience and mitigate collapse risk in tall structures. Importantly, the shear wall ratios were considered separately for each principal direction, with the total combined ratio doubling, highlighting the need for balanced wall distribution in both directions. Full article

The earthquake of 23 June 2001, Mw 8.4, caused catastrophic damage in the city of Moquegua (Peru), especially in reinforced-concrete educational buildings. In this research, advanced procedures have been used and compared to assess the seismic performance of a new educational building designed under the current Peruvian construction regulations. Two nonlinear static procedures, the capacity spectrum method and an improved procedure based on the equivalent linearization method, have been applied and compared. Damage probabilities for a 475-year-return-period earthquake for the city of Moquegua evidence that the improved procedure based on the equivalent linearization method turns out to be slightly more conservative than the capacity spectrum method. Incremental dynamic analyses, based on 15 seismic events selected according to specific criteria, are taken as reference and complete the building damage assessment. Probabilistic damage matrices are proposed to assess damage using a probabilistic approach, which makes it possible to determine the levels of risk to be assumed in likely post-seismic scenarios and to carry out probabilistic estimates of the impacted population, the expected damage to structures, and the ranges of economic (social and material) costs. These tools assist stakeholders, civil protection and fire departments and the administrations involved in risk management and contingency planning in developing prevention strategies and improving preparedness for natural disasters such as earthquakes. Full article

Geological hazards are characterized by their sudden occurrence, high destructiveness, and wide spatial impact. In particular, landslides and debris flows triggered by earthquakes and intense rainfall often lead to severe casualties and substantial property losses. Therefore, the rapid delineation of affected areas is crucial for disaster assessment and post-disaster reconstruction. To this end, several geohazard datasets have been developed from remote sensing imagery, focusing on specific regions, disaster types, and data sources, providing valuable support for geohazard detection and risk assessment. Our study addresses the diversity of real-world geological disasters in terms of their types, causes, and spatial distribution and constructs the Composite Geological Hazards Dataset (CGHD), a dual-temporal geohazard dataset that enhances generalisation and practical applicability. CGHD incorporates pre- and post-disaster remote sensing images of 14 landslide and debris flow events that occurred worldwide between 2017 and 2024, collected using four remote sensing platforms and encompassing multiple spatial scales and land-cover categories. The affected areas varied significantly in size and shape, with land-cover types including roads, buildings, vegetation, farmland, and water bodies. This resulted in 3963 pairs of pre- and post-disaster images, each with a size of 1024 × 1024 pixels. We validated the reliability of the CGHD through experiments with nine change-detection models and further evaluated its generalisation capability using an unseen dataset. The experimental results demonstrate that CGHD achieves high recognition accuracy and strong generalisation across diverse geographic environments, providing comprehensive data support for intelligent geohazard recognition and disaster assessment. Full article

Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose severe threats to lives, infrastructure, and post-disaster recovery. While machine learning (ML) and deep learning (DL) approaches to coseismic landslide susceptibility mapping have advanced considerably, spaceborne interferometric synthetic aperture radar (InSAR) products, particularly line-of-sight (LOS) displacement and coherence-based damage proxy maps (DPMs), remain underutilized in event-based frameworks. This study develops and evaluates a multi-factor coseismic landslide probability model that integrates InSAR-derived deformation metrics with geomorphic and hydrologic predictors to support rapid post-earthquake hazard assessment. Using the 25 April 2015 Mw 7.8 Gorkha earthquake as a case study, LOS displacement was derived from ALOS-2 PALSAR-2 ScanSAR interferometry, and the normalized channel steepness index (K_sn) was computed from a digital elevation model. Fourteen conditioning factors were used to train five architectures: Random Forest (RF), XGBoost, CNN, U-Net, and DeepLabV3. Spatial autocorrelation was mitigated using a leave-one-basin-out three-fold spatial cross-validation strategy, with models evaluated on a patch-based domain comprising 655,360 pixels at a positive-class prevalence of 6.35%, establishing a no-skill AUC-PR baseline of 0.0635. InSAR integration consistently improved model performance under high class imbalance, increasing AUC-PR across all models by 7.8% to 17.3%. Random Forest achieved the highest AUC-PR (0.7940, nearly 12.5 times the baseline) and CSI (0.3027), providing the best balance between landslide recall (88.09%) and non-landslide specificity (88.68%) with the lowest false alarm rate (11.32%). XGBoost attained the highest AUC-ROC (0.9501) but exhibited lower recall (83.73%) and poorer calibration (Brier = 0.1397). Among DL models, DeepLabV3 produced the best-calibrated probabilities (Brier = 0.0693) and the highest CSI (0.2307), while U-Net offered the most balanced DL performance and CNN achieved the highest recall (92.40%) at the expense of elevated false alarms. Permutation feature importance identified K_sn as the dominant predictor, highlighting the strong tectono-geomorphic control on coseismic landslide occurrence. These results demonstrate that integrating InSAR-derived products substantially enhances landslide hazard assessment and supports more reliable rapid response in the Nepal Himalaya. Full article

Background/Objectives: Environmental changes following large-scale natural disasters may influence the clinical course of chronic diseases. However, the impact of post-earthquake environmental exposures on pediatric allergic diseases remains insufficiently studied. To evaluate the association between post-earthquake environmental exposures and disease control in children with allergic diseases. Methods: This retrospective longitudinal cohort study included 528 children with previously diagnosed asthma, allergic rhinitis, or atopic dermatitis who were followed in a tertiary pediatric allergy center in Malatya, Türkiye. Clinical assessments performed before the 6 February 2023 Kahramanmaraş earthquakes (T0) were compared with follow-up evaluations conducted 6–12 months after the earthquake (T1). Environmental exposures assessed during the post-earthquake period included prolonged residence in temporary housing, demolition-related dust exposure, and elevated ambient particulate matter levels. Clinical deterioration was defined using disease-specific indicators (decline in ACT/cACT score or treatment step escalation for asthma, increase in TNSS for allergic rhinitis, and increase in SCORAD for atopic dermatitis). Multivariable logistic and linear regression models were used to evaluate associations between environmental exposures and clinical outcomes. Results: Clinical deterioration was observed in 219 children (41.5%). Prolonged residence in temporary housing for ≥6 months (aOR 2.1, 95% CI 1.2–3.9, p = 0.01) and exposure to demolition-related dust (aOR 1.9, 95% CI 1.1–3.5, p = 0.02) were independently associated with clinical deterioration. Among children with asthma, both prolonged temporary housing (adjusted β −1.84, p = 0.002) and demolition-related dust exposure (adjusted β −1.39, p = 0.018) were associated with worsening asthma control. Conclusions: Post-earthquake environmental exposures, particularly prolonged residence in temporary housing and demolition-related dust exposure, were associated with worsening control of pediatric allergic diseases. These findings highlight the importance of environmental health considerations in disaster response and long-term management of children with chronic allergic conditions. Full article

Metallic Shear Plate Dampers (MSPDs) are essential components in passive vibration control systems and require rapid post-earthquake inspection to assess damage and determine replacement needs. Traditional visual inspection methods suffer from low efficiency and limited ability to detect concealed damage. This study proposes a novel MSPD damage detection method based on active sensing and the k-nearest neighbor (KNN) algorithm, featuring high accuracy, efficiency, and low cost. Quasi-static tests were conducted to simulate various damage states. Sweep-frequency excitation was applied using a charge amplifier, and piezoelectric sensors were employed to generate and receive stress wave signals corresponding to different damage conditions. The acquired signals were processed using wavelet packet transform (WPT) and energy spectrum analysis to extract discriminative time–frequency features, which were used to train and validate the KNN model. Results show that the model achieved a validation accuracy of 98.9% using all valid data and 98.1% using a single excitation-sensing channel. When tested on an MSPD with a similar overall structure but lacking stiffeners, the model achieved an accuracy of 92.6% in distinguishing between healthy and damaged states. This indicates that the proposed method has good robustness and practical potential for MSPDs with similar damage evolution and failure modes despite certain structural variations. Full article