Engineering

Abstract: Lithium-ion batteries (LIBs) electric bicycles are widely used in China, with many accidental fires occurring in parking facilities in high-rise building. Electric bicycle parking areas in high-rise buildings have become fire-prone zones. There are urgent needs to establish fire codes for the parking facilities in high-rise buildings. But only limited research has been conducted on protecting against such fires. There are also uncertainties in the appropriate methods for implementing fire barriers and fire suppression facilities. To better understand the parking facility fires in this area, four fire scenarios were studied in this paper, aiming to seek principles on how to prevent serious fire accidents by isolating E-bicycles parked in parking facilities. These principles include fire barrier design, fire separation distance between the islands and the selection of fire suppression devices. A total of six experiments on LIBs bicycle fires were conducted. Fire spread between the LIBs bicycles and the propagation patterns of smoke generated by electric bicycle fires within parking facilities were studied. The effectiveness of different fire extinguishing methods in suppressing LIBs bicycle fires was discussed. The reasonable fire separation distance for electric bicycles was determined. It was found that LIBs with ternary lithium-ion batteries (such as NCM) are more prone to initiate thermal runaway. A sprinkler system with lower hazard class is proposed to operate under lower water pressure and flow rates. Fire control methods were proposed, such as including fire-resistive eave and fire barrier. The results can be used in setting up fire code and are useful for AI training cases in developing fire models.

Abstract: Recent advances in Micro-Electro-Mechanical Systems (MEMS) have enabled the development of accelerometers increasingly suitable for seismological and structural engineering applications. Quartz MEMS (QMEMS) sensors combine low self-noise, wide dynamic range, excellent thermal stability, and compact dimensions, providing a cost-effective alternative to conventional force-balance and piezoelectric accelerometers. This study presents the development and validation of a complete QMEMS-based sensing platform for seismic monitoring and Structural Health Monitoring (SHM), integrating the recently introduced Epson M-A370 accelerometer, a Smart Sensor Box with precise timing synchronization, embedded acquisition and edge-processing capabilities, and comprehensive laboratory and field validation. The M-A370 is evaluated against the previous-generation M-A352 and representative MEMS, piezoelectric, and force-balance accelerometers. Experimental results demonstrate that accelerometer self-noise is a primary factor governing the reliability of Operational Modal Analysis (OMA) and long-term SHM. Self-noise densities below 1 μg/√Hz, and preferably below 0.5 μg/√Hz, are shown to be necessary for robust modal identification and long-term tracking of structural dynamic properties under weak ambient excitation. The ultra-low-noise M-A370 (0.02 μg/√Hz) provides data quality comparable to engineering-grade force-balance accelerometers while enabling continuous monitoring of buildings, bridges, and heritage structures. The proposed sensing platform, combining ultra-low-noise QMEMS technology, precise timing synchronization, and embedded processing, provides a scalable framework for Urban Seismic Observatories, Operational Modal Analysis, distributed SHM systems and impact-based Earthquake Early Warning.

Abstract: The quality of sweet tamarind fruit, as determined by its total soluble solids (TSS), titratable acidity (TA), and TSS/TA ratio, is important for consumer satisfaction. Nondestructive techniques are therefore required to assess the quality of sweet tamarind fruit. This study investigated whether near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm can be used as a non-destructive method to assess TSS, TA, and the TSS/TA ratio of sweet tamarind fruit and to classify it under commercial standards. NIR-HSI, combined with deep learning and chemometrics, was applied for quantification and qualification analyses. Calibration models for determining TSS, TA, and the TSS/TA ratio were developed using partial least squares regression (PLSR) and support vector machine regression (SVMR). A combination of 1st derivative and SNV spectral pretreatment, was optimized to establish an SVMR model for TSS determination. MSC spectral pretreatment was optimized to develop the SVMR model for TA assessment, and the 1st derivative spectral pretreatment was optimized to establish an SVMR model for the TSS/TA ratio. Correlation coefficients of prediction (Rp) of 0.959, 0.961, and 0.956 were obtained with root mean square errors of prediction (RMSEP) of 1.102%, 0.369%, and 11.282 for TSS, TA, and the TSS/TA ratio evaluation, respectively. Partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used for classifying sweet tamarind fruit under a commercial acidity standard ( 4%). The SVMC with SNV spectral pretreatment produced the best prediction results for distinguishing standard and off-standard sweet tamarind fruit with an 82.86% accuracy. NIR HSI can be used to non-destructively predict the quality of tamarind fruit. It can be applied for online sorting to evaluate individual sweet tamarind fruits for grading and quality control in factory environments.

Abstract: Conventional hazard and operability (HAZOP) studies remain central to process safety management, but their periodic, document-centered implementation and dependence on expert judgment limit their ability to track dynamic operational risk across the full plant lifecycle. This critical literature review examines how Digital Twin (DT) and Artificial Intelligence (AI) technologies may augment HAZOP practice, advancing an augmentation principle: DT-AI should strengthen expert-led hazard analysis as a decision-support layer, not replace human judgment. The review synthesizes representative literature on DT-AI-enabled HAZOP enhancement and maps conventional HAZOP limitations to four complementary technology pathways: AI-assisted knowledge capture and deviation reasoning; DT-based hazard monitoring and early warning; hybrid physics-data modeling for predictive safety; and explainable AI for operator trust and regulatory acceptance. The review shows that these pathways offer strong potential to improve HAZOP completeness, traceability, operational relevance, and lifecycle learning. However, most implementations remain at conceptual, prototype, or limited pilot levels, with limited evidence of long-term industrial validation. Key failure modes-including model drift, sensor faults, large language model hallucination, and automation complacency-require mitigation through validation protocols, explainability, human oversight, cybersecurity, and regulatory engagement. The review proposes a lifecycle-oriented safety-management framework and a three-horizon research agenda for advancing toward reliable HAZOP-informed Digital Twin systems.

Abstract: Adulteration of freshly milled rice with rice from older sources is a fraudulent and illegal practice that exploits consumers. The purpose of this study was to develop a rapid and non-destructive technique that can detect this adulteration of milled rice, using near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm. Adulterated samples were prepared by adding old and freshly milled rice at different levels, scanning the mixed samples, and comparing the results with 100% freshly milled rice samples. All samples were divided into a calibration set and a prediction set to establish classification and calibration models. Spectral pretreatment methods were tested to develop the optimum models. For qualitative prediction, the best results for differentiation between freshly milled rice and adulterated samples using support vector machine classification (SVMC) yielded 92.31% accuracy, a 7.69% error rate, 88.89% sensitivity, and 96.55% specificity. For quantitative prediction, the best calibration model for determining the percentage of mixing with old rice using support vector machine regression (SVMR) gave results of coefficient of determination of prediction (R2p) = 0.95, and root mean square errors of prediction (RMSEP) = 6.75%. These results indicated that NIR-HSI could be successfully used in both qualitative and quantitative analyses to detect adulteration of freshly milled rice with old rice. It can be used as a rapid, nondestructive technique for assessing the authenticity of milled rice.

Abstract: Electrochemical impedance spectroscopy (EIS), a rapid and non-destructive detection technique, offers a novel technical pathway for monitoring the degradation and assessing the quality of gear oil. This research delves into the electrochemical response characteristics and the underlying evolution mechanism of high-viscosity gear oil specifically formulated for wind turbines during its oxidative degradation process. Utilizing Mobil SHC™ Gear Oil 320 WT as the research subject, oil samples with varying degrees of degradation were prepared through accelerated oxidation experiments. Broadband frequency-sweep EIS testing was employed to acquire impedance spectra. The EIS data were subsequently analyzed using the equivalent circuit (ECM) method to extract electrochemical fingerprint parameters, enabling a systematic analysis of the variation patterns in the electrochemical response of gear oil with respect to oxidation temperature and time. Concurrently, the impact of test temperature on measurement results was evaluated. The findings reveal that EIS can dissect the intricate oxidative degradation process of gear oil into quantifiable interfacial electrochemical responses, with the characteristic parameters derived from ECM demonstrating consistent trends. Through a comprehensive analysis of the evolution patterns of electrochemical fingerprints, the oxidative degradation state of gear oil can be effectively evaluated. This research provides empirical evidence supporting the application of EIS technology in monitoring oxidative degradation and assessing the quality of gear oil for wind turbines.

Abstract: Quality risks in digitalized equipment manufacturing processes are increasingly characterized by multi-source coupling, chain-like transmission, and latent evolution, making it difficult for single-method approaches alone to fully reveal their transmission mechanisms and key driving factors. This study proposes a mixed-methods framework integrating grounded theory, covariance-based structural equation modeling (CB-SEM), and XGBoost-SHAP. First, grounded theory was applied to in-depth interview data to develop a five-dimensional quality risk framework covering process design and transformation, data flow and collaboration, production execution and process control, quality inspection and data traceability, and personnel capability matching. Second, based on 420 valid questionnaire responses, CB-SEM was used to validate the chain transmission path of risks along “process design–data collaboration–production execution–inspection and traceability” and to reveal the pervasive effect of personnel capability matching risk, with indirect effects accounting for 43.5%. Finally, the XGBoost-SHAP framework was introduced to capture nonlinear and item-level effects, identifying key risk drivers such as the absence of pre-job certification, underlying control failures, and distorted simulation boundary conditions. By integrating qualitative construction, linear validation, and nonlinear attribution, this study provides cross-method evidence for identifying the driving factors and transmission mechanisms of quality risks in equipment digital manufacturing processes. The findings deepen the understanding of how quality risks are formed and transmitted across equipment digital manufacturing processes and provide decision support for agile and targeted digital quality control in equipment manufacturing enterprises.

Abstract: The stability of financial institutions is crucial; however, current regulations evaluate credit, liquidity, and market risks separately, which hampers a consistent assessment of an entity's true loss-absorbing capacity. To address this, our study introduces the Risk Capacity Index (ICR) as a comprehensive indicator of financial sustainability for organizations in Colombia's solidarity sector. The approach adjusts a macrofinancial risk capacity model to fit the institutional setting, defining the ICR as the ratio of technical equity to total risk exposure, including expected credit losses, market Value-at-Risk, and the liquidity gap. This index was empirically tested with monthly data from 2025 from a closed savings and credit cooperative, using sensitivity tests and stress scenarios aligned with Basel III standards. Results show that liquidity risk is the main driver of capacity depletion, responsible for most of the index's fluctuations and causing non-linear deterioration during adverse conditions, while market risk effects are minor. Significant funding pressures sharply reduce the ICR below viability levels, leading to structural issues on the balance sheet. The ICR provides a new, integrated early-warning tool that complements traditional solvency measurements. The study highlights that managing liquidity and liabilities proactively, rather than just increasing capital, is key to preserving financial stability in cooperative models.

Abstract: IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally-structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at AUROC 0.93), detecting 100% of injected faults at a mean delay under 25 minutes. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW's shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW's shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems.

Abstract: Battery Energy Storage Systems (BESS), utilizing chemistries based on Nickel Manganese Cobalt (NMC) containing lithium-ion devices, often present fire safety hazards that existing qualitative risk frameworks, including NFPA 855's 5×5 consequence-likelihood matrix, are insufficiently granular to quantify. This paper presents an original probabilistic risk assessment (PRA) of fire hazards associated with BESS for a 485.52 kWh NMC installation at the Equinix SG4-4A data centre in Singapore, using Monte Carlo simulation (N = 10,000 iterations) to characterise uncertainty in hydrogen fluoride (HF) gas dose, time to Immediately Dangerous to Life or Health (IDLH) concentration, cabinet-to-cabinet propagation probability, and suppression effectiveness. The HF yield is modelled as a triangular distribution (0.3–0.8 g/kWh, mode 0.5 g/kWh), ventilation activation delay as log-normal (median 90 s), and suppression effectiveness as a piecewise function of water application delay. HF dose exceeded the National Institute for Occupational Safety and Health (NIOSH) IDLH of 25 mg/m³ in 100% of simulated scenarios for both single- and two-compartment designs, confirming that HF toxicity is essentially present for any occupant during a full thermal runaway event and that ventilation alone cannot achieve adequate risk reduction. Single-stage suppression effectiveness was only 37.9% (mean), confirming that two-stage (clean agent + water) suppression is warranted for NMC chemistry. The two-compartment design reduced peak HF dose by 50% and reduced mean IDLH clearance time from 599 to 301 minutes, shifting residual risk from As Low As Reasonably Practicable (ALARP)-tolerable to broadly acceptable under UK Health and Safety Executive (HSE) criteria. The paper proposes a quantitative PRA framework as a complement to NFPA 855 Chapter 5's qualitative Hazard Mitigation Analysis. To the best of our knowledge, this is the first study to apply Monte Carlo simulation to HF dose modelling in a tropical data-centre BESS context, addressing a documented gap in the literature.

Abstract: Fire safety in high-rise residential buildings depends on the reliable performance of active fire protection systems subject to technical, human, and organizational risks. Probabilistic risk assessment frameworks incorporating human and organizational errors (HOEs) show that HOEs raise expected risk-to-life by 20 to 37%, yet such models remain inaccessible to the building owners, facility managers, qualified persons, and regulators who must act on their outputs. This paper applies Explainable Artificial Intelligence, specifically SHAP (SHapley Additive exPlanations), to a Bayesian network probabilistic fire risk model integrated with Markov Chain Monte Carlo posterior uncertainty quantification, extending the validated T-H-O-Risk methodology across sixteen active fire safety system configurations and seven case studies in Singapore, Australia, Hong Kong, New Zealand, and the United Kingdom. Global SHAP analysis shows that maintenance-related HOEs (H8, insufficient safety check; H9, inadequate periodic inspection) account for 83.1% of total HOE attribution, outranking compliance and training variables and reframing the primary intervention from behavioural to structural. Validation against published results yields Pearson r = 0.927 across 112 building-design combinations. This is the first application of SHAP attribution to a Bayesian network fire risk model, giving regulators and qualified persons a transparent, uncertainty-aware tool for inspection-regime calibration and ALARP demonstration.

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.