Search Results (223)

Designing long-span cable-stayed bridges in high seismic hazard zones presents significant challenges due to their flexible structural systems, the influence of multi-support excitation, and the need to control large displacements while limiting seismic demands on critical components. These difficulties are further amplified in regions with complex geology and for bridges required to maintain high levels of post-earthquake serviceability. This study develops a low-damage seismic design approach for long-span cable-stayed bridges and demonstrates its application in the New Panama Canal Bridge. Probabilistic seismic hazard assessment and site response analyses are performed to generate spatially varying ground motions at the pylons and side piers. The pylons adopt a reinforced concrete configuration with embedded steel stiffeners for anchorage, forming a composite zone capable of efficiently transferring concentrated stay-cable forces. The lightweight main girder consists of a lattice-type steel framework connected to a high-strength reinforced concrete deck slab, providing both rigidity and structural efficiency. A coordinated girder–pylon restraint system—comprising vertical bearings, fuse-type restrainers, and viscous dampers—ensures controlled stiffness and effective energy dissipation. Nonlinear seismic analyses show that displacements of the girder remain well controlled under the Safety Evaluation Earthquake, and the dampers and bearings exhibit stable hysteretic behaviours. Cable tensions remain within 500–850 MPa, meeting minimal-damage performance criteria. Overall, the results demonstrate that low-damage seismic performance targets are achievable and that the proposed design approach enhances structural control and seismic resilience in long-span cable-stayed bridges. Full article

Coal mine ventilation systems face coupled and systemic risks characterized by structural interconnection and disaster chain propagation. In order to accurately quantify and evaluate this overall system risk, this study presents a new method of risk assessment of the coal mine ventilation system based on fuzzy polymorphic Bayesian networks. This method effectively addresses the shortcomings of traditional assessment approaches in the probabilistic quantification of risk. A Bayesian network with 44 nodes was established from five dimensions: ventilation power, ventilation network, ventilation facilities, human and management factors, and work environment. The risk states were divided into multiple states based on the As Low As Reasonably Practicable (ALARP) metric. The probabilities of evaluation-type root nodes were calculated using fuzzy evaluation, and the subjective bias was corrected by introducing a reliability coefficient. The concept of distance compensation is proposed to flexibly calculate the probabilities of quantitative-type root nodes. Through the verification of the ventilation system of H Coal Mine in Shanxi, China, it is concluded that the high risk of the ventilation system is 18%, and the high-risk probability of the ventilation system caused by the external air leakage of the mine is the largest. The evaluation results are consistent with real-world conditions. The results can provide a reference for improving the safety of the ventilation systems. Full article

Low-level wind shear (LLWS) is a critical aviation hazard that can cause flight disruptions and pose significant safety risks. Despite its operational importance, forecasting LLWS remains a challenging task. To improve LLWS prediction, probabilistic forecasting approaches based on ensemble prediction systems are increasingly used. In this study, LLWS forecasts were generated using a high-resolution, limited-area ensemble model, which allows for the representation of forecast uncertainty and variability in atmospheric conditions. Forecasts for Incheon International Airport were generated twice daily over the period from December 2018 to February 2020. To enhance forecast skill, statistical post-processing techniques, specifically Ensemble Model Output Statistics (EMOS), were applied and calibrated using Aircraft Meteorological Data Relay (AMDAR) observations. Prior to calibration, rank histograms were examined to assess the reliability and distributional consistency of the ensemble forecasts. Forecast performance was evaluated using commonly applied probabilistic verification metrics, including the mean absolute error (MAE), the continuous ranked probability score (CRPS), and probability integral transform (PIT). The results indicate that ensemble forecasts adjusted through statistical post-processing generally provide more reliable and accurate predictions than the unprocessed raw ensemble outputs. Full article

This paper presents a quantitative framework for assessing and managing public-safety risks around dams. The framework integrates a hazard–event–objective–control structure with the Analytic Hierarchy Process (AHP) to transform qualitative judgments into quantitative risk measures. Likelihoods, consequences, and overall risk are expressed on a ratio scale, allowing results to be aggregated, compared, and communicated in monetary terms. Probabilistic simulation accounts for uncertainty and generates outputs such as Value-at-Risk (VaR), loss-exceedance curves, and societal F–N charts, providing a clear picture of both expected and extreme outcomes. Optimization identifies control portfolios that achieve the greatest risk reduction for available budgets. A hypothetical dam case study demonstrates the framework’s application and highlights its ability to identify high-value safety investments. The framework offers dam owners and regulators a transparent, data-driven basis for prioritizing public-safety improvements and supports both facility-level (micro) and program-level (macro) decision-making consistent with international risk-tolerability and ALARP principles. Full article

Monitoring the stability of hydraulic structures such as dams and reservoirs in seismically active regions is essential for ensuring their safety and operational reliability. This study presents a comprehensive geospatial approach combining lineament analysis and geodynamic zoning to assess the structural stability of the Voroshilov and Priyut reservoirs located in the Almaty region, Kazakhstan. A regional lineament map was generated using ASTER GDEM data, while ALOS PALSAR data were used for detailed local analysis. Lineaments were extracted and analyzed through automated processing in PCI Geomatica. Lineament density maps and azimuthal rose diagrams were constructed to identify zones of tectonic weakness and assess regional structural patterns. Integration of lineament density, GPS velocity fields, InSAR deformation data, and probabilistic seismic hazard maps enabled the development of a detailed geodynamic zoning model. Results show that the studied sites are located within zones of low local geodynamic activity, with lineament densities of 0.8–1.2 km/km², significantly lower than regional averages of 3–4 km/km². GPS velocities in the area do not exceed 4 mm/year, and InSAR analysis indicates minimal surface deformation (<5 mm/year). Despite this apparent local stability, the 2024 Voroshilov Dam failure highlights the cumulative effect of regional seismic stresses (PGA up to 0.9 g) and localized filtration along fracture zones as critical risk factors. The proposed geodynamic zoning correctly identified the site as structurally stable under normal conditions but indicates that even low-activity zones are vulnerable under cumulative seismic loading. This demonstrates that an integrated approach combining remote sensing, geodetic, and seismic data can provide quantitative assessments for dam safety, predict potential high-risk zones, and support preventive monitoring in tectonically active regions. Full article

Over the last 15 years, mixture risk assessment for food xenobiotics has evolved from conceptual discussions and simple screening tools, such as the Hazard Index (HI), towards operational, component-based and probabilistic frameworks embedded in major food-safety institutions. This review synthesizes methodological and regulatory advances in cumulative risk assessment for dietary “cocktails” of pesticides, contaminants and other xenobiotics, with a specific focus on food-relevant exposure scenarios. At the toxicological level, the field is now anchored in concentration/dose addition as the default model for similarly acting chemicals, supported by extensive experimental evidence that most environmental mixtures behave approximately dose-additively at low effect levels. Building on this paradigm, a portfolio of quantitative metrics has been developed to operationalize component-based mixture assessment: HI as a conservative screening anchor; Relative Potency Factors (RPF) and Toxic Equivalents (TEQ) to express doses within cumulative assessment groups; the Maximum Cumulative Ratio (MCR) to diagnose whether risk is dominated by one or several components; and the combined Margin of Exposure (MOET) as a point-of-departure-based integrator that avoids compounding uncertainty factors. Regulatory frameworks developed by EFSA, the U.S. EPA and FAO/WHO converge on tiered assessment schemes, biologically informed grouping of chemicals and dose addition as the default model for similarly acting substances, while differing in scope, data infrastructure and legal embedding. Implementation in food safety critically depends on robust exposure data streams. Total Diet Studies provide population-level, “as eaten” exposure estimates through harmonized food-list construction, home-style preparation and composite sampling, and are increasingly combined with conventional monitoring. In parallel, human biomonitoring quantifies internal exposure to diet-related xenobiotics such as PFAS, phthalates, bisphenols and mycotoxins, embedding mixture assessment within a dietary-exposome perspective. Across these developments, structured uncertainty analysis and decision-oriented communication have become indispensable. By integrating advances in toxicology, exposure science and regulatory practice, this review outlines a coherent, tiered and uncertainty-aware framework for assessing real-world dietary mixtures of xenobiotics, and identifies priorities for future work, including mechanistically and data-driven grouping strategies, expanded use of physiologically based pharmacokinetic modelling and refined mixture-sensitive indicators to support public-health decision-making. Full article

Soil contamination by heavy metals (HMs) threatens crop productivity, food safety, and ecosystem health, especially in intensively cultivated Mediterranean regions. This study investigated the influence of soil HM contamination on nutrient uptake, photosynthetic traits, and metal bioaccumulation in avocado (Persea americana Mill.) orchards. Twenty orchard sites were sampled, collecting paired soil and mature leaf samples. Soil physicochemical properties and HM concentrations were determined, while leaves were analyzed for macro- and micronutrients, photosynthetic pigments, and metal contents. Bioaccumulation Factors (BAFs) were computed, and multivariate analyses (Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR)) were applied to assess soil–plant relationships, complemented by Monte Carlo simulations to quantify probabilistic contamination risks. Results revealed substantial inter-site variability, with leaf Cd and Pb concentrations reaching 0.92 and 3.54 mg/kg, and BAF values exceeding 1 in several orchards. PLSR models effectively predicted leaf Cd (R² = 0.789) and Pb (R² = 0.772) from soil parameters. Monte Carlo simulations indicated 15–25% exceedance of FAO/WHO safety limits for Cd and Pb. These findings demonstrate that soil metal accumulation substantially alters avocado nutrient balance and photosynthetic efficiency, highlighting the urgent need for site-specific soil monitoring and sustainable remediation strategies in contaminated orchards. Full article

In response to the relevant provisions of safety design criteria for fuze, and considering that Traditional Fault Tree Analysis (TFTA) struggles to describe system failure behavior, such as in its multi-state system faults and probabilistic logic linkages among components, this paper proposed a method for analyzing fuze system failure based on the integration of T-S Fuzzy Fault Tree (T-SFFT) and Bayesian Network (BN), introducing an interval triangular fuzzy subset method for describing failure rates in the safety assessment of the fuze system. Taking the fault tree of the fuze function prior to the initiation of the ordained arming and safety-interruption sequence as an example, using this approach, the analysis and calculation results indicated that the fuzzy subsets of failure probability for the top event under the complete failure state of the fuze system were of the same order of magnitude as those obtained using the TFTA method. This therefore validated the feasibility and effectiveness of this method in fuze system safety assessment. Furthermore, using BN to obtain the posterior probabilities of nodes, this approach provided a data foundation for fuze system fault diagnosis, holding significant engineering significance for fuze system safety assessment. Full article

The reassessment of structural capacity is critical to maintain the safety, serviceability, and sustainability of ageing civil engineering infrastructure. Structural Health Monitoring (SHM) allows in situ measurements to be incorporated into structural models, updating the performance and reliability estimation based on available information. Digital Twins can be used to capture the behaviour of the system in the real world as live data and make use of rich sensorial data flow from the structural system. However, the growing complexity of multi-domain models, as well as decision-making and stakeholder interactions, makes it necessary to implement a structured modelling framework. This paper proposes a Model-Based Systems Engineering (MBSE) framework that incorporates an MBSE layer to coordinate model dependencies, correlate important parameters, and enforce traceability between measurement data, probabilistic assessment, and decision-making. Illustrated with a prototype application to an idealised case study of a bridge, this paper describes how using MBSE as a scalable, adaptive, and comprehensive framework can help enable data-driven structural reassessment. The work illustrates that MBSE can be used in civil engineering processes across multi-disciplinary departments to benefit the system lifecycle over time and identifies areas of further research required before the approach can be adopted for large-scale, real-world infrastructure. Full article

In response to the challenges posed by the complex evolution of risks and the static nature of traditional assessment methods during the construction of long-span prestressed concrete (PC) continuous rigid-frame bridges, this study proposes a risk assessment framework that integrates visual perception with dynamic probabilistic reasoning. By combining an improved YOLOv8 model with the Decision-making Trial and Evaluation Laboratory–InterpretiveStructure Modeling (DEMATEL–ISM) algorithm, the framework achieves intelligent identification of risk elements and causal structure modelling. On this basis, a dynamic Bayesian network (DBN) is constructed, incorporating a sliding window and forgetting factor mechanism to enable adaptive updating of conditional probability tables. Using the Tongshun River Bridge as a case study, at the identification layer, we refine onsite targets into 14 risk elements (F1–F14). For visualization, these are aggregated into four categories—“Bridge, Person, Machine, Environment”—to enhance readability. In the methodology layer, leveraging causal a priori information provided by DEMATEL–ISM, risk elements are mapped to scenario probabilities, enabling scenario-level risk assessment and grading. This establishes a traceable closed-loop process from “elements” to “scenarios.” The results demonstrate that the proposed approach effectively identifies key risk chains within the “human–machine–environment–bridge” system, revealing phase-specific peaks in human-related risks and cumulative increases in structural and environmental risks. The particle filter and Monte Carlo prediction outputs generate short-term risk evolution curves with confidence intervals, facilitating the quantitative classification of risk levels. Overall, this vision-guided dynamic risk assessment method significantly enhances the real-time responsiveness, interpretability, and foresight of bridge construction safety management and provides a promising pathway for proactive risk control in complex engineering environments. Full article

Failure Mode and Effects Analysis (FMEA) is a systematic risk assessment tool that effectively evaluates the safety and reliability of products prior to their deployment. However, traditional FMEA fails to consider and leverage inherent system-specific information during risk assessment, while also neglecting the weights of risk factors (RFs) when processing data related to the Risk Priority Number (RPN). This leads to significant subjectivity in the final risk ranking of failure modes. To overcome these drawbacks, this study proposes an improved FMEA risk assessment method based on load sharing, aiming to develop an improved FMEA method that addresses the critical limitations of traditional approaches by integrating load sharing principles and systematic weight determination, thereby enhancing risk assessment objectivity and accuracy in complex multi-component systems. First, probabilistic linguistic terms are adopted to quantify experts’ risk assessment information, and the geometric mean method is then used to aggregate assessments from multiple experts. Second, the Fuzzy Best–Worst Method (FBWM) is employed to determine the relative weights of the three RPN factors (Occurrence, Severity, and Detection). Additionally, partial system structural data are obtained through load sharing, and these data—combined with the calculated factor weights—are integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate the final risk ranking of failure modes. Finally, a case study of a magnetic crane is conducted to verify the feasibility and effectiveness of the proposed method, supplemented by comparative experiments to demonstrate its superiority. Full article

The structural performance of mid-rise buildings with a soft first story is a critical issue in earthquake-prone regions. This paper presents a detailed assessment of both the seismic performance and the structural reliability of a confined masonry mid-rise building with a soft reinforced-concrete first-story irregularity located in Mexico. This structure was designed according to outdated building codes to reflect construction practices that remain common in some parts of the country. Nonlinear dynamic analyses were conducted using ETABS v21. To simulate various seismic scenarios, ground motion records associated with return periods of 72, 475, and 975 years, respectively, were implemented. The results demonstrated that maximum inter-story drift is predominantly concentrated at the first story, exceeding the performance thresholds for immediate occupancy, life safety, and collapse prevention. Furthermore, a probabilistic performance assessment was developed considering the randomness of inter-story drift responses. Then, reliability index (β) was calculated for each seismic scenario. In all cases, β values remained consistently below the minimum recommended limit. These findings confirm the formation of a soft-story mechanism at the first level and are relevant for buildings designed under construction provisions like those used in the present case study. Full article

Uncertainty and variability in soil properties strongly impact slope stability under extreme rainfall. This study applies a probabilistic hydro-mechanical slope stability assessment to unsaturated volcanic hillslopes in southern Guam, covering a range of slope angles and subjected to four major 2023 typhoons. The slope scenarios analyzed include bare slopes, vegetated slopes with root water uptake, and vetiver with both uptake and root reinforcement. Laboratory-derived variability in effective cohesion, friction angle, and unit weight was incorporated via Latin hypercube sampling. Gentler slopes (≤40°) remained stable with a probability of failure (PoF) = 0%. For steep slopes (45–60°), vetiver root reinforcement improved the mean factor of safety by up to 12–15% and reduced variability in outcomes to less than 2%. Probabilistic predictions advanced failure timing compared to deterministic estimates, with differences more pronounced on steeper slopes. By integrating soil variability and vegetation effects within probabilistic frameworks, this approach provides a more accurate and comprehensive assessment of tropical slope failure risks, thereby informing more effective and resilient slope management strategies. Full article

Mushroom production and economic value on a global scale are significantly increasing. On the other hand, food safety has raised concerns; however, limited research exists on the presence of plant growth regulator (PGR) residues in edible mushrooms. Herein, this study appears to be the first to comprehensively investigate PGR residual characteristics and assess their associated dietary exposure risks to consumers. A total of 105 edible mushroom samples of seven different varieties were analyzed, and the overall detection rate was 81%. The residual level of PGRs ranged from below the limit of detection to 6.308 mg/kg. Among varieties, 100% of A. aegerita, T. fuciformis Berk, and H. erinaceus samples contained at least one PGR residue. Dietary exposure risks were assessed using both deterministic and probabilistic approaches. Calculated values of both %ADI (acceptable daily intake) and %ARfD (acute reference dose)were below 100 and do not indicate a potential health concern with respect to edible mushroom consumption. However, several PGRs had a relatively high %ADI or %ARfD value, suggesting that the Maximum Residual Limits (MRLs) and associated regulatory norms should be immediately established. This work not only provides valuable information for edible mushroom consumers but also an important reference for the risk management decision. Full article

Lithium-ion batteries serve as critical energy storage units for electric vehicles, unmanned aerial vehicles, and other emerging transportation systems. Numerous real-world incidents have demonstrated that thermal runaway (TR) remains a predominant cause of spontaneous combustion in these applications. Concerns over TR risks have significantly hindered broader adoption of lithium-ion batteries. While existing research predominantly focuses on battery heat generation mechanisms, TR initiation processes, and advanced materials with enhanced safety, limited attention has been paid to TR risk evolution induced by cycle-induced performance degradation. To address this gap, this study proposes a data-driven prognostic framework for quantifying TR risks under battery aging scenarios. Leveraging the Open Access XJTU Battery Dataset, we first identify eight degradation-sensitive parameters (including mean current, current standard deviation, and charging time, etc.) by analyzing temporal degradation patterns within characteristic segments of charging curves. These parameters are then fused into a composite degradation index through Physics-Informed Neural Networks (PINNs). Recognizing the stochastic nature of both degradation trajectories and TR-triggering stresses, a Wiener process-based random failure threshold model is developed to probabilistically predict TR risks under time-varying operational conditions. The proposed methodology enables quantitative risk assessment throughout battery service life, offering a novel perspective for aging-aware battery safety management. Full article