Search Results (1,845)

Power cable tunnels are typical enclosed cable-routing spaces in which cable overheating, insulation aging, and partial discharge may gradually develop into fire hazards. During the early stage of cable fires, abnormal sensor responses are often weak, localized, and continuously evolving, which increases the difficulty of early warning based on a single sensor or a single temporal feature. Motivated by cable fire early warning in power cable tunnels, this study uses cable-fire records from a publicly available indoor EN 54 fire-test-room dataset with distributed multi-sensor nodes to evaluate the proposed model under controlled laboratory conditions. In monitoring scenarios with fixed sensor nodes, temporal-only modeling methods often struggle to simultaneously characterize short-term variations, temporal evolution, and spatial differences in node responses. To address this limitation, this study proposes a multi-sensor spatiotemporal feature fusion model that integrates a gated recurrent unit (GRU), a Modern Temporal Convolutional Network (ModernTCN), and an enhanced graph convolutional network (GCN+). The proposed model adopts the ModernTCN as the temporal modeling backbone. A GRU module is introduced at the front end to encode local fluctuations and short-term continuous changes between consecutive time steps, while GCN+ is embedded at the intermediate feature stage of the backbone to model spatial correlations and cross-node coordinated responses among fixed sensor nodes. Experimental results show that the proposed model achieves strong classification performance in the cable fire early warning discrimination task, with a test accuracy of 0.9884 and a false negative rate (FNR) reduced to 0.0150. The comparative experimental results indicate that the proposed model achieves better overall performance than typical temporal baseline models. The ablation study further shows that, under the experimental settings of this study, the introduction of GRU and GCN+ leads to overall improvements in the main evaluation metrics, suggesting that both modules provide a certain enhancement to the cable fire early warning discrimination performance of the ModernTCN backbone. Full article

Rockfalls are natural slope-instability phenomena that pose a significant hazard to infrastructure and human activity. In recent years, the increasing availability of high-resolution three-dimensional (3D) models acquired through photogrammetric techniques has enabled detailed pre-/post-event analyses of rock slopes. However, in this domain, the availability of accurate ground truth for the quantitative evaluation of 3D change detection methods and for training machine-learning approaches aimed at recognising and volumetrically quantifying detachments on rock faces remains very limited. This work presents a simulator that, starting from a 3D model of a rock face, generates pre-/post-failure scenarios through controlled removal of rock blocks and produces photogrammetric acquisitions affected by realistic measurement noise. The pipeline emulates the main processing stages of the reconstruction workflow. Noise realism is validated and calibrated by comparing real and simulated data through a multi-indicator framework (marginal distribution, variogram, power spectrum, and multiscale roughness), integrated into a Mahalanobis-distance-based acceptance test with empirical thresholds derived from real measurements. Results from two pilot sites show that, after site-specific tuning of the simulator noise levels, the calibrated configurations reproduce the main magnitude and spatial-structure characteristics of the real noise, with stronger agreement for the fixed stereo-pair configuration and partial but still informative agreement for the more complex UAV-based case. Moreover, the simulator provides a controlled environment for benchmarking and sensitivity analyses of change detection methods. Full article

Armenia is located within the Arabia–Eurasia collision zone and is exposed to a significant seismic hazard associated with active fault systems capable of generating destructive earthquakes. The 1988 Spitak earthquake highlighted the vulnerability of Armenian urban areas and the need for reliable seismic risk assessment methods. This study presents the first harmonized scenario-based seismic risk assessment framework for six major Armenian cities by integrating seismotectonic source characterization, deterministic ground-motion modeling, locally derived Vs30-based site characterization, GIS-based exposure modeling, and vulnerability assessment within the ELER (Earthquake Loss Estimation Routine) platform. Vulnerability functions were adapted to Armenian building typologies and calibrated using observed damage from the 1988 Spitak earthquake. Deterministic earthquake scenarios (Mw 6.5–7.3) were developed based on the seismic potential of the country’s principal active fault systems. The results reveal substantial spatial variability in seismic risk controlled by differences in ground-motion intensity, local site conditions, building vulnerability, and population exposure. Masonry-dominated urban areas exhibit the highest relative structural losses, whereas Yerevan experiences the greatest absolute losses because of its large population and concentrated building stock. Severe damage and collapse (D4–D5) may affect more than 20–25% of buildings in the most vulnerable cities. Validation against observed 1988 earthquake damage demonstrates the applicability of the proposed framework for seismic risk reduction, emergency preparedness, and long-term urban resilience planning in Armenia. Full article

Numerous studies have examined the contribution of polycyclic aromatic hydrocarbons (PAHs) to soil contamination, resulting from legacy oil extraction and storage activities in the energy sector, and the human health risks. In this research, a human health risk assessment (HHRA) was performed on a site contaminated with crude oil, incorporating spatial analysis to identify hotspots of carcinogenic and non-carcinogenic risk from PAH-contaminated soil. In total, eight soil sampling locations were considered. The HHRA was conducted using a software tool developed by the National University of Science and Technology POLIETHNICA of Bucharest according to the United States Environmental Protection Agency (US EPA) and American Society for Testing and Materials (ASTM International) standards. The results obtained in the current assessment were validated against the Risk-net software 3.2 (an Italian tool for risk assessment validated by the ISPRA). The assessment considered a residential scenario (adult and child receptors) using four different exposure pathways: soil ingestion, dermal contact, vapor inhalation (due to the volatilization process) and groundwater ingestion (because of the leaching process). The carcinogenic risk (CR = 1 × 10⁻⁵) and total hazard index (THI < 1) in the studied area were above the threshold values for both adults’ and children’s receptors. The highest concern is given to the process of PAHs leaching from soil to groundwater. The highest carcinogenic concern is benzo(a)pyrene for children’s exposure via groundwater ingestion (CR = 3.53 × 10⁻⁵), while the highest toxic effect is registered for the contaminant naphthalene (THI = 6.22) for the same receptor and exposure pathway. Full article

Vehicle-to-grid (V2G) integration in commercial building microgrids (CBMGs) offers a promising path for grid support, economic arbitrage, and resilience enhancement. However, practical implementation is hindered by the optimization–execution gap, where high-level aggregated commands fail to match low-level physical charger capacities and individual battery boundaries, as well as by the lack of sociotechnical coupling under extreme weather events, where vehicle owner range anxiety dominates. To address these challenges, a closed-loop multi-timescale energy management system for V2G-enabled CBMGs under exogenous meteorological conditions is proposed. The framework features an integrated four-layer cyber–physical control architecture connecting macroscopic day-ahead scheduling, receding-horizon model predictive control (MPC), discrete real-time parking slot allocation with hardware safety boundary constraints, and equipment-level power flow execution. To handle extreme events, an exogenous meteorological stress index is formulated to quantify ambient structural hazards and temperature deviations, which are then mapped to owner range anxiety and loss-aversion behaviors using prospect theory. Rather than relying on heuristic rule-switching, the optimizer executes a smooth and continuous transition from normal economic peak-shaving to active pre-disaster energy reservation and load demand survival. The cyber–physical system is validated using high-fidelity simulations under typical summer and winter blizzard scenarios. The results demonstrate that the proposed hierarchical architecture successfully eliminates optimization–execution mismatches and guarantees zero load shedding. Furthermore, sensitivity analyses establish the optimal system configuration with the critical defense tolerance of 0.6 and the baseline anxiety ratio of 4, which successfully resolves the trade-off between premature defensive actions and insufficient energy reserves while considering human behavioral uncertainty. Full article

Sudan faces riverine flooding along the Blue Nile and chronic drought across Darfur–Kordofan, yet no national assessment integrates both hazards with social vulnerability to support sustainable and climate-resilient development. This study develops a Societal Impact Index (SII) for Sudan’s eighteen states using a terrain-based flood-susceptibility surface, a drought-frequency indicator (SPEI-12), and thirteen social-vulnerability indicators. These are combined into four weighted pillars following the Intergovernmental Panel on Climate Change (IPCC) risk architecture and validated against independent humanitarian-needs assessments, with convergent checks based on displacement and malnutrition. An unsupervised machine-learning audit, combining k-means clustering with principal component analysis, tests whether the data’s structure supports the composite ranking. The audit shows that the five High-impact states follow two distinct pathways: hazard-and-exposure dominance in Al Qadarif and Al Jazirah, and sensitivity dominance in the remaining three Darfur states. This distinction enables risk-reduction and infrastructure measures to be tailored to the dominant pathway in each state. The first two principal components correlate only weakly with the SII ($r=0.02$ and $r=0.36$), indicating that the ranking reflects the assigned weights as well as the data structure. Each score is exactly decomposed into pillar contributions, improving transparency, while a prototype scenario tool illustrates practical use. Flood-exposed population increased by 7.4 percent between 2017 and 2020, highlighting the need for continuous updating. The reproducible, open-data framework can support equitable resource allocation, sustainable infrastructure planning, long-term vulnerability reduction, and future disaster-resilience digital twins in data-scarce Sahelian settings. Full article

Climate change is altering river-ice regimes across northern basins, with important implications for river hydraulics, infrastructure and flood hazards. In this study, river-ice thickness was simulated at 59 hydrometric stations across the Mackenzie River Basin (MRB) for the period 1980–2024 using a thermodynamically based Stefan ice-growth model driven by daily mean air temperature from the Canadian Surface Reanalysis (CaSR). Cumulative freezing degree-days (CFDD) were derived from 10 km gridded air temperature fields, and site-specific Stefan coefficients (α) were calibrated using measured average ice thickness data from the updated Canadian River Ice Database (CRID). The model reproduced observed river-ice thickness with good accuracy, achieving a median coefficient of determination (R²) of 0.96 across all stations. Basin-wide analysis revealed statistically significant warming in annual and seasonal air temperatures, averaging 0.37 °C decade⁻¹, accompanied by widespread declines in CFDD. These climatic changes translated into widespread reductions in simulated annual maximum river-ice thickness, averaging 1.1 cm decade⁻¹, together with a shift toward earlier peak ice thickness. Sensitivity analyses with uniform air-temperature increases of +1 to +3 °C further indicated average reductions in maximum river-ice thickness of up to 10 cm. Overall, the results demonstrate that the Stefan ice-growth model provides a robust and computationally efficient framework for basin-scale assessment of river-ice thickness and show that river ice across the MRB is already responding to climate warming, with continued thinning expected under projected future warming scenarios. Full article

Forecasting research repeatedly finds that simple methods can match or beat complex ones out of sample. We test this in a safety-critical domain, near-real-time prediction of fire hazard state and structural damage, using a simulator-grounded benchmark: high-fidelity computational fluid dynamics (Fire Dynamics Simulator, FDS) provides reference data, an FDS-calibrated zone model (CFAST) generates a large corpus cheaply, and the temperature trajectories drive a finite-element model (OpenSees) and a HAZUS/Eurocode-informed damage rule. Under one protocol we compare simple, deep (PatchTST, TimesNet), and physics-informed (PINN, PIKAN) forecasters. Complex models do not dominate: a small corpus lets parsimonious models approach best accuracy, and physics helps mainly when data are scarce (crossover near twenty scenarios). We propose CD-PINN, which identifies a data-optimal reduced-order physics residual from the corpus by physics-guided regression over a candidate library and uses it as the physics constraint. This lifts a per-event physics model to the accuracy of corpus-trained forecasters while staying interpretable. On 26 laboratory-fire experiments, however, in-distribution rankings do not transfer: the large accuracy spread collapses to near-parity, so a leaderboard poorly predicts laboratory-fire accuracy. For downstream damage, we further show that the label definition, not the model class, sets the achievable ceiling. Full article

Very large-scale biogas (biomethane) projects are important infrastructure systems for integrating organic waste treatment, renewable energy substitution, and carbon mitigation under China’s carbon peaking and carbon neutrality goals. However, their long process chains, concentrated hazardous media, and frequent confined-space operations create coupled safety risks that may undermine sustainable operation. To identify the dominant risk drivers and safety governance priorities, this study investigated five operating very large-scale biogas projects in Shanxi Province, China. On-site inspections, semi-structured interviews, and document reviews were used to identify ten safety-risk causative factors. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was combined with Interpretive Structural Modeling (ISM) to quantify causal relationships and reveal the hierarchical transmission structure among the factors. The results show a structural imbalance between document-based compliance and operational implementation. Although basic safety documents were generally established, only 20% of the projects had scenario-specific emergency response plans for major accident scenarios; the compliance rate of explosion-proof electrical equipment, the configuration rate of fixed monitoring and alarm systems for combustible and toxic gases, and the effective operation rate of forced ventilation facilities were 40%, 60%, and 40%, respectively. Insufficient enterprise safety investment (M1) and unclear external regulatory responsibilities (M2) were the dominant root causes of system-level risk propagation, while inadequate control of high-risk operations (M9) and unsafe worker behavior (M10) were the final manifestations. A four-pillar governance pathway is proposed, including policy and standard improvement, technological support and equipment upgrading, personnel capacity enhancement, and sustainable funding mechanisms. The findings provide empirical evidence for risk-based supervision and indicate how operational safety governance can serve as an enabling condition for the long-term sustainability of the biomethane industry, rather than as a direct measurement of carbon-mitigation or energy-performance outcomes. Full article

Under the impact of earthquakes, substation systems may experience equipment failures, prolonged functional recovery periods, and expanded power outage consequences. This paper proposes a collaborative optimization framework for enhancing the seismic performance of substation systems and deploying repair resources, aimed at pre-earthquake planning. The system function is characterized by the outage availability of feeders weighted by load and user importance, and the user outage cost is explicitly defined as an economic consequence indicator with monetary units, rather than a dimensionless resilience index. A directed graph model is constructed to simulate the post-earthquake functional recovery process, and under given seismic hazard and vulnerability parameters, Monte Carlo sampling is used to capture the randomness of equipment condition failures and repair durations. Sensitivity analysis is employed to identify critical equipment, and the elitism-preservation and adaptive evolution non-dominated sorting genetic algorithm II (ERA-NSGA-II algorithm), which integrates heuristic initialization, adaptive evolution, and diversity maintenance mechanisms, is proposed to achieve joint optimization of repair teams and equipment reinforcement plans. A typical 220 kV substation case study demonstrates that the framework is feasible under the analyzed scenarios and exhibits better empirical search performance compared to the selected benchmark algorithm. Due to the limitations of a single topology and certain fixed input parameters, the obtained results are scenario-dependent and cannot be used to infer general applicability or global convergence. Full article

To elucidate the influence of operational conditions on the seismic responses of offshore wind turbines (OWTs) and the vibration mitigation efficacy of tuned mass dampers (TMDs) under multi-hazard scenarios, time-domain dynamic analyses are performed for OWT systems subjected to combined wind, wave and seismic excitations. Five typical operational conditions are considered, including cut-in operation, rated-power operation, cut-out shutdown, 1-year return-period extreme shutdown, and 50-year return-period extreme shutdown. The nacelle acceleration and tower-top displacement responses of the uncontrolled structure are comparatively characterized, the peak and root-mean-square (RMS) vibration reduction ratios of the TMD for fore-aft vibrations are quantitatively assessed, and the intrinsic mechanism governing the response discrepancies across operational conditions is elucidated. Numerical results demonstrate that seismic excitation dominates the extreme structural responses of the OWT system. Under the rated-power condition, the peak acceleration and displacement under coupled seismic loading reach 6.90 and 2.19 times the corresponding values under wind–wave loads alone, respectively. Substantial discrepancies in structural responses are observed across operational conditions, with aerodynamic damping magnitude and the spectral properties of hub rotational loads identified as the key influencing factors. The TMD exhibits reliable vibration control performance overall: the optimal control efficacy is achieved under the 1-year return-period shutdown condition, with a peak acceleration reduction ratio of 34.8%—by contrast, its mitigation performance degrades significantly under the 50-year return-period extreme-turbulence condition, with the peak acceleration reduction ratio dropping to merely 15.8%. Full article

Steep rock slopes are widely distributed in the karst mountainous regions of southwestern China, where structurally controlled rockfalls frequently threaten transportation infrastructure and human safety. Accurate identification of unstable rock blocks (URs) and quantitative assessment of their post-failure hazards remain major challenges because of complex discontinuity networks and fragmentation during rockfall motion. Taking the Zuojiaying steep rock slope in Guizhou Province as a representative case, this study integrates high-resolution UAV photogrammetry, automatic discontinuity identification, unstable rock block detection, and three-dimensional rockfall simulation to investigate the formation mechanisms and hazard characteristics of discontinuity-controlled rockfalls. A high-resolution three-dimensional terrain model was reconstructed from UAV imagery, and six dominant discontinuity sets were automatically identified using the I-MinPts-constrained DBSCAN algorithm. Combined with the Rock Occurrence Kinematic Analysis (ROKA) algorithm and Block Theory, 54 unstable rock blocks were identified, with wedge failure and toppling failure representing the dominant instability modes. The results indicate that discontinuity combinations govern both rock mass segmentation and unstable rock block geometry. Specifically, discontinuity sets J1, J3, and J5 mainly control wedge-shaped blocks, and J2 and J4 dominate columnar toppling blocks, whereas J6 further promotes the formation of isolated unstable rock blocks. Three-dimensional RockGIS simulations considering fragmentation reproduced the complete rockfall process from detachment to final deposition. The maximum travel distance, kinetic energy, and bounce height reached 395 m, 748.5 kJ, and 40.1 m, respectively. Fragmentation increased the number of rock blocks from 54 to 1013, substantially enlarging the potential impact area. A raster-based Rockfall Hazard Index (RHI) further revealed that the middle–lower slope and slope toe constitute the principal high-hazard zones, and under extreme scenarios, high-energy fragments may reach the G246 National Highway and adjacent infrastructure. This study revealed the formation mechanisms and hazard characteristics of unstable rock blocks controlled by discontinuity combinations in the study area, providing a case reference for rockfall hazard identification and mitigation on similar high-steep rock slopes in karst mountainous regions. Full article

Indoor positioning identifies location but does not directly indicate whether a route remains passable, how hazard exposure changes, or which alternative should be considered under deteriorating fire conditions. As a result, a geometrically shorter route may still be selected despite greater hazard exposure, blockage, or positioning uncertainty. This study proposes a dynamic hazard-aware route-risk assessment model based on Fire Positioning Infrastructure Theory (FPIT) for fireground decision support in building fires. The model converts BIM/IFC spatial semantics into a computable graph, maps normalized hazard scenario data onto graph edges, excludes edges exceeding scenario-specific hazard or blockage criteria, and evaluates the remaining feasible routes using an integrated route-risk score, hazard exposure, travel time, and positioning uncertainty. A normalized illustrative computational demonstration showed that the conventional shortest route had the lowest travel time but higher route-risk score, hazard exposure, and positioning uncertainty. The FPIT-based lower-route-risk-score alternative had lower values for these indicators but required longer travel time, while the intermediate detour provided a compromise. Pareto comparison retained the three feasible routes as non-dominated alternatives with different score–time–uncertainty characteristics. The computational demonstration illustrates the model’s internal calculability, traceability, comparability, and ability to represent route trade-offs; it does not constitute empirical validation or evidence of operational effectiveness in actual fireground environments. Full article

This paper introduces the Complementary Asymmetric-Odds Weibull (CAO–W) distribution, a new three-parameter distribution that improves the accuracy of modeling lifetime data with complex behavior. The proposed distribution is based on an asymmetric transformation that combines the baseline distribution with its complement, providing more flexibility when modeling a variety of hazard patterns, such as increasing, decreasing, and bathtub-shaped. Some statistical properties of the CAO–W distribution were studied including a mathematical and graphical analysis of the hazard rate function (HRF). The model parameters were estimated using four widely used estimation approaches: maximum likelihood (ML), least squares (LS), maximum product of spacings (MPS), and the Cramér-von-Mises (CVM). The efficiency of these approaches in estimating the model parameters was investigated through simulation studies under different scenarios. Moreover, the proposed distribution was applied to four real datasets, and compared to some flexible distributions to demonstrate its ability to provide a good fit for lifetime data in survival and reliability analysis applications. Finally, a change point analysis, based on the Minimum Information Criterion (MIC), is also conducted to highlight the flexibility of the proposed model. Full article

Methane emissions from coal mines pose significant environmental and operational challenges. Methane can be released from coal seams and surrounding rock layers as a result of mining operations. These emissions pose environmental risks and can lead to fire and explosion hazards. Therefore, reducing coal mine methane emissions is essential for both protecting miners’ safety and cutting greenhouse gas emissions. Additionally, capturing methane before it escapes into the atmosphere can be economically beneficial and used as a valuable energy source. This study proposes an integrated approach that combines advanced methane capture and hydrogen production technologies to enhance both environmental performance and energy recovery in coal mining operations. By combining methane capture with hydrogen production, the study presents a practical solution for lowering greenhouse gas emissions in the coal sector. This strategy promotes the adoption of low-carbon energy sources and offers a sustainable path forward for coal-dependent regions facing decarbonization challenges. A scenario-based techno-economic analysis is presented, including investment and operating costs, hydrogen yield, energy generation potential, and greenhouse gas mitigation. Further research should focus on process optimization, the integration of carbon capture technologies, and the valorization of by-products to further reduce the environmental footprint. Full article