Search Results (107)

Effective earthquake early warning (EEW) is essential for disaster prevention in the built environment, enabling a rapid structural response, system shutdown, and occupant evacuation to mitigate damage and casualties. However, most current EEW systems lack rigorous reliability analyses of their predictive outcomes, limiting their effectiveness in real-world scenarios—especially for on-site warnings, where data are limited and time is critical. To address these challenges, we propose a Bayesian neural network (BNN) framework based on Stein variational gradient descent (SVGD). By performing Bayesian inference, we estimate the posterior distribution of the parameters, thus outputting a reliability analysis of the prediction results. In addition, we incorporate a continual learning mechanism based on elastic weight consolidation, allowing the system to adapt quickly without full retraining. Our experiments demonstrate that our SVGD-BNN model significantly outperforms traditional peak displacement (Pd)-based approaches. In a 3 s time window, the Pearson correlation coefficient R increases by 9.2% and the residual standard deviation $SD$ decreases by 24.4% compared to a variational inference (VI)-based BNN. Furthermore, the prediction variance generated by the model can effectively reflect the uncertainty of the prediction results. The continual learning strategy reduces the training time by 133–194 s, enhancing the system’s responsiveness. These features make the proposed framework a promising tool for real-time, reliable, and adaptive EEW—supporting disaster-resilient building design and operation. Full article

In recent decades, climate change has led to more frequent episodes of extreme rainfall, increasing the risk of river flooding. Streams and rivers characterized by short flow times are subject to rapid and impressive floods; for this reason, the modeling of their beds is of fundamental importance for the execution of hydraulic calculations capable of predicting the flow rates and identifying the points where floods may occur. In the context of studies conducted on three watercourses in Calabria (Italy), different survey and restitution techniques were used (aerial LiDAR, terrestrial laser scanner, GNSS, photogrammetry). By integrating these methodologies, multi-resolution models were generated, featuring a horizontal accuracy of ±16 cm and a vertical accuracy of ±15 cm. These models form the basis for the hydraulic calculations performed. The results demonstrate the feasibility of producing accurate models that are compatible with the memory and processing capabilities of modern computers. Furthermore, the technique set up and implemented for the refined representation of both the models and the effects predicted by hydraulic calculations in the event of exceptional rainfall (such as flow, speed, flooded areas, and critical points along riverbanks) serves as a valuable tool for improving hydrogeological planning, designing appropriate defense works, and preparing evacuation plans in case of emergency, all with the goal of mitigating hydrogeological risk. Full article

Historical districts face pressing disaster preparedness challenges due to their special spatial properties—risks compounded by static approaches that overlook dynamic fire–pedestrian interactions. This study employs an agent-based model (ABM) for fire simulations and AnyLogic pedestrian dynamics to address these gaps in Dukezong Ancient Town, Yunnan Province, China, considering diverse ignition points, seasonal temperatures, and wind conditions. Dynamic simulations of 16 scenarios reveal critical spatial impacts: within 30 min, ≥28% of streets became impassable, with central ignition points causing faster obstructions. Static models underestimate evacuation durations by up to 135%, neglecting early stage congestions and detours caused by high-temperature zones. Congestions are concentrated along main east–west arterial roads, worsening with longer warning distances. A mismatch between evacuation flows and shelter capacity is found. Thus, a three-stage interaction simplification is derived: localized detours (0–10 min), congestion-driven delays on critical roads (11–30 min), and prolonged structural damage afterward. This study challenges static approaches by highlighting the “fast alert-fast congestion” paradox, where rapid alerts overwhelm narrow pathways. Solutions prioritize multi-route guidance systems, optimized shelter access points, and real-time information dissemination to reduce bottlenecks without costly infrastructure changes. This study advances disaster modeling by bridging disaster development with dynamic evacuation, offering a replicable framework for similar environments. Full article

With the rapid expansion of urban underground spaces, safety concerns related to ascending evacuation have become increasingly critical, particularly for children, who are more susceptible to fatigue than adults. However, most existing research focuses on adults and overlooks the unique needs of children. This study investigated two key fatigue-related factors, evacuation height and body mass index (BMI), to construct a predictive model of children’s fatigue levels and proposed a non-invasive, code-compliant assistive solution integrated into underground fire escape stairways. Data were collected from 41 child participants during an ascending evacuation under simulated emergency conditions using real-time heart rate monitoring and video analysis. Statistical correlation and regression modeling revealed a significant positive correlation between evacuation height and heart rate (p < 0.01). Female participants exhibited higher mean heart rates and greater variability, with a strong positive correlation between BMI and heart rate observed in females (p < 0.01). Regression analysis showed that heart rate increased with BMI but plateaued in the obese group. These findings demonstrate that evacuation height and BMI significantly influence children’s fatigue levels. Based on these physiological insights, this study proposes a non-invasive architectural intervention to enhance children’s evacuation performance, offering practical guidance for the design of intelligent evacuation systems. Furthermore, it provides theoretical support for child-centered assistive design and safety improvement within the boundaries of current fire protection codes. Full article

Landslide hazard chains pose significant threats in mountainous areas worldwide, yet their cascading effects remain insufficiently studied. This study proposes an integrated framework to systematically assess the landslide-landslide dam-outburst flood hazard chain in mountainous river systems. First, landslide susceptibility is assessed through a random forest model incorporating 11 static environmental and geological factors. The surface deformation rate derived from SABS-InSAR technology is incorporated as a dynamic factor to improve classification accuracy. Second, motion trajectories of rock masses in high-risk zones are identified by Rockfall Analyst model to predict potential river blockages by landslide dams, and key geometric parameters of the landslide dams are predicted using a predictive model. Third, the 2D HEC-RAS model is used to simulate outburst flood evolution. Results reveal that: (1) incorporating surface deformation rate as a dynamic factor significantly improves the predictive accuracy of landslide susceptibility assessment; (2) landslide-induced outburst floods exhibit greater destructive potential and more complex inundation dynamics than conventional mountain flash floods; and (3) the outburst flood propagation process exhibits three sequential phases defined by the Outburst Flood Arrival Time (FAT): initial rapid advancement phase, intermediate lateral diffusion phase, and mature floodplain development phase. These phases represent critical temporal thresholds for initiating timely downstream evacuation. This study contributes to the advancement of early warning systems aimed at protecting downstream communities from outburst floods triggered by landslide hazard chains. It enables researchers to better analyze the complex dynamics of such cascading events and to develop effective risk reduction strategies applicable in vulnerable regions. Full article

Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion. Full article

Emergency water release from underground reservoirs is characterized by its suddenness and significant harm. The quantitative prediction of water spreading processes in mine tunnels is crucial for enhancing underground safety. The study focuses on an underground roadway in a coal mine, constructing a three-dimensional physical model of the complex tunnel network to explore the spatiotemporal characteristics of water flow spreading after water release in coal mine tunnels. The Volume of Fluid (VOF) model of the Eulerian multiphase flow was adopted to simulate the flow state of water in the roadway. The results indicate that after water release from the reservoir, water flows along the tunnel network towards locations with relatively lower altitude terrain. During the initial stage of water release, sloping tunnels act as barriers to water spreading. The water level height at each point in the tunnel network generally experiences three developmental stages: rapid rise, slow increase, and stable equilibrium. The water level height in the tunnel area near the water release outlet rises sharply within a time range of 550 s; tunnels farther from the water release outlet experience a rapid rise in water level height only after 13,200 s. The final stable equilibrium water level in the tunnel depends on the location of the water release outlet and the relative height of the terrain, with a water level height ranging from 0.3 to 3.3 m. The maximum safe evacuation time for personnel within a radius of 300 m from the drainage outlet is only 1 h. In contrast, areas farther away from the drainage location benefit from the water storage capacity of the complex tunnel network and have significantly extended evacuation opportunities. Full article

Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. To detect early fire risk indicators, the system processes data from 62,630 sensor measurements across 15 ecological parameters, including temperature, humidity, total volatile organic compounds (TVOC), CO₂ levels, and particulate matter. A comparative analysis of six machine learning models—Logistic Regression, Support Vector Machines (SVM), Random Forest, and proposed EvacuNet—demonstrates that EvacuNet outperforms all other models, achieving an accuracy of 99.99%, precision of 1.00, recall of 1.00, and an AUC-ROC score close to 1.00. The predictive alarm system significantly reduces false alarm rates and enhances fire detection speed, allowing emergency responders to take preemptive action. Moreover, integrating AI-driven evacuation optimization minimizes bottlenecks and congestion, reduces evacuation times, and improves structured crowd movement. These findings underscore the necessity of intelligent fire detection systems in high-occupancy venues, demonstrating that AI-based predictive modeling can drastically improve fire response and evacuation efficiency. Future research should focus on integrating IoT-enabled emergency navigation, reinforcement learning algorithms, and real-time crowd management systems to further enhance predictive accuracy and minimize casualties. By adopting such advanced technologies, large-scale venues can significantly improve emergency preparedness, reduce evacuation delays, and enhance public safety. Full article

In the whole service life of building clusters, they will encounter multiple hazards, including the disaster chain of earthquakes and building debris. The falling debris may block the post-earthquake roads and even severely affect the evacuation, emergency, and recovery operations. It is of great significance to develop a surrogate model for predicting seismic responses of building clusters as well as a prediction model of post-earthquake debris. This paper presents a general methodology for developing a surrogate model for rapid seismic responses calculation of building clusters and probabilistic prediction model of debris width. Firstly, the building cluster is divided into several types of representative buildings according to the building function. Secondly, the finite element (FE) method and discrete element (DE) method are, respectively, used to generate the data pool of structural floor responses and debris width. Finally, with the structural response data of maximum floor displacement, a surrogate model for rapidly calculating seismic responses of structures is developed based on the XGBoost algorithm, achieving R² > 0.99 for floor displacements and R² = 0.989 for maximum inter-story drift ratio (MIDR) predictions. In addition, an unbiased probabilistic prediction model for debris width of blockage is established with Bayesian updating rule, reducing the standard deviation of model error by 60% (from σ = 10.2 to σ = 4.1). The presented models are applied to evaluate the seismic damage of the campus building complex in China University of Mining and Technology, and then to estimate the range of post-earthquake falling debris. The results indicate that the surrogate model reduces computational time by over 90% compared to traditional nonlinear time-history analysis. The application in this paper is helpful for the development of disaster prevention and mitigation policies as well as the post-earthquake rescue and evacuation strategies for urban building complexes. Full article

Floods resulting from dam failures are highly destructive, characterized by intense impact forces, widespread inundation, and rapid flow velocities, all of which pose significant threats to public safety and social stability in downstream regions. To improve evacuation efficiency during such emergencies, it is essential to study flood evacuation route planning. This study aimed to minimize evacuation time and reduce risks to personnel by considering the dynamic evolution of dam-break floods. Using aerial photography from an unmanned aerial vehicle, the downstream road network of a reservoir was mapped. A coupled flood–road network coupling model was then developed by integrating flood propagation data with road network information. This model optimized evacuation route planning by combining the dynamic evolution of flood hazards with real-time road network data. Based on this model, a flood evacuation route planning method was proposed using Dijkstra’s algorithm. This methodology was validated through a case study of the Shanmei Reservoir in Fujian, China. The results demonstrated that the maximum flood level reached 18.65 m near Xiatou Village, and the highest flow velocity was 22.18 m/s near the Shanmei Reservoir. Furthermore, evacuation plans were developed for eight affected locations downstream of the Shanmei Reservoir, with a total of 13 evacuation routes. These strategies and routes resulted in a significant reduction in evacuation time and minimized the risks to evacuees. The life-loss risk was minimized in the evacuation process, and all evacuees were able to reach safe locations. These findings confirmed that the proposed method, which integrated flood dynamics with road network information, ensured the safety and effectiveness of evacuation routes. This approach met the critical needs of emergency management by providing timely and secure evacuation paths in the event of dam failure. Full article

In October 2003, an artificial canal was dug across the Langue de Barbarie sand spit at the mouth of the Senegal River to prevent the city of Saint-Louis (Senegal) from being submerged by floods. This study aimed to explore the multiple facets of this sudden environmental change to provide a holistic overview of the situation and a better understanding of man-made alterations of coastal features, a crucial step for implementing efficient management of such situations and developing appropriate mitigation and adaptation policies. Satellite imagery from the US Geological Survey was used to show the historical evolution of the breach, and a comprehensive overview of the existing literature was conducted to explore its hydrological, geomorphological, ecological, and socioeconomic impacts. Although the canal facilitated the rapid evacuation of floodwaters and saved the city from a major flooding event, the breach widened considerably, becoming the new river mouth and resulted in unforeseen adverse consequences. Environmental consequences included the partial dismantling of the spit, increased tidal range, salinization of land and water, and loss of habitat and local biodiversity. Socioeconomic consequences were severe, including the loss of agricultural land and reduced yields, declining fishing productivity, the destruction of villages, the displacement of entire communities, and the forced migration of many young people. Affected communities developed resilience strategies, with women playing a leading role in these adaptive responses. This study highlights the need for integrated coastal management and policies that consider both environmental and human factors, as well as for future research that will help improve the management of coastal ecosystem alterations. Full article

Background/Objectives: Pontine cavernomas are rare and challenging vascular malformations, representing a critical subset of brainstem lesions due to their deep location and proximity to essential neural structures. When hemorrhagic, these lesions can cause rapid neurological deterioration, posing life-threatening risks. Management requires a delicate balance between aggressive intervention and preserving vital functions. This case report presents the successful surgical treatment of a giant hemorrhagic pontine cavernoma, highlighting the integration of advanced imaging, precision surgical techniques, and multidisciplinary care to achieve an exceptional patient outcome. Methods: A 47-year-old female presented with acute neurological deterioration, including severe right-sided hemiparesis, dysphagia, and obnubilation. High-resolution MRI, including susceptibility-weighted imaging, confirmed a giant hemorrhagic pontine cavernoma causing brainstem compression. An urgent left-sided pterional craniotomy with a transsylvian approach was performed to access the lesion. Subtotal resection and hematoma evacuation were carried out to relieve brainstem compression while preserving critical structures. Postoperative recovery and lesion stability were evaluated through clinical assessments and imaging after three months. Results: Postoperatively, the patient exhibited marked neurological recovery, with near-complete resolution of hemiparesis, restored swallowing function, and significant functional improvement. Follow-up imaging confirmed a stable residual lesion, no recurrence of hemorrhage, and a well-preserved ventricular system. The combination of early intervention and tailored surgical strategies resulted in a highly favorable outcome. Conclusions: This case underscores the complexity of managing giant hemorrhagic pontine cavernomas and demonstrates that carefully planned surgical intervention, combined with advanced imaging and patient-focused care, can yield remarkable outcomes. It highlights the critical importance of early diagnosis, meticulous surgical planning, and future innovations in neurovascular surgery to improve outcomes in these rare but high-stakes cases. Full article

The rapid pace of global urbanization has exacerbated the urban wind-heat environment, posing a severe threat to public health and sustainable urban development. This study explores the aerodynamic transport characteristics of bioaerosols in a local urban area of Beijing following an accidental bioaerosol release. By coupling the Weather Research and Forecasting (WRF) model with a Computational Fluid Dynamics (CFD) model, the research accounts for the temporality of urban airflow and atmospheric stability. A dose–response model was employed to assess the exposure risks to Beijing Institute of Technology personnel. The findings reveal substantial differences in flow fields and bioaerosol dispersion under varying atmospheric stability: the infection area ratio was 42.19% under unstable conditions and 37.5% under stable conditions. Infection risk was highest near the release source, decreasing with distance. Under the three stability conditions, the probability of infection is highest near the release source and decreases with increasing distance. Contaminants propagate more rapidly under unstable conditions, while stable conditions have a higher concentration of high-risk areas. Gender-based analysis indicated a higher infection probability for males due to elevated inhalation rates. This study elucidates the critical role of atmospheric stability in bioaerosol dispersion and provides a robust scientific foundation for biosafety planning, including early warning, mitigation, and emergency evacuation strategies. Full article

The presented article discusses the possibilities and methods of carrying out evacuation works in the event of an emergency associated with slope deformation in the built-up area of Šalgovík, Slovak Republic. From the point of view of extraordinary events, slope deformations are a negative phenomenon for every country. Besides the most serious natural disasters such as floods, landslides and earthquakes, slope deformations are in third place in terms of the extent of direct or indirect damage. Moreover, for the above reasons, the presented article discusses the possibilities of area evacuation in the event of an emergency in a given built-up area, where, as described in the article, it is a location that is susceptible to slope deformation. Given that it is a built-up area that is not stabilized for slope deformations and is also active, the article explains the activities of the Integrated Rescue System components in the event of an emergency in the said area. The aim was also to carry out a widespread evacuation, which has different characteristics than normal evacuations in the case of other emergencies since a large part of the territory with a certain number of inhabitants is affected. It should be noted that the evacuation of the said territory must be carried out in a rapid time frame so that the consequences for health and human life are minimal, which is explained in the present article. The activities the individual rescue services perform to carry out the evacuation will have to be conducted in a different way than normal, and for this reason, the cooperation and activities required are different from the activities normally carried out. Full article

This study analyzes the implementation of firefighting procedures and evacuation methods in a hospital environment, with a focus on ensuring rapid rescue operations and evacuation methods in a real fire in 2024. This research emphasizes the integration of firefighting technologies, including fire detection systems, real-time communication networks, and specialized evacuation strategies for immobile patients. This work further examines the optimization of the emergency response through the coordinated efforts of an integrated rescue system, emphasizing tactical decision making and resource allocation. The findings demonstrate the effectiveness of evacuation methods in the event of needing to evacuate a larger number of people, as well as meeting the need to ensure that active fire protection systems are in an operational state. This research provides key recommendations for improving fire protection measures in healthcare facilities, ensuring faster response times and increased patient protection. Subsequently, after evaluating and reviewing all the options, conclusions were drawn from the on-site results, and recommendations were defined for future fires in similar facilities. Full article