Engineering

Abstract: Fast charging of lithium-ion batteries is essential for accelerating a widespread use of electric vehicles; however, its adoption significantly increases battery thermal stress and the risk of thermal runaway, particularly in aged cells. This study proposes a sim-ulation-trained digital twin (DT) framework for probabilistic assessment of thermal runaway and critical charging current estimation under fast charging conditions. A dataset is generated using an electrochemical–thermal Single Particle model, varying current rate, capacity, and internal resistance, then, an encoder–decoder neural net-work architecture is developed to map and convert static operating conditions into dynamic temperature evolution, enabling efficient surrogate modeling of thermal be-havior. The proposed methodology provides a computationally efficient tool for risk-aware fast-charging strategies which can be integrated into battery management systems for enhanced safety. While the current study is applied to specific single cell chemistry and simulation-based training, the framework can be easily extended to other battery systems and operating conditions.

Abstract: Road tunnel ventilation systems have traditionally been designed to dilute vehicle-generated pollutants and also control smoke during fires. However, the thermal environment including temperature and humidity is not the variable taken into consideration. Despite the operation of its ventilation system, Boryeong Subsea Tunnel (6.9 km), the longest subsea road tunnel in Korea, has experienced severe condensation since its opening in December 2021. As hot, humid ambient air enters the tunnel and meets wall surfaces cooled by seawater and the surrounding ground, condensation and fog may form, reducing visibility. To investigate the causes of condensation and develop a decision-making tool for prediction, a variety of tasks had been carried out : (1) field measurements of temperature, humidity, tunnel wall temperature, and tunnel air velocity; (2) development of a 1D model for condensation rate quantification; and (3) 3D CFD simulations. Condensation occurred mainly from June to September, with the most severe conditions in July and August. Both the 1D model analysis and the CFD simulations showed good agreement with field measurement data, with wall temperature errors within 7.3%. Under current traffic conditions (peak approximately 250 veh/h), the annual condensation volume was estimated at approximately 12,415 ton/year. Under the design traffic volume (1,550 veh/h), heat from vehicles was found to effectively suppress condensation. The Condensation Contour Map (CCM) was developed as a decision-support tool to predict the likelihood and quantity of condensation based on tunnel air temperature and humidity conditions. The results of this study clearly imply that condensation should be explicitly considered in the design and operation of long subsea road tunnels.

Abstract: Managing safety and operational efficiency in large-scale events requires tools capable of capturing complex crowd dynamics while supporting rapid and informed decision-making. This paper presents a Generative AI-powered digital twin framework that integrates agent-based crowd simulation, an API-based execution pipeline, and a Large Language Model (LLM)-driven conversational interface within a unified system. The proposed approach enables dynamic configuration, execution, and analysis of crowd scenarios under varying operational conditions, including high-demand and emergency evacuation contexts. Experimental results demonstrate the system’s ability to reproduce nonlinear crowd dynamics, detect congestion patterns, and evaluate evacuation performance, providing actionable insights for planning and safety assessment. A key contribution lies in the introduction of an API-based execution paradigm that exposes the full simulation lifecycle (configuration, validation, execution, and output retrieval) through programmatic interfaces, enabling reproducible and scalable what-if analysis. Additionally, the integration of an LLM-based conversational interface allows non-technical users to interact with complex simulation models through natural language, significantly improving accessibility and usability. The framework is validated through a TRL-4 prototype, demonstrating robust performance, scalability, and interaction reliability. Overall, the proposed system advances digital twins from static analytical tools to executable, interactive, and user-centric platforms for decision support in complex urban environments.

Abstract: Critical ventilation velocity is crucial for smoke control in tunnel fires, yet its behavior in tunnels with unconventional cross-sections remains inadequately quantified. This study numerically investigates the critical velocity in a full-scale, 1000-m-long semi-circular tunnel using Fire Dynamics Simulator (FDS). A systematic parametric analysis was conducted to evaluate the effects of fire heat release rate (HRR, 4-10 MW), cross-sectional geometry (semi-circular vs. three arched sections of equal area), and longitudinal slope (-1% to +2%). The critical velocity was determined using a successive approximation method, validated against multi-criteria safety thresholds including smoke back-layering length, upstream temperature, and visibility height. Results demonstrate that HRR is the dominant factor, with critical velocity increasing from 2.2 to 2.7 m/s. More importantly, cross-sectional shape exhibits a significant, non-monotonic influence; the streamlined semi-circular arch requires a lower critical velocity (2.2 m/s) compared to arched sections (2.4-2.6 m/s) of the same area, attributed to reduced flow resistance and a more coherent ceiling jet. Within the studied range, the effect of slope is minor compared to HRR and geometry, showing only a slight decrease in critical velocity for uphill gradients. These findings provide quantitative insights into optimizing ventilation design for semi-circular tunnels, highlighting that an aerodynamically favorable shape can reduce the required longitudinal airflow, thus balancing safety and energy efficiency.

Abstract: Background: Perception-based evaluation using Likert-scale survey data is widely applied in tourism and transport research, yet conventional point-valued encoding imposes artificial precision and overlooks ambiguity between adjacent ordinal categories. This limitation is particularly relevant in experiential contexts, where subjective judgments often involve transitional evaluations. Methods: This study develops a parameterized fuzzy–entropy exploratory factor analysis (FE-EFA) framework for uncertainty-aware analysis of ordinal perception data. The approach transforms ordinal responses into fuzzy membership distributions, constructs a correlation structure in membership space, and incorporates Shannon entropy and Jensen–Shannon divergence to characterize distributional dispersion and representation differences. The framework is applied to survey data from Chengdu Tramway Line 2 (N = 1242; 32 indicators). Results: Under the Kaiser criterion (eigenvalues > 1), conventional EFA yields a seven-factor structure, whereas FE-EFA identifies an additional eighth factor located near the retention boundary. Under a unified factor specification, both approaches preserve a consistent high-level structure, while FE-EFA shows clearer factor separation, fewer cross-loadings, and more coherent indicator clustering. From an information-theoretic perspective, FE-EFA produces higher entropy (average = 0.8688) and moderate Jensen–Shannon divergence (average = 0.0133), indicating a controlled redistribution of ordinal information rather than structural distortion. Entropy-informed weighting further reveals systematic shifts in indicator importance across key dimensions. Conclusions: The FE-EFA framework extends conventional Likert-scale analysis by introducing an uncertainty-aware representation layer prior to factor extraction. It preserves overall structural stability while improving the resolution of latent constructs and the sensitivity of indicator representation. The proposed approach provides a practical and theoretically grounded basis for perception-based evaluation and decision support in tramway cultural-tourism development and related contexts.

Abstract: Error-proof prediction is currently a major interest in machine learning (ML) based-gas turbine (GT) failure prognostics applications, indicated by the rise of probabilistic, ensemble, Physics-informed, and explainable AI (XAI) models. For effective maintenance planning, it is important to validate the existence of degradation during life assessment. However, probabilistic and ensemble models can only confirm anomaly which does not necessarily point to degradation while Physics-informed models sometimes work poorly on actual data due to limitations of physics models. XAI can make ML model transparent to confirm the presence of degradation. Existing XAI-based GT prognostics works however suffer from the lack of uncertainty quantification, making it hard to evaluate the prediction trustworthiness. Subsequently, false explanation, which misguides maintenance decision making, risked being generated. In this work, a transparent machine learning (ML) model that predicts and justifies gas turbine’s remaining useful life (RUL) prediction is developed, evaluated and validated using fouling failure created from thermodynamic modelling. Specifically, a Bayesian ML model incorporated with XAI capability was employed to estimate the RUL of a twinshaft GT. Thermodynamic modelling was conducted on actual GT data and compressor fouling was injected to create failure data. The uncertainty and trend from the ML prediction and the generated XAI explanation were compared with baseline uncertainty level and explanation to confirm anomaly occurrence to support RUL prediction. The life estimation and explanation were used next to determine the defective component. The model predicted MAPE metric to be 18.04% in a multi-step ahead, long term forecasting horizon. The predictions are supported by the uncertainty level of 0.146 and 0.147 for partial and failure data respectively which is higher than the baseline level of 0.022 that implies anomaly. The prediction and explanations match the thermodynamic modelling which points to compressor failure.

Abstract: In recent years, the rapid expansion of low-temperature facilities—such as cold storage and indoor ice and snow venues—has underscored their pronounced vulnerability to fire, as evidenced by multiple severe incidents. Due to their distinct environmental conditions, existing theoretical frameworks, technical approaches, and standards exhibit limited applicability. Consequently, the fire risk characteristics of such facilities remain insufficiently defined, and systematic methods for hazard identification and assessment are lacking. This study conducts a detailed analysis of fire incident data from representative low-temperature facilities to identify the fire risks characteristics across all lifecycle stages, including construction, renovation and expansion, operation, maintenance, and demolition. An integrated framework combining the WBS/RBS matrix and CN methods is then proposed to establish a structured methodology for full lifecycle fire hazard identification and classification. The results address critical gaps, including the absence of clearly defined lifecycle fire risk profiles and a robust scientific basis for hazard identification, and provide a technical foundation for lifecycle fire risk management in low-temperature facilities.

Abstract: Hydrogen-blended natural gas (HBNG) is widely regarded as a transitional option for decarbonizing urban gas systems. However, the coupled evolution from buried pipeline leakage to pre-ignition flammable cloud formation remains poorly integrated across research stages. This review synthesizes experimental, numerical, and data-driven studies on the sequential processes of leak source-term dynamics, subsurface migration through porous media, surface breakthrough and escape, accumulation in semi-enclosed spaces, and pre-ignition flammable cloud development. Existing studies indicate that hydrogen blending alters the density, diffusivity, flammability limits, and ignition sensitivity of the gas mixture, thereby affecting the breakthrough time, stratification behavior, and pre-ignition early warning windows. The hazard evolution is jointly governed by pipeline pressure, leak orifice size, burial depth, soil heterogeneity, soil moisture content, spatial confinement, and ventilation conditions. Six major knowledge gaps are identified: the fragmentation of physical evolution stages in current research, the lack of full-scale multi-physics coupled experimental datasets, insufficient characterization of in-situ heterogeneous soil conditions, bottlenecks in high-resolution transient gas cloud measurement, inadequate integration of mechanistic findings into quantitative risk assessment frameworks, and the lag in full-lifecycle integrity management of hydrogen-blended pipeline networks. Based on the identified gaps, this review proposes a coherent, mechanism-informed analytical framework for urban HBNG pipeline safety. This framework emphasizes the incorporation of dynamic mechanistic parameters into high-consequence area zoning, sensor placement, ventilation interlocking, and full-lifecycle integrity management, thereby supporting safer engineering deployment.

Abstract: Tsunami fragility modeling plays a central role in probabilistic coastal risk assessment; however, representing structural vulnerability under near-field tsunami conditions remains challenging due to complex hydrodynamic loading, strong spatial variability, and the presence of pre-existing earthquake damage. This paper provides a compre-hensive review and synthesis of current approaches for modeling near-field tsunami impacts on infrastructure, with a particular focus on bridging simulation-based meth-ods and empirical damage survey observations. The discussion highlights how succes-sive hazard simulations can be used to capture coupled earthquake–tsunami effects, while damage surveys offer critical insights into observed relationships between structural damage, hydrodynamic intensity measures, and spatial characteristics such as coastal proximity. Special attention is given to the role of momentum flux as a physically meaningful predictor of damage and to the systematic differences between near-field and far-field fragilities. Building on these insights, the paper outlines practical strategies for adapting baseline fragility relationships to near-field conditions, including the use of spatially dependent intensity adjustments informed by empirical data. Rather than proposing a single methodology, this work aims to provide a structured perspec-tive on existing knowledge and to guide researchers and practitioners in developing more physically consistent and data-informed fragility models for near-field tsunami risk and resilience assessments.

Abstract: Rapid integration of UAVs into multiple sectors involving military, commercial, and civilian applications introduces new operational capabilities but also raises critical safety, reliability and resilience challenges. This paper presents a quantitative risk assessment approach for evaluating the performance and resilience of drone-assisted systems. The methodology is based on existing assessment frameworks and combines established standards with the principles of the multi-criteria hierarchy concept. The proposed approach models the interactions between systems’ components, environmental factors, structural limitations and operational uncertainties to identify potential failure scenarios and quantify their impact. A qualitative analysis is performed to identify and register the required risk elements of assets, vulnerabilities, threats, likelihood, and impact. Following this, a hierarchical model is developed to define the dependencies among them and enable their quantification. To demonstrate the applicability and feasibility of the proposed methodology, a drone-assisted delivery system is examined, showcasing its effectiveness in identifying hazards, evaluating critical risk elements and quantifying risk events. The results indicate the significance of the methodology in ranking the verified risk elements and identifying those that made the greatest contribution to system failure. Also, it highlights that weather-driven and power-related elements are among the most significant contributors to performance deterioration.

Abstract:

Musculoskeletal disorders represent one of the most frequent occupational health problems in labor-intensive industries, particularly in fish processing, where repetitive tasks and prolonged postures are common. The objective was to determine the level of ergonomic risk by applying the Rapid Entire Body Assessment (REBA) method and based on the results, to formulate recommendations aimed at preventing musculoskeletal disorders and improving preventive management within the organization. The assessment included 30 workers distributed across three operational workstations, where the overall average REBA score was 8.60 ± 1.65 (range: 6–12), indicating a predominantly high level of ergonomic risk. In categorical terms, 60.0% of the workers were classified as high risk, 13.3% as very high risk, and 26.7% as medium risk, while none reached negligible or low risk levels. Significant differences were observed between workstations (Kruskal-Wallis H = 16.72, p < 0.001, ε² = 0.545), with the nobbing stage exhibiting the highest biomechanical load (mean REBA = 10.38 ± 1.06). It is concluded that ergonomic risk is structurally integrated into the operational design of the evaluated production system; therefore, ergonomic interventions focused on redesigning workstations, adjusting height, and configuring tasks are recommended to reduce biomechanical exposure and strengthen the organization’s preventive occupational safety framework.

Abstract: This study examined how Mechanical, Electrical, and Plumbing (MEP) practitioners understand and apply quality and safety management in construction projects in Taiwan. It focused on the gap between what practitioners know about best practices and what they can carry out on site, defined here as the “Cognitive-Execution Gap.” A mixed-methods design combined a questionnaire survey of 130 MEP practitioners with semi-structured interviews with six senior experts. Practitioners with MEP-related academic backgrounds scored significantly higher in professional knowledge and practice than those from un-related fields, with mean differences of roughly 30% in key indicators. In contrast, awareness of management optimization strategies was high and similar across all de-mographic groups. Interview findings suggested that schedule pressure, the lower or-ganizational status of MEP compared with civil engineering, and persistent talent shortages prevent practitioners from applying the practices they recognize as necessary. The results support the existence of a Cognitive–Execution Gap and suggest that bridging it requires organization‑level reforms, including contractually enforced BIM‑based co-ordination, clearer standard operating procedures and performance indicators, and structured mentorship programs to strengthen professional capacity in MEP engineering.

Abstract: Heavy equipment in aluminum smelters operates under harsh thermal and mechanical conditions, leading to increased risks of vehicle failure and unplanned downtime. This study proposes an Economic Risk Priority Number (ERPN) approach to overcome the limitations of the conventional Risk Priority Number (RPN) used in Failure Mode and Effects Analysis (FMEA). A five-year maintenance dataset (2019–2024), comprising 2,303 corrective work orders across 58 units, was analyzed. The classical RPN is approach prioritized failure modes primarily based on frequency, identifying wheels and hydraulic systems as the most critical subsystems. However, the proposed ERPN model incorporates economic impact, including maintenance cost, labor cost, and production loss, leading to a reprioritization of the engine subsystem as the highest-risk component. The most severe engine failure resulted in a financial loss of approximately USD 1.92 million due to extended downtime and repair costs. Root cause analysis identified coolant loss, low oil pressure, and excessive vibration as primary contributors to failure, supported by diagnostic data and repeated alarm patterns. Statistical validation using the Kruskal–Wallis test confirmed significant differences among subsystem risk rankings for both RPN (χ² = 846.07, df = 4, p < 0.0001) and ERPN (χ² = 131.69, df = 4, p < 0.0001). The results demonstrate that ERPN provides a more realistic and economically aligned framework for maintenance prioritization in heavy industrial operations. The proposed approach enhances decision-making by integrating reliability analysis with economic impact, offering a practical tool for improving maintenance strategies and reducing operational risk in aluminum smelter fleets.

Abstract: This systematic literature review aims to first, explore the influence of remote work on occupational health and safety, in terms of mental and physical health and second, to shed light on best practices that can be adopted to improve the health and safety of employees working remotely. Twenty-four (24) peer-reviewed articles published from 2020 to 2024, were selected through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The review identified four themes, namely–positive impact of remote work; negative impact of remote work; challenges associated with remote work, and best practices for effective remote work practices. Findings from the study revealed that the impact of remote work on OHS was mixed. Eight (8) papers found that remote employees performed better in their OHS, whereas 18 papers found the opposite. The most dominant health disorders reported were depression, stress, anxiety and musculoskeletal pain. In contrast, the study identified that vertical trust levels and job design that considers physical and psychosocial aspects of the job can enhance safety while working remotely. Remote workers are encouraged to follow ergonomics best practices, take regular breaks during work to stretch and move around to reduce musculoskeletal disorders.

Abstract: In architecture and construction, it is common to use acrylic products with a high flammable content, from lacquers to improve the curing of concrete and mortar to resins that offer protection, sealing, flexibility and elasticity properties. The drying process of the treated surface involves the formation of volatile organic compound (VOC) vapours. To prevent these from degenerating into a potentially dangerous flammable atmosphere, a procedure is presented that establishes the maximum application yield for solvent-based products, providing equations that relate the maximum application surface area and minimum drying time to the air velocity available in the work area. The results are provided for both indoor and outdoor applications. A maximum application speed is established to prevent the generation of areas classified as fire or explosion hazards: 1.5 m²/h indoors and 1 m²/h outdoors. When this is carried out at an ambient temperature of 20°C, and above 40°C, it is not possible to apply varnishes without generating a flammable atmosphere.

Abstract: Rapid access to building intelligence is critical for emergency response, yet paper Fire Safety Instructions (FSi) often provide limited utility under stress. This structured narrative review addresses the "information gap" between unit arrival and decision-making by analyzing legal admissibility, technological requirements, and security risks of digital FSi across Poland, Germany, France, Belgium, and Hungary. While no explicit prohibition of digital forms was identified, enforcement practices prioritize paper as the evidentiary master. Consequently, we propose a hybrid model: a paper master for compliance and redundancy, supplemented by a digital operational overlay accessed via "zero-install" offline-first Progressive Web Apps (PWA). The review defines a Minimum Operational Dataset (MOD)—prioritizing critical data like utility shut-offs and hazards over full documentation—and addresses cybersecurity threats, specifically QR-phishing ("quishing"). We conclude that the hybrid model minimizes legal and operational risks while significantly reducing time-to-information, provided that strict content identity and change management protocols are maintained.

Abstract: Rail trespassing remains a persistent safety challenge at the system level in the United States. However, identifying hot spots proactively is difficult due to limited incident data and strong spatial dependencies within the built environment. This study creates a ZIP-code–level geospatial analytics framework to identify current and emerging trespassing hot spots across North Carolina by combining land-use composition, rail exposure metrics, and historical Federal Railroad Administration (FRA) trespassing records. Geospatial layers were integrated within a GIS workflow to derive predictors such as rail miles, grade crossings, crossings per mile, population density, and land-use types encoded as one-hot vectors. Exploratory spatial analysis showed significant clustering of trespassing incidents, with Global Moran’s I indicating positive spatial autocorrelation across multiple neighborhood sizes. Permutation z-scores confirmed non-random hotspot formation along major rail corridors. A k-means clustering method identified four structural risk environments, and a Cluster Risk Index (CRI) was developed from weighted, standardized exposure and land-use variables to quantify latent risk, independent of raw casualty counts. Results demonstrate that dense urban–industrial rail corridors have the highest CRI values and exhibit the strongest spatial autocorrelation. In contrast, rural ZIP codes with long rail lines show increased exposure-based risk despite fewer historical casualties. The resulting risk surfaces and hotspot classifications provide an interpretable and scalable framework for statewide safety planning, early hotspot detection, and targeted interventions by transportation agencies.

Abstract: Industrial maintenance is increasingly software defined and interconnected through the internet of things, which forces a redefinition of uptime as cyber incidents begin to behave like unplanned downtime as per International Electrotechnical Commission (IEC) [1]. ISO/IEC 17025:2017 is a widely used standard for demonstrating laboratory competence in testing and evaluation across many industrial areas and disciplines [2]. Despite its long-standing and broad use, it remains under-documented and challenging when applied to cybersecurity testing. This is due in part to the nature of the business, which is highly fragmented and unique and must be treated differently from traditional laboratory activities. Cybersecurity testing has its own specific characteristics, in which software, hardware, cloud services and other components are tested individually or as integrated systems and solutions.

Abstract: This paper presents a reliability-oriented analytical framework for the quantitative assessment of fragment-resistant multilayer protective equipment subjected to impulsive fragment loading. The study is motivated by the stochastic nature of fragment generation and impact conditions in industrial and occupational accident scenarios, where deterministic penetration criteria are insufficient to describe protective performance. Fragment interactions are modelled as stochastic spatial events, with impact locations and kinematic characteristics treated as random variables and mapped onto a predefined protected region. System failure is formulated using an energy-based limit-state criterion defined by comparison between the absorbed energy demand induced by fragment impact and a critical admissible energy threshold. The fragment–PPE interaction is described using a reduced-order dynamic formulation with concentrated parameters, capturing the dominant normal deformation response under short-duration impulsive loading. Closed-form analytical expressions are derived that relate fragment mass and velocity to impact impulse and absorbed energy. The resulting formulation establishes a direct link between impulse-driven dynamic response, progressive multilayer engagement, and failure probability under single and repeated impact events. Application of the proposed framework to a representative multilayer protective configuration demonstrates physically consistent reliability trends and confirms its computational efficiency. The framework provides a practical tool for reliability-informed assessment and preliminary design of fragment-resistant multilayer protective equipment.

Abstract: Speed management plays a critical role in road safety; however, conventional speed limits are determined based on geometry and traffic characteristics, with limited consideration of pavement structural condition and surface distress. This study proposes an integrated mechanistic–quantitative framework that links pavement distress and road safety indicators to the selection of speed limits. A flexible pavement section on Highway No. 80 in Iraq is analyzed as a case study. Mechanistic pavement analysis using KENPAVE is employed to estimate critical strains based on field traffic data and Equivalent Single Axle Loads (ESAL). The rate of failure is estimated by comparing the ESAL and the allowable load repetitions. Safety-related constraints are then derived to quantify hydroplaning risk, braking performance through stopping sight distance, and the vertical shock criterion. The results indicate that the existing pavement structure is marginal, with a high probability of fatigue failure and sensitivity to rutting under traffic growth. The integrated safety analysis yields a critical wet-weather speed of approximately 67–70 km/h, while localized settlements exceeding 10 mm require speed reductions to 50–60 km/h to maintain vehicle stability. The proposed framework demonstrates that pavement condition directly influences safe speed and provides a rational basis for safety-oriented speed management.