Search Results (1,365)

During the production, storage, and use of solid rocket motors, the impact generated by unexpected accidents, such as collision or drop, will cause damage to the propellant and affect the safety of the motor. However, the progressive evolution mechanism of mesoscopic damage in NEPE propellant under such impact conditions has not been fully elucidated, and there is still a lack of quantitative method to evaluate the impact-induced damage degree, which restricts the engineering safety assessment of solid rocket motors. To investigate the influence mechanism, the mesoscale damage characteristics of NEPE propellant under drop-weight impact is systematically studied. First, damaged NEPE specimens are obtained by conducting drop-weight experiments with a 10 kg hammer, where the drop height is varied to apply different impact impulses. The internal meso-structure of the propellant is then characterized using micro-CT, yielding detailed imagery of the refined meso-structural features and damage morphologies in the NEPE propellant. To capture the dynamic evolution process of mesoscale damage, a mesoscopic model incorporating AP, Al, HMX particles and voids, is subsequently constructed based on the high-precision mesoscopic morphology characterized by micro-CT. By integrating the deviatoric constitutive model, Gurson plastic damage model, and bilinear cohesive zone model, high-fidelity numerical simulations of the drop-weight impact damage process are performed using the advanced SPH-FEM coupling algorithm. The results indicate that no significant damage occurs when the impact impulse is less than 13.85 N·s. As the impulse increases, phenomena including matrix microcracks, void collapse, particle/matrix interface debonding, and main crack formation appear sequentially. When the impulse exceeds 24.25 N·s, particle fragmentation and transgranular fracture occur, accompanied by plastic flow and frictional heating that induce ignition. Finally, the overall damage degree is fitted by the Boltzmann function, and a function for quantitatively describing the damage degree is obtained, which can provide theoretical support for the impact safety assessment of solid rocket motors. Full article

Compared with other construction operations, high-altitude operations are more dangerous. Falling from a height is the main type of accident in construction. It is important to study the human risk of falling from height to reduce falling accidents. Based on the Human Factors Analysis and Classification System (HFACS) model, a preliminary evaluation index system for fall risk in building construction was established. Through the Delphi method and sensitivity analysis, the initial indicators were screened, the index factors that did not meet the requirements were removed, and the final human risk index evaluation system was determined. The system includes five first-level indicators and 17 s-level indicators of organizational influence, unsafe supervision, preconditions for unsafe behavior, and unsafe behavior. Subsequently, the analytic network process–entropy weight method (ANP-EWM) is used to subjectively and objectively weight the evaluation indicators, and the combined weight is obtained through game theory. The matter–element extension model is constructed to evaluate the human risk of falling from height in construction. Finally, an empirical analysis is carried out with the Y project as a case study. The novelty of this study lies in integrating human-factor analysis with the matter–element extension model for fall risk assessment in construction, while combining ANP, the entropy weight method, and game theory to balance subjective and objective weighting. The proposed model provides a practical tool for evaluating and controlling human risk in high-altitude construction operations. The results show that the correlation degree calculated according to the matter–element extension model is K₄ = 3.5, and the human risk of falling from height in the construction of Y project has generally reached an excellent level. However, the evaluation level of some evaluation indexes is still low, which is consistent with the actual situation of construction enterprises in Y project. This model provides a direction for the study of human risk assessment of falling from different construction heights. Full article

With the rapid development of society, the number of road vehicles has increased significantly, leading to a growing severity of traffic accident issues. Timely and accurate detection of road traffic anomalies or accidents is crucial for reducing fatalities and alleviating traffic congestion. Consequently, the detection of road traffic anomalies has become a focal point of research in recent years. With the assistance of computer technologies such as deep learning, researchers have developed more accurate and effective methods for detecting road traffic anomalies. However, the small proportion of anomaly-prone areas in surveillance video frames, combined with the complex and difficult-to-capture patterns of accidents, presents new challenges for the application of deep models to traffic anomaly detection from a surveillance perspective. In light of this, this paper annotates the TADS dataset we previously proposed, a popular text-assisted video representation learning method, to develop a more efficient detection method. Utilizing the well-known video-text model CLIP, we have constructed a detection model that leverages unique text and eye-gaze annotation data from the TADS dataset to learn anomaly representations more effectively, thereby improving the detection of road traffic anomalies from a surveillance perspective. Experimental results demonstrate the superiority of our model for detecting traffic anomalies from a surveillance perspective, as well as the utility of the text and eye-gaze data included in the dataset. Full article

Traffic accident detection is a critical component of intelligent transportation systems (ITS), enabling timely incident response and traffic management. While most existing approaches rely on temporal information from video sequences, such methods are not always applicable in resource-constrained surveillance environments. This study investigates the feasibility of detecting traffic accidents from single static images by formulating the task as a binary classification problem. Representative architectures, including Vision Transformer (ViT), Swin Transformer, and ResNet-50, are systematically evaluated on the Car Crash Dataset (CCD) under multiple training configurations. To assess generalization capability, cross-domain evaluation is conducted using an external crash video dataset (ECVD) constructed to approximate real-world deployment conditions. Experimental results show that all models achieve strong performance under in-domain evaluation. However, cross-domain testing reveals substantial performance degradation, particularly in recall, indicating limited generalization capability under domain shift. Qualitative analysis further shows that missed detections are associated with weak visual cues, occlusion, and complex traffic environments, while false positives are caused by visually ambiguous patterns resembling accident scenarios. Unlike prior studies that primarily report performance improvements, this work provides empirical evidence that model behavior in static-image-based accident detection is governed by dataset composition rather than architectural design. Therefore, static-image-based accident detection should be interpreted as a coarse-level screening tool rather than a fully reliable decision-making system. This study highlights the importance of data-centric design and cross-domain evaluation for improving real-world applicability. Full article

In the complex and high-risk underground environment of coal mining, ensuring occupational health and safety is a fundamental pillar of social sustainability. Traditional safety compliance is insufficient to prevent unpredictable accidents and sustain long-term enterprise resilience. Thus, fostering proactive safety citizenship behavior is essential for enhancing organizational resilience. Drawing on the cognitive appraisal theory of stress, this study constructs a double-edged sword model of felt accountability on miners’ safety citizenship behavior. A three-wave time-lagged survey was conducted among 375 frontline coal miners in China, with data analyzed using SPSS 26.0 and AMOS 24.0. The findings show that felt accountability can increase work engagement and promote employee safety citizenship behavior, while also enhancing psychological strain and inhibiting employee safety citizenship behavior. In addition, safety-specific transformational leadership amplifies the positive impact of felt accountability on work engagement and mitigates its effects on psychological strain. These findings enrich our understanding of the impact of felt accountability, and provide practical insights for coal enterprise managers to improve sustainable safety performance and foster a socially sustainable work environment. Full article

Occupational safety analytics is increasingly moving toward data-driven methodologies; however, existing models often struggle to capture the multidimensional nature of accident causation. This study presents a multimodal Hybrid Transformer-LSTM framework for classifying occupational fatalities by jointly modeling unstructured narratives, cyclical temporal features, and regional spatial indicators. Utilizing a large-scale dataset of 14,914 OSHA fatality records, the proposed architecture leverages BERT-based embeddings for semantic extraction and Bidirectional LSTMs as non-linear pattern encoders for spatiotemporal context. Conceptually grounded in the Swiss Cheese Model, the framework treats different data modalities as proxies for distinct layers of system risk, ranging from proximal unsafe acts to environmental preconditions. Experimental results show that the multimodal architecture achieves an accuracy of 84.56%, representing a 5.33% gain over unimodal BERT baselines. To address the inherent “black-box” nature of deep learning, a SHAP-based explainability framework is incorporated to quantify the contributions of both textual tokens and environmental features to the model’s decision-making process. The results indicate that integrating narrative semantics with temporal and spatial context enhances discriminative performance and enables context-aware classification within a weakly supervised setting. By providing a scalable and interpretable classification framework, this study offers a data-driven decision-support approach for safety professionals and regulatory bodies seeking to implement evidence-based risk management strategies in high-risk industrial sectors. Full article

This study investigates the structural misalignment between official wage benchmarks and actual market wages in the Republic of Korea to establish an independent, forestry-specific wage system essential for labor sustainability. Historically, the Republic of Korea forestry project costs have relied on construction industry benchmarks, leading to a “diverging hypothesis” where official rates fail to reflect the specialized risks and technical skills required in forest operations. To address this, a comprehensive wage survey was conducted in 2025 across 13 specialized forestry occupations. Utilizing a sampling frame of 7555 sites, 1044 units were selected via stratified sampling with square-root proportional allocation, ensuring a relative standard error (RSE) of 2.5%. The findings reveal that market wages consistently exceed construction benchmarks by 4.5% to 41.0%. The most significant disparities were observed in leadership and mechanized roles, reflecting substantial “risk–responsibility” and “skill premiums”. Furthermore, the study identifies a structural shift toward risk-transfer strategies, such as stumpage sales, in response to the Serious Accidents Punishment Act (SAPA). These results underscore the urgent need for a specialized wage framework to ensure safety and long-term resilience. Ultimately, such institutional refinement is a prerequisite for securing the high-quality human capital necessary for a sustainable circular bioeconomy. Full article

Road construction, inspection and maintenance are activities that often require workers near heavy equipment, traffic, and dangerous materials. This proximity to potential hazards along with the characteristics of highway and street work zones—transient and in restricted areas—increases the possibility of accidents and near-misses. Recent developments in remote-controlled technology can provide workers and inspectors with the ability to conduct activities from a safer distance. This paper aims to scan and evaluate several promising remote-controlled technologies that could be used to improve safety in highway and streets work zones. The technology scanning highlighted over twenty technologies in several levels of development that met this goal. Each technology was briefly evaluated not only based on safety features but also on productivity, data processing, and requirements for implementation. Finally, recommendations for implementation of selected technologies were provided. This consolidated review provides a unique and timely resource for researchers and practitioners. Full article

This study proposes an improved tree-augmented Bayesian network (TAN-BN) method for analyzing the severity of ship collision accidents by introducing the information contribution rate (ICR) for edge orientation and flexible filtering constraints for structure optimization. Based on 634 ship collision accident reports, a Bayesian network covering accident attributes and causal factors was constructed. The results show that the improved model achieved an overall AUC of 0.864, higher than that of the traditional TAN model (0.827). Mutual information analysis identified ship length as the factor most strongly associated with accident severity, with a mutual information value of 0.0868. Sensitivity analysis based on true risk impact (TRI) further showed that ship length, time, and ship type were the most influential factors, with average TRI values of 19.4%, 8.8%, and 7.2%, respectively. The proposed model effectively captures the dependency relationships between accident severity and multiple influencing factors and can provide quantitative support for risk warning and accident prevention in maritime traffic safety. Full article

Accurate forecasting of fatal construction accidents is critical for proactive safety management; however, accident time series exhibit strong non-stationarity, nonlinear dynamics, and multi-scale temporal patterns that challenge conventional models. This study proposes a hybrid STL–BiGRU framework that integrates Seasonal–Trend decomposition using Loess (STL) with a Bidirectional Gated Recurrent Unit (BiGRU) network to deliver robust and interpretable forecasts tailored to construction safety needs. STL first decomposes the original monthly accident series (January 2012–December 2024, OSHA) into trend, seasonal, and residual components, reducing structural complexity and mitigating non-stationarity. Independent BiGRU models are then trained on each component to capture bidirectional temporal dependencies, and final forecasts are reconstructed through component aggregation. Comparative experiments against Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and their STL-enhanced variants demonstrate that the proposed STL–BiGRU model achieves superior performance across both short-term and medium-term horizons. The model achieves the lowest error levels, with a short-term Root Mean Squared Error (RMSE) of 6.8522 and a medium-term RMSE of 7.0568, and shows consistent improvements in Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results indicate that multi-scale decomposition combined with bidirectional deep learning provides a practical, forward-looking tool. It helps regulators and contractors anticipate high-risk periods, optimize resource allocation, and reduce fatal accidents through targeted preventive measures. Full article

Unsafe behaviors by construction workers are a primary cause of accidents in commercial building construction. While traditional studies focus on the frequency of violations, they often overlook the disorder and unpredictability of such behaviors. This study introduces “Unsafe Behavior Entropy” as a new index to measure the disorder of workers’ behaviors, complementing traditional violation frequency. Utilizing a dataset from a large-scale commercial building construction project in Wuhan, China, this research uses Partial Least Squares Regression (PLSR) and Gray Relational Analysis (GRA) to examine the influence of six key factors, including safety meeting coverage and supervision density. The PLSR results indicate that the number of workers supervised per safety officer is the most critical driver of both frequency and entropy, while the coverage rate of entry safety education significantly impacts behavioral stability. GRA findings further reveal a high degree of correlation between management interventions and reductions in behavioral disorder. The study concludes that optimizing safety resource allocation and standardizing educational processes are fundamental to controlling human-related risks. By integrating the dual perspectives of frequency and entropy, this research provides a more comprehensive framework for safety management in complex building projects. Full article

To unveil the underlying risk structures in complex industrial systems, this paper proposes a hybrid analytical framework that integrates BERTopic modeling, a large language model (LLM), and social network analysis (SNA). This framework aims to extract systemic safety intelligence from unstructured accident reports. It first employs BERTopic to identify latent causal topics based on 745 Chinese accident investigation reports and utilizes DeepSeek-V3.1 (LLM) for semantic refinement and causal mapping of these topics. Subsequently, a semantic network of causal keywords based on positive pointwise mutual information (PPMI) is constructed, and its topological structure is analyzed using SNA methods. The study identifies and analyzes five major risk communities: confined spaces, fire, mining, construction, and road traffic. It reveals that accident causation exhibits the small-world characteristics of multi-factor coupling and non-linearity, with core risk nodes concentrated in systemic inducements such as organizational management and compliance deficiencies. The results demonstrate that this framework effectively identifies the latent systemic risk patterns embedded within the texts, providing methodological support for developing sustainable safety management mechanisms based on design for safety. Full article

As a key node in the global supply chain, the resilience of ports is crucial for coping with multiple risks such as increasingly frequent climate change, operational accidents, and geopolitics, and ensuring the smooth flow of trade and sustainable development. This paper takes Ningbo-Zhoushan Port, which leads the world in throughput, as the research object, aiming to construct a comprehensive port resilience assessment model. Through the system dynamics method, the smart port system is deconstructed into three interrelated subsystems: meteorology, production, and economic-politics, and a simulation model including a causal relationship diagram and a system flow diagram is established accordingly. The model is verified to be effective and robust through historical data testing and sensitivity analysis. By setting different scenarios, this paper quantitatively analyzes the impact of single and compound risk shocks such as extreme weather, production accidents, and tariff policies on port throughput, and classifies port resilience into three levels: strong, medium, and weak. The research results show that Ningbo-Zhoushan Port shows strong resilience to the above-mentioned single risks. Even when the risk parameters are increased by 100%, the change rate of port throughput is less than the historical average annual change rate by 5.06%. However, in the extreme scenario of multiple risk couplings, the decline in port throughput is more significant, highlighting the importance of coping with compound risks. Further strategy simulation reveals that accelerating the economic development of the hinterland, increasing investment in port infrastructure, increasing the frequency of equipment maintenance, expanding the proportion of high-quality employees, and strengthening public facility management for accurate risk prediction are all effective ways to enhance port resilience. This research provides a scientific decision-making support tool for port managers, and the proposed resilience enhancement strategies have important theoretical and practical significance for ensuring the long-term stable operation of ports and the sustainable development of the regional economy. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

To address the limitations of traditional construction safety accident analysis, which relies on manually defined causal relationships, requires extensive data annotation, and struggles to identify latent risks from Chinese unstructured texts, this study proposes an unsupervised and data-driven framework, termed CESA-Miner, for mining statistical association patterns among construction safety accidents. The proposed framework adopts a modularity-driven keyword optimization strategy to automatically identify a stable set of risk-related features. Based on this, an accident risk weighted co-occurrence network is constructed, where statistical associations are represented through keyword co-occurrence patterns and network community structures. Community detection algorithms are then applied to identify accident clusters and their underlying relationships. Using a dataset of 1368 official construction accident reports, the results show that the network modularity increases from 0.173 to 0.683, indicating significantly improved structural quality and community separability. In the absence of explicit ground truth, structural quality is evaluated using network modularity as a proxy metric. Compared with conventional clustering-based and embedding-based approaches, the proposed method yields a more structurally distinct network community organization and offers a complementary structure-aware perspective for characterizing accident relationships. The framework enables large-scale intelligent analysis of accident texts without requiring manual annotation, providing data-driven support for latent risk identification and statistical pattern analysis in construction safety. Full article