Search Results (1,119)

Rapid localisation of trapped victims after urban disasters is essential but challenging because Bluetooth Low Energy (BLE) beacons are intermittent, radio propagation is obstructed by rubble, UAVs are energy-constrained, and real-world multi-UAV training is impractical in high-risk search-and-rescue (SAR) environments. This study formulates post-disaster victim localisation as a cooperative Dec-POMDP and adapts a model-aided federated multi-agent reinforcement learning framework based on FedQMIX. The proposed pipeline combines a lightweight LoS/NLoS surrogate channel model, PSO-based victim-position estimation, return-to-base and map-feasibility safety checks, an SAR-aligned shaped reward, and a leakage-free centralised training state based on estimated rather than ground-truth victim locations. Each UAV trains locally inside a learned digital-twin simulator and periodically shares only QMIX network parameters, avoiding the exchange of raw trajectories or RSSI logs. The framework is evaluated on two synthetic post-earthquake urban maps representing a compact return-to-base scenario and a larger reach-to-destination scenario. Across five independent seeds per method and map, Model-Aided FedQMIX achieves the highest and most stable victim-localisation performance, with the clearest advantage observed in the larger long-horizon scenario. Additional diagnostic tests examine reward-weight sensitivity, RF channel-shift robustness, BLE/smartphone hardware heterogeneity, non-IID client-data variation, and partial-client FedAvg under missing client updates. The results indicate that combining model-aided localisation cues, decentralised value factorisation, SAR-aligned objective design, and federated parameter sharing can improve the robustness of UAV-based victim-localisation policies. The framework also clarifies deployment considerations for federated SAR coordination, including communication payload, privacy boundaries, heterogeneous client experience, device variability, and intermittent connectivity. This study remains simulation-based, and future validation with real UAVs, BLE devices, and rubble-inspired testbeds is required before operational deployment. Full article

The Pamir–Tian Shan domain constitutes one of the most actively deforming intracontinental orogenic systems associated with continued India–Eurasia convergence. Characterizing present-day deformation in this region is fundamental to deciphering its geodynamic evolution and assessing seismic risk. Existing strain rate models based on GNSS measurements display noticeable discrepancies, largely attributable to variations in analytical strategies and uneven station distribution. In this study, we determine the present crustal strain and rotation fields across the Pamir–Tian Shan area using the most updated GNSS velocity solution referenced to stable Eurasia. To address the issues of inconsistent strain rate field results and lack of reliability verification in previous studies based on GNSS data, this paper computes the crustal strain rate field (principal strain rate, maximum shear strain rate, dilatation strain rate, and rotational strain rate) with a grid spacing of 0.75° × 0.75° in the study area, followed by numerical validation of the results’ reliability. The derived strain field is characterized by dominant NNW–SSE shortening throughout much of the orogenic system, with peak compressional strain rates (~1.0 × 10⁻⁷ yr⁻¹) concentrated along the Pamir Frontal Thrust. By contrast, the interior of the Pamir Plateau exhibits clear EW extension, consistent with areas affected by normal-faulting earthquakes. High values of shear strain rates are primarily localized along major active fault systems, whereas negative dilatational components indicate overall contraction within the Tian Shan. The rotation-rate distribution reveals clockwise rotation of the Tarim Basin (approximately 0.6°/Myr) together with counterclockwise rotation affecting the Pamir and Tian Shan blocks, accommodated by prominent strike–slip fault networks. The close spatial agreement between the modeled strain patterns, active tectonic structures, and focal mechanism solutions supports the reliability of the inferred deformation field. The research results of this paper are of great scientific significance for in-depth study of the tectonic evolution and earthquake disaster assessment in the Pamir–Tian Shan region. Full article

Natural disasters such as floods and earthquakes cause severe physical, social, and economic losses, highlighting the critical need for timely and reliable early warning systems. Conventional water level and structural health monitoring technologies are often costly, limiting deployment to high-priority infrastructure only. This paper presents the development and validation of two low-cost Internet of Things (IoT) systems for multi-hazard disaster monitoring and early warning, explicitly supporting UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities) by enabling equitable monitoring of rural or minor bridges. The proposed system achieves a significant cost reduction (approximately $300 compared to conventional systems typically exceeding $5000), highlighting its potential for scalable and sustainable deployment. The first system integrates a Raspberry Pi, Pi Camera, Lidar Lite V3, and ADXL355 accelerometer to simultaneously capture floodwater images, measure water levels, and record bridge vibrations, with distance measurements recorded at user-defined intervals and vibration data sampled up to 100 Hz. Laboratory repeatability and uncertainty analyses of the Lidar Lite V3 indicate a root mean square error of ~2.4 cm over a 0–25 cm range, demonstrating stable performance for flood monitoring and sufficient accuracy for early warning applications using low-cost sensing systems. The ADXL355 accelerometer is validated through harmonic excitation tests (0.1–2 Hz) and real earthquake recordings, confirming its suitability for low-frequency structural response monitoring. The second system combines a Raspberry Pi, an HX711 amplifier, and a CDP25 displacement transducer to measure bridge-bearing displacements up to 25 cm, with data acquisition at sampling rates of up to 80 Hz, with laboratory tests demonstrating consistent and repeatable measurements during both loading and unloading cycles. The IoT framework is resilient, incorporating solar power and local data storage to ensure operation during power or network outages. Unlike prior studies focusing on individual sensors, this work delivers a fully integrated multi-sensor platform with formalized early warning logic based on predefined thresholds. The results demonstrate the feasibility of scalable, real-time, low-cost monitoring for disaster risk reduction and infrastructure resilience, providing a sustainable solution for community-scale early warning applications. Full article

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

Indonesia experiences some of the world’s highest seismic activity, making earthquakes a major source of physical, economic, and social losses. To better quantify these impacts, this study proposes a comprehensive evaluation framework with two new metrics: the Seismic Severity Index (SSI) and the Seismic Impact Index (SII). The indices are derived using four weighting methods—Grey Relational Analysis (GRA), Equal Weights, the Entropy Weight Method (EWM), and Criteria Importance Through Intercriteria Correlation (CRITIC)—and applied to 28 regencies and municipalities affected by damaging earthquakes from 2016 to 2022. Results show that the 2018 earthquake, intensified by a tsunami and liquefaction, caused the most severe losses. Palu Municipality and Donggala Regency consistently recorded high SSI values across all weighting schemes. The SII further identifies Sigi Regency, Donggala Regency, and Palu Municipality as the most heavily impacted areas, although rankings varied by method. Overall, Sigi Regency, Palu City, Donggala Regency, North Lombok, West Lombok and Cianjur exhibit the highest combined severity and impact, while Garut, Ciamis, and Cilacap experienced relatively minor effects. The study concludes that integrating GRA with EWM and CRITIC yields a robust earthquake loss index to support future disaster risk reduction policies. 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

Low-altitude UAV scheduling and planning has become a critical technological pillar in disaster response systems; however, systemic challenges in complex environments and under uncertain risk conditions remain insufficiently understood. Although substantial progress has been achieved in model formulation and algorithm design in recent years, scheduling and planning frameworks still lack a systematic representation of key risk factors, such as meteorological disturbances, terrain damage, and communication constraints, thereby undermining operational safety and decision reliability. This study conducts a systematic review of low-altitude UAV scheduling and planning research over the past decade, covering representative disaster scenarios including forest fires, large building fires, earthquakes, floods, major public health emergencies, and traffic accidents. By comparatively analyzing scheduling objectives and technical pathways across the pre-disaster, during-disaster, and post-disaster stages, this paper summarizes the dominant research paradigms and limitations of multi-UAV coordination, air–ground coordination, and risk reduction-oriented scheduling and planning. This review reveals that existing approaches generally lack explicit modeling of dynamic risks and uncertainties, highlighting an urgent need to incorporate risk-aware considerations and reliability analysis frameworks into scheduling and planning to enhance the overall robustness and decision credibility of UAV systems in disaster environments. Full article

Seismic Shake-e is a free app that provides valuable data and tools related to earthquakes, covering the stages before, during, and after seismic events. In this text, we describe the main features of the Seismic Shake-e 2.1 (SSe) app, the considerations that guided its development, examples of its use, and the challenges for future versions. Version 1.0 of this app was awarded as one of the winners of EOVALUE: Call for Innovative Apps in environmental and social fields, a project by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. SSe recognizes two user levels: basic and intermediate/advanced. There are six modules for each level. The main topics of these modules for both user types are: (1) Accelerometer Networks (AN), (2) Seismograms Analyzer-e (SAe), (3) Seismic Design of Buildings (SDB), (4) Earthquake Preparedness (EP), (5) Earthquake Early Warning Systems (EEWS) & Tsunami Warning Systems (TWS), and (6) Earthquake Emergency Response & Recovery. The two key modules are AN and SAe: the first explains how to obtain seismic records, and the second provides tools for their analysis. We include some applications of SSe, along with their results and discussion. We also list the advantages of the main modules and discuss potential future developments and improvements. The uniqueness of this work is that we highlight the software’s essential features and demonstrate its applications. Full article

A key component of research on disaster risk in modern-age society in the inland areas of the Campania Region, southern Italy, was discovered in parish registers. Ecclesiastical archives, containing thousands of largely unpublished pages, served as a rich source of information on disruption and casualties. The parish registers preserved in these archives from the 16th century provide demographic records as well as notes on the most terrible events that affected society at the time. They include the catastrophic effects of seismic events recorded in this sector of the southern Apennines, an area characterised by high seismicity due to the complex dynamics of the convergence zone between the African and Eurasian plates. New findings reveal a more precise number and previously unreported deaths in several villages, confirming and suggesting some macroseismic intensities for the 1694 seismic event; moreover, further evidence was found for the hypothesised 1692 seismic event. A greater number of deaths was observed in some villages during the 1702 and 1732 events. Parish documents provided details about local construction techniques adopted after the well-known earthquake of 1732, including the use of more resilient materials and design modifications. Full article

Earthquake-induced landslides are a major secondary seismic hazard in mountainous regions and can cause significant human and economic losses. This study presents COSISA (Co-Seismic Slope Instabilities Susceptibility Assessment), a software tool developed in Python and GIS to automate the generation of co-seismic landslide susceptibility maps based on the Newmark displacement method combined with a logic-tree approach. The software integrates geomorphological, geotechnical, and seismic data to compute Newmark displacement using several available empirical equations. The logic-tree framework incorporates the variability and uncertainty of geotechnical parameters, failure depth, degree of saturation, and empirical models through weighted combinations of input variables. As a result, COSISA produces numerous susceptibility maps corresponding to different parameter combinations and generates a weighted susceptibility map. The tool was applied to a case study in the Granada Basin (southeastern Spain), an area affected by the 2021 Santa Fe seismic sequence. Results show that COSISA efficiently generates multiple susceptibility scenarios and identifies best- and worst-case conditions, significantly reducing the time and effort required compared with conventional step-by-step procedures. This approach supports seismic hazard assessment and can contribute to territorial planning and risk management strategies aimed at reducing damage from future co-seismic landslides. Full article

Afghanistan is located within one of the world’s most seismically active regions, where recurrent earthquakes pose a persistent threat to human life and the built environment. The 7 October 2023 Herat earthquake exposed critical vulnerabilities in both the construction sector and institutional frameworks, manifested through the widespread presence of non-engineered buildings, poor construction quality, and the absence of mandatory and enforceable seismic design regulations. This study examines the structural, construction-related, and governance deficiencies that significantly contributed to extensive building damage and high casualty rates, while also assessing shortcomings in public preparedness and disaster risk governance. A comparative case-study approach is adopted to evaluate seismic resilience and disaster management practices in India, Pakistan and Iran. The findings indicate that the elevated vulnerability observed in Herat primarily resulted from deficient construction practices, the lack of codified seismic standards, weak regulatory enforcement, and limited technical capacity within the construction industry. In contrast, regions characterized by well-established seismic codes, engineered structural systems, and coordinated institutional mechanisms experienced substantially reduced levels of structural damage and human losses, although earthquake impacts are also influenced by factors such as hazard characteristics, site conditions, exposure levels, and population distribution. The study highlights that seismic safety and sustainable development are inherently interdependent objectives. Improving earthquake resilience in Afghanistan requires the integration of earthquake-resistant engineering with sustainable construction practices, enhancement of technical and professional capacity, rigorous enforcement of region-specific seismic regulations, and strengthened community-based awareness programs. The adoption of internationally recognized best practices and risk-informed planning strategies is essential for fostering safer, more resilient, and environmentally sustainable urban development capable of withstanding future seismic events. 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

This study presents an integrated design methodology for earthquake demolition rescue robots by combining UXMs, Kano, and QFD to improve design rationality and performance in extreme rescue scenarios. It addresses key gaps in existing approaches, particularly the lack of systematic experiential data acquisition, quantitative requirement analysis, and effective design translation. UXMs are applied to reconstruct critical task scenarios and identify high-load nodes and user experience variations. The Kano model is used to prioritise and classify user requirements, which are then translated into engineering characteristics through QFD. Based on this framework, a conceptual robot design is developed using the FBS model and evaluated through process-level simulation and usability assessment. The results demonstrate that the proposed method enables structured requirement transformation and supports traceable design decisions. Simulation indicates the consistency of task workflows and coordination among functional modules at the process level. A System Usability Scale score of 80.22 indicates a relatively high level of perceived usability at the conceptual evaluation stage. The proposed methodology provides a structured and traceable conceptual design framework for earthquake rescue robots. While the current validation is based on conceptual-level evaluation, the methodology offers a traceable design pathway that may be extended to other high-risk emergency equipment with further empirical testing. Full article

Post-earthquake emergency response planning requires rapid and adaptive resource allocation under disrupted accessibility, uneven district-level demand, and hazardous field conditions. In large metropolitan areas, these challenges are intensified by spatial differences in social vulnerability, infrastructure disruption, operational feasibility, and responder exposure. Static allocation approaches are often insufficient in such environments because they cannot adequately reflect temporal change or the evolving relationship between urgency, accessibility, and operational risk. This study proposes a dynamic multi-objective model for district-level post-earthquake resource allocation that integrates social vulnerability, occupational safety, and Markov-based updating within a single analytical framework. First, district priority scores are derived through an Analytic Hierarchy Process based on building damage ratio, intervention time, social vulnerability, critical infrastructure damage, secondary hazard risk, team capacity, and occupational safety. Second, a Markov-based updating mechanism is used to represent time-dependent redistribution across response periods. Third, a constrained weighted-sum multi-objective optimization model is formulated to balance district priority, social vulnerability, and responder safety under capacity and accessibility limits. The model is applied to Istanbul using official district-level data from national and local institutional sources. Scenario-based analysis is conducted under balanced, priority-oriented, vulnerability-oriented, and safety-oriented settings, together with static and dynamic model comparisons. The results show that the dynamic structure produces a more adaptive allocation profile than the static structure, with the share of the Very High allocation class declining from 37.66% to 34.95% and the Low allocation class increasing from 12.89% to 16.00% over the response horizon. The findings also indicate that greater emphasis on social vulnerability shifts allocation toward more fragile districts, whereas stronger safety emphasis reduces cumulative operational exposure at the cost of moderate reductions in immediate coverage. Overall, the study contributes to the disaster response literature by linking multi-criteria district prioritization, dynamic redistribution, and safety-aware allocation within a unified district-level decision structure. Beyond the Istanbul application, the proposed framework offers a practical basis for more responsive, equitable, and operationally sustainable post-earthquake planning in complex urban environments. Full article

Soft story irregularity poses a critical seismic risk to existing building stocks. While current seismic codes define stiffness irregularity factors to detect this vulnerability, they are typically evaluated based solely on initial elastic properties. This study investigates the evolution of these code-defined factors (ASCE/SEI-7, UBC, NBC, TBEC-2018, and BSL) within the post-elastic range to examine how structural damage affects soft story irregularity. The methodology comprises two phases: a low-strength RC plane frame (Case A) and a parametric study on a 3D RC building with incrementally increased ground story heights (Case B). Nonlinear pushover analyses were conducted to track the variation in irregularity factors at each pushover step and examined graphically. Results demonstrate that soft story behavior is not a static characteristic; irregularity factors deteriorate significantly as plastic hinges form. Crucially, several models that initially satisfied code limits in the elastic range eventually exceeded irregularity thresholds under inelastic behavior. This indicates that relying solely on initial stiffness may mask latent irregularities emerging during seismic actions. Consequently, to capture the true severity of soft story mechanisms, it is recommended that stiffness irregularity factors be evaluated at target displacement levels corresponding to the design earthquake. Full article