Search Results (99)

Ensuring the structural integrity of reinforced concrete (RC) components in nuclear facilities exposed to extreme conditions is essential for safe decommissioning. This study investigates the impact of high-temperature exposure on RC pedestal structures supported by steel ring-shaped shear connectors—critical elements for maintaining vertical and lateral load paths in containment systems. Scaled-down cyclic loading tests were performed on pedestal specimens with and without prior thermal exposure, simulating post-accident conditions observed at a damaged nuclear power plant. Experimental results show that thermal degradation significantly reduces lateral stiffness, with failure mechanisms concentrating at the interface between the concrete and the embedded steel skirt. Complementary finite element analyses, incorporating temperature-dependent material degradation, highlight the crucial role of load redistribution to steel components when concrete strength is compromised. Parametric studies reveal that while geometric variations in the inner skirt have limited influence, thermal history is the dominant factor affecting vertical capacity. Notably, even with substantial section loss in the concrete, the steel inner skirt maintained considerable load-bearing capacity. This study establishes a validated analytical framework for assessing structural performance under extreme conditions, offering critical insights for risk evaluation and retrofit strategies in the context of nuclear facility decommissioning. Full article

Concrete is a fundamental material in structural engineering, widely used in critical infrastructure such as bridges, nuclear power plants, and dams. These structures may be subjected to extreme dynamic loads resulting from natural disasters, industrial accidents, or missile impacts. Therefore, a comprehensive understanding of concrete behavior under high strain rates is essential for safe and resilient design. Experimental investigations, particularly spalling tests, have highlighted the strain-rate sensitivity of concrete in dynamic tensile loading conditions. This study presents a macroscopic 3D discrete element model specifically developed to simulate the dynamic response of concrete subjected to extreme loading. Unlike conventional continuum-based models, the proposed discrete element framework is particularly suited to capturing damage and fracture mechanisms in cohesive materials. A key innovation lies in incorporating a physically grounded strain-rate dependency directly into the local cohesive laws that govern inter-element interactions. The originality of this work is further underlined by the validation of the discrete element model under dynamic tensile loading through the simulation of spalling tests on normalstrength concrete at strain rates representative of severe impact scenarios (30–115 s⁻¹). After calibrating the model under quasi-static loading, the simulations accurately reproduce key experimental outcomes, including rear-face velocity profiles and failure characteristics. Combined with prior validations under high confining pressure, this study reinforces the capability of the discrete element method for modeling concrete subjected to extreme dynamic loading, offering a robust tool for predictive structural assessment and design. Full article

The Engine Electronic Controller (EEC), as the core component of an aircraft engine control system, is vulnerable to rapid failure when exposed to thermal shock during engine fire incidents, potentially leading to catastrophic aviation accidents. To address this issue, this study conducts both numerical simulations and experimental investigations to evaluate the thermal performance of the EEC under thermal shock conditions, exploring the weaknesses of the EEC chassis under high-temperature thermal shock and the damage to important internal electronic components. A three-dimensional finite element model of the EEC was established to simulate its behavior under a thermal shock of 1100 °C. Simulation results reveal that the aluminum alloy chassis wall cannot withstand the extreme thermal load, resulting in failure of the internal electronic components within the first 5 min of exposure, thereby rendering the EEC inoperative. In contrast, when the chassis wall is made of stainless steel, all components and internal electronics remain functional throughout the initial 5 min thermal shock period. Experimental results show that the temperature evolution and component failure patterns under both scenarios align well with the simulation outcomes, thus validating the model’s accuracy. Full article

Under extreme operating conditions such as high temperatures, strong corrosion, and cyclic thermal shocks, key equipment in solar tower power plants (STPPs) is prone to severe accidents and results in significant losses. To systematically quantify potential failure risks and address the methodological gaps in existing research, this study proposes a risk assessment framework combining a novel scenario propagation model covering triggering factors, precursor events, accident scenarios, and response measures with an interval type-2 fuzzy set (IT2FS) Bayesian network. This framework establishes equipment failure evolution pathways and consequence evaluation criteria. To address data scarcity, the methodology integrates actual case data and expert elicitation to obtain assessment parameters. Specifically, an IT2FS-based similarity aggregation method quantifies expert opinions for prior probability estimation. Additionally, to reduce computational complexity and enhance reliability in conditional probability acquisition, the IT2FS-integrated best–worst method evaluates the relative importance of parent nodes, combined with a leakage-weighted summation algorithm to generate conditional probability tables. The model was applied to a western Chinese STPP and the results show the probabilities of receiver blockage, pipeline blockage, tank leakage, and heat exchanger blockage are 0.061, 0.059, 0.04, and 0.08, respectively. Under normal operating conditions, the occurrence rates of level II accident consequences for all four equipment types remain below 6%, with response measures demonstrating significant suppression effects on accidents. The research results provide critical decision-making support for risk management and mitigation strategies in STPPs. Full article

Severe earthquakes, extreme floods, tragic accidents, mega-fires, and even viruses belong to disasters that can destroy the economic, social, or cultural life of people. Due to the climate crisis, disasters will likely become more frequent and intense over the years. Unmanned aerial vehicles (UAVs/drones) have obtained an increasing role in disaster management, which was particularly evident during the COVID-19 pandemic. However, lack of social acceptability remains a limiting factor of drone usage. Drones as a means of state surveillance—possibly mass surveillance—are subject to certain limits since their advanced monitoring technology, including Artificial Intelligence, may affect human rights, such as the right to privacy. Due to the severity of the pandemic, which has been described as the “ideal state of emergency”, despite the rising use of drones, such privacy concerns have been underestimated so far. At the same time, the existing approach of the European Court of Human Rights (ECtHR) and the Court of Justice of the European Union (CJEU) regarding the COVID-19 health crisis and human rights during emergencies seems rather conservative and, thus, setting limits between conflicting rights in such exceptional circumstances remains vague. Under these conditions, the fear that the COVID-19 pandemic may have become a starting point for transitioning to a world normalizing the exception is evident. Such fear in terms of privacy implies a world with a narrowed scope of privacy; thus, setting questions and exploring the challenges about the future of drone regulation, especially in the European Union, are crucial. Full article

Recently, the development of autonomous vehicles (AVs) and intelligent driver assistance systems has drawn significant attention from the public. Despite these advancements, AVs may encounter critical situations in real-world scenarios that can lead to severe traffic accidents. This review paper investigated these critical scenarios, categorizing them under weather conditions, environmental factors, and infrastructure challenges. Factors such as attenuation and scattering severely influence the performance of sensors and AVs, which can be affected by rain, snow, fog, and sandstorms. GPS and sensor signals can be disturbed in urban canyons and forested regions, which pose vehicle localization and navigation problems. Both roadway infrastructure issues, like inadequate signage and poor road conditions, are major challenges to AV sensors and navigation systems. This paper presented a survey of existing technologies and methods that can be used to overcome these challenges, evaluating their effectiveness, and reviewing current research to improve AVs’ robustness and dependability under such critical situations. This systematic review compares the current state of sensor technologies, fusion techniques, and adaptive algorithms to highlight advances and identify continuing challenges for the field. The method involved categorizing sensor robustness, infrastructure adaptation, and algorithmic improvement progress. The results show promise for advancements in dynamic infrastructure and V2I systems but pose challenges to overcoming sensor failures in extreme weather and on non-maintained roads. Such results highlight the need for interdisciplinary collaboration and real-world validation. Moreover, the review presents future research lines to improve how AVs overcome environmental and infrastructural adversities. This review concludes with actionable recommendations for upgrading physical and digital infrastructures, adaptive sensors, and algorithmic upgrades. Such research is important for AV technology to remain in the zone of advancement and stability. Full article

The operational condition of fire water supply aims to ensure the continuous and reliable supply of high-pressure water in emergency situations. Assuming a fire breaks out in a mountain village located far from the city center, due to the significantly higher flow rate and velocity of the water supply pipeline compared to normal operating conditions, any malfunction or shutdown of the pump caused by improper operation could result in catastrophic damage to the pipeline system. In response to the call for sustainable development, addressing this urgent academic challenge means finding a way to safely and economically maintain a continuous water supply to the target water demand point, even under extreme accident conditions. In this paper, drawing on engineering examples, we considered air tanks with varying process parameters installed at multiple locations within a water conveyance system to prevent water hammer and ensure water supply safety. To ensure that air tanks are of high quality and cost-effective after procurement and use, a multi-objective optimization design model comprising fitting, optimization, and evaluation plates was constructed, aimed at selecting certain process parameters. In the multi-objective optimization design model, Latin hypercube sampling improved by simulated annealing (LHS-SA), stepwise regression analysis (SRA), the Multi-Objective Whale Optimization Algorithm (MOWOA), and the Multi-Criteria Decision Analysis (MCDA) method with various weight biases are used to ensure the rationality of the optimization process. By comparing the optimization results obtained using these different MCDA methods, it is evident that the results output after AHP-EWM evaluation tend to be economic indicators, whereas the results output after FN-MABAC evaluation tend to be safety indicators. In addition, according to the sensitivity analysis of weight distribution, it can be inferred that the changes in maximum transient pressure head caused by water hammer have the most significant impact on final decision-making. Full article

Rail transportation of hazardous material (Hazmat) involves low-probability, and high-consequence risks, requiring strategies that mitigate extreme accident impacts while ensuring fair risk distribution. To address this, we introduce conditional value-at-risk with equity (CVaRE) into railway Hazmats risk assessment, enabling flexible decision-making that balances risk minimization and equity considerations. Unlike conventional models that focus solely on risk reduction, CVaRE incorporates a risk equity goal, ensuring a more balanced distribution of risk across transportation routes. This study develops a novel CVaRE model that replaces fixed threshold constraints with a dynamic risk equity goal, providing greater flexibility in risk distribution adjustments. A k-shortest path-based algorithm was designed to balance extreme risk minimization with equitable risk allocation in route selection. A case study on the Yangtze River Delta railway network validates the model, demonstrating that moderate cost increases can significantly reduce extreme accident risks while achieving fairer risk distribution. Findings also show that direct transportation improves risk equity over transfer-based routes, highlighting the importance of strategic route planning. This research offers practical decision support for transport companies and regulators, helping optimize routes while ensuring cost efficiency and regulatory compliance. It also provides a scientific foundation for risk-equity-based policies, promoting safer, more sustainable Hazmat railway transportation. Full article

Unmanned Aerial Vehicles (UAVs) play an extremely important role in real-time object detection for maritime emergency rescue missions. However, marine accidents often occur in low-visibility weather conditions, resulting in poor image quality and a lack of object detection samples, which significantly reduces detection accuracy. To tackle these issues, we propose GD-Det, a low-data object detection model with high accuracy, specifically designed to handle limited sample sizes and low-quality images. The model is primarily composed of three components: (i) A lightweight re-parameterization feature extraction module which integrates RepVGG blocks into multi-concat blocks to enhance the model’s spatial perception and feature diversity during training. Meanwhile, it reduces computational cost in the inference phase through the re-parameterization mechanism. (ii) A cross-scale gather-and-distribute pyramid module, which helps to augment the relationship representation of four-scale features via flexible skip fusion and distribution strategies. (iii) A decoupled prediction module with three branches is to implement classification and regression, enhancing detection accuracy by combining the prediction values from tri-level features. (iv) We also use a domain-adaptive training strategy with knowledge transfer to handle low-data issues. We conducted low-data training and comparison experiments using our constructed dataset AFO-fog. Our model achieved an overall detection accuracy of 84.8%, which is superior to other models. Full article

Research on the multi-field coupling effects in rocks has been ongoing for several decades, encompassing studies on single physical fields as well as two-field (TH, TM, HM) and three-field (THM) couplings. However, the environmental conditions of rock masses in deep resource extraction and underground space development are highly complex. In such settings, rocks are put through thermal-hydrological-mechanical-chemical (THMC) coupling effects under peak temperatures, strong osmotic pressures, extreme stress, and chemically reactive environments. The interaction between these fields is not a simple additive process but rather a dynamic interplay where each field influences the others. This paper provides a comprehensive analysis of fragmentation evolution, deformation mechanics, mechanical constitutive models, and the construction of coupling models under multi-field interactions. Based on rock strength theory, the constitutive models for both multi-field coupling and creep behavior in rocks are developed. The research focus on multi-field coupling varies across industries, reflecting the diverse needs of sectors such as mineral resource extraction, oil and gas production, geothermal energy, water conservancy, hydropower engineering, permafrost engineering, subsurface construction, nuclear waste disposal, and deep energy storage. The coupling of intense stress, fluid flow, temperature, and chemical factors not only triggers interactions between these fields but also alters the physical and mechanical properties of the rocks themselves. Investigating the mechanical behavior of rocks under these conditions is essential for averting accidents and assuring the soundness of engineering projects. Eventually, we discuss vital challenges and future directions in multi-field coupling research, providing valuable insights for engineering applications and addressing allied issues. Full article

Hydrogen energy is considered the most promising clean energy in the 21st century, so hydrogen refuelling stations (HRSs) are crucial facilities for storage and supply. HRSs might experience hydrogen leakage (HL) incidents during their operation. Hydrogen-producing and refuelling integrated stations (HPRISs) could make thermal risks even more prominent than those of HRSs. Considering HL as the target in the HPRIS, through the method of fault tree analysis (FTA) and analytic hierarchy process (AHP), the importance degree and probability importance were appraised to obtain indicators for the weight of accident level. In addition, the influence of HL from storage tanks under ambient wind conditions was analysed using the specific model. Based upon risk analysis of FTA, AHP, and ALOHA, preventive measures were obtained. Through an evaluation of importance degree and probability importance, it was concluded that misoperation, material ageing, inadequate maintenance, and improper design were four dominant factors contributing to accidents. Furthermore, four crucial factors contributing to accidents were identified by the analysis of the weight of the HL event with AHP: heat, misoperation, inadequate maintenance, and valve failure. Combining the causal analysis of FTA with the expert weights from AHP enables the identification of additional crucial factors in risk. The extent of the hazard increased with wind speed, and yet wind direction did not distinctly affect the extent of the risk. However, this did affect the direction in which the risk spreads. It is extremely vital to rationally plan upwind and downwind buildings or structures, equipment, and facilities. The available findings of the research could provide theoretical guidance for the applications and promotion of hydrogen energy in China, as well as for the proactive safety and feasible emergency management of HPRISs. Full article

A new method to discover open-loop, unstable, longitudinal aerodynamic parameters, using a ‘two-stage optimization approach’ for designing optimal inputs, and with an application on the fighter aircraft platform, has been presented. System identification of supersonic aircraft requires formulating optimal inputs due to the extremely limited maneuver time, high angles of attack, restricted flight conditions, and the demand for an enhanced computational effect. A pre-requisite of the parametric model identification is to have a priori aerodynamic parameter estimates, which were acquired using linear regression and Least Squares (LS) estimation, based upon simulated time histories of outputs from heuristic inputs, using an F-16 Flight Dynamic Model (FDM). In the ‘first stage’, discrete-time pseudo-random binary signal (PRBS) inputs were optimized using a minimization algorithm, in accordance with aircraft spectral features and aerodynamic constraints. In the ‘second stage’, an innovative concept of integrating the Fisher Informative Matrix with cost function based upon D-optimality criteria and Crest Factor has been utilized to further optimize the PRBS parameters, such as its frequency, amplitude, order, and periodicity. This unique optimum design also solves the problem of non-convexity, model over-parameterization, and misspecification; these are usually caused by the use of traditional heuristic (doublets and multistep) optimal inputs. After completing the optimal input framework, parameter estimation was performed using Maximum Likelihood Estimation. A performance comparison of four different PRBS inputs was made as part of our investigations. The model performance was validated by using statistical metrics, namely the following: residual analysis, standard errors, t statistics, fit error, and coefficient of determination $({\mathrm{R}}^2)$. Results have shown promising model predictions, with an accuracy of more than 95%, by using a Single Sequence Band-limited PRBS optimum input. This research concludes that, for the identification of the decoupled longitudinal Linear Time Invariant (LTI) aerodynamic model of supersonic aircraft, optimum PRBS shows better results than the traditional frequency sweeps, such as multi-sine, doublets, square waves, and impulse inputs. This work also provides the ability to corroborate control and stability derivatives obtained from Computational Fluid Dynamics (CFD) and wind tunnel testing. This further refines control law design, dynamic analysis, flying qualities assessments, accident investigations, and the subsequent design of an effective ground-based training simulator. Full article

The compressor blades of the gas turbine continually operate under extreme conditions, including elevated temperature, increased pressure, rapid rotation speed, and high-load environments, and are also subjected to complex vibrations, which inevitably lead to performance degradation and failures. Early fault warning based on historical operation data and real-time working conditions can enhance the safety and economy of gas turbines, preventing severe accidents. However, previous studies often faced challenges, such as a lack of fault data, imbalanced datasets, and low data utilization, which limited the accuracy of the algorithms. This study proposes a fault warning technique for gas turbine compressor blades based on AAKR-ACGAN. First, a digital twin model of the gas turbine is constructed using long-term operation data and simulation data from the mechanism model. Then, an auto-associative kernel regression (AAKR) model is used for the fault warning, monitoring multiple parameters to provide effective early warnings of potential faults. Additionally, an auxiliary classifier generative adversarial network (ACGAN) is employed to fully extract hidden data features of the fault points, balance the dataset, and accurately simulate the process of fault occurrence and development. The proposed approach is utilized for the early detection of faults in the compressor blades of a high-capacity gas turbine, and its precision and applicability are confirmed. The multisource early warning indicator can provide an early warning of a failure up to one year in advance of its occurrence. It was also able to detect a severe surge that occurred six months before the failure, which is speculated to be one of the causes that led to the failure. Full article

Shipping activities continue to experience growth across a multitude of industrial sectors within the Arctic, hence there are risks in terms of severity and likelihood of accidents. The Arctic region is inherently dangerous to transportation and human existence due to its extreme climate and environmental conditions, and hence the complexities associated with emergency situations within the maritime domain are amplified when operating within the Arctic and North-Atlantic (ANA). The definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region are significant enablers in providing a set of critical and sustainable tools for Search and Rescue (SAR), Oil Spill Response (OSR), and emergency management practitioners. Therefore, in this paper we aim to identify and characterise high-priority potential seaborne disasters and catastrophic incidents in the ANA region such as cruise ship accidents, oil leaks, radiological leaks, and fishing boat groundings. These were compiled as an outcome of a set of workshops carried out as part of the ARCSAR, EU Horizon 2020 funded project, and from analysis of the literature. We also provide root cause analysis techniques, tools for strategic decision-making, and means of mitigation. We demonstrate how such tools can be used by applying some of them to a selective case study and drawing lessons learned from the application of root cause analysis, which can help emergency response organisations with preparedness work and hence more efficient response. In doing so, we provide a set of tools that can be used for strategic and operational learning. Such approaches can help standardise the definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region in both prospective and retrospective analysis. Full article

Winter weather can cause extremely dangerous road conditions. In order to analyze traffic accidents occurring in winter months in more detail, it is very important to evaluate the slope and the condition of road surfaces together. For this purpose, this study analyzed the accidents that occur during these months in Erzurum, one of the cities in Turkey with long winter months. A total of nine different classes of road conditions were created according to these two factors. In accordance with these classes, the accidents were analyzed using machine learning algorithms, and the success of the classification was analyzed. As a result of the analysis, it was found that the J48 algorithm gave more accurate results. J48 processes both continuous and categorical attributes and is a decision tree algorithm that can effectively manage missing data. According to the results of this algorithm, a map of accident density in the city was created using ArcGIS 10.5 software. Accordingly, it was found that the highest risk of accidents during the winter months occurred on road sections with a slope of more than 6% and covered with ice. Another important result of the study is that the slope of the road is a more effective factor than the surface condition. Full article