安全科学与韧性(英文)最新文献

The risk of forest fires is substantial due to uneven precipitation distributions and abnormal climate change. This study employs cellular automata principles to analyze forest fire behavior, taking into account meteorological elements, combustible material types, and terrain slopes. The Wang Zhengfei model is utilized to compute fire spread speed, and a multifactor coupled forest fire model is developed. Comparisons with experimental data show a mean calculated fire spread speed of 0.69 m/min, which is consistent with the experimental results. Using the forest fire in Anning city, Yunnan Province, as a case study with a mean burned area of 2281 ha, the burned area, rate of change in burned area, and burning area demonstrated an increasing trend, with fluctuating states in the rate of change of the burning area. Employing the controlled variable method to examine forest fire spreading patterns under varying factors such as wind speed, vegetation type, and maximum slope reveals that under wind influence, the fire site adopts an elliptical shape with the downwind direction as the major axis. Quantitatively, when the wind speed increases from 2 m/s to 10 m/s, the burned area expands by a factor of 1.37. The ratio of the combustible material configuration coefficient to the burned area remains consistent across the different vegetation types, and the burned area increases by a factor of 1.92 when the maximum slope increases from 5° to 25°.

Rapid and accurate acquisition and analysis of information is crucial for emergency management, but traditional methods have limitations such as incomplete information acquisition and slow processing speed. The natural language oriented spatial scene reconstruction method provides a new solution for emergency management, but existing generative models have limited understanding of spatial relationships and lack high-quality training samples. To address these issues, this paper proposes a novel spatial scene reconstruction framework. Specifically, the BERT based spatial information knowledge graph extraction method is used to encode the input text, label and classify the encoded text, identify spatial objects and relationships in the text, and accurately extract spatial information. Additionally, a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition, and based on the obtained biases, a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph. Finally, use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints. In addition, a high-quality training sample set of “text-scene-knowledge graph” was constructed.

Slender structures often lead to vibration discomfort for occupants when exposed to wind forces. This study proposes an innovative method for assessing comfort against wind-induced vibrations for slender structures that combines field monitoring, numerical simulations, codal provisions, and Chang's comfort chart. The method utilizes ambient vibration tests (AVT) and operational modal analysis (OMA) to create a reliable finite element (FE) model for the structure. It involves analyzing the time history and calculating the peak acceleration values at various points within the structure using synthetic ambient wind forces derived from superposing waves. The comfort assessment compares peak acceleration values estimated from time history analysis against those provided in Chang's chart for different comfort levels. The effectiveness of the proposed method is demonstrated through a case study on a tall, slender reinforced concrete (RC) staircase structure, confirming its suitability for practical applications.

Stringent fire prevention requirements are imperative in expansive environments. Fire detection in diverse large-scale settings typically relies on sensor-based or AI-driven target detection methods. Traditional fire detectors often suffer from false alarms and missed detections, failing to meet the fire safety requirements of large-scale structures. Many existing target detection algorithms are characterized by substantial model sizes. Some detection terminals in large structures face challenges deploying these models due to constrained computational resources. To address this issue, we propose a lightweight model, YOLOv8-EMSC, derived from YOLOv8n. The incorporation of C2f_EMSC, replacing the C2f module, significantly reduces the model parameters in the enhanced YOLOv8-EMSC model compared to YOLOv8n, thereby enhancing model inference speed. Extensive testing and validation using a custom-built large-scale infrared fire dataset demonstrates a 9.6 % reduction in parameters compared to the baseline model for YOLOv8-EMSC, achieving an average precision of 95.6 %, surpassing both the baseline and mainstream models and significantly enhancing fire detection accuracy in expansive environments.

Earthquakes are major catastrophes that cause great life and economic losses to human society and environment. This paper reviews and synthesizes relevant studies, drawing from a systematic examination of 4229 articles from the Web of Science core collection (1982–2023). Employing the CiteSpace visualization and analysis tool, current research and emerging trends in seismic risk assessment are discussed and analyzed. This paper provides a holistic overview of principal contributions, knowledge sources, interdisciplinary characteristics, and principal research topics in this field. Additionally, we propose key technologies that are in urgent need of enhancement, including data availability, quantity and quality of data, interpretability of machine learning models, performance improvement of machine learning methods and application of foundation models, as well as real-time risk assessment techniques. These insights support both theoretical understanding and practical applications of seismic risk assessment and damage analysis.

Floods and earthquakes are the most common natural disasters, causing significant damage and casualties in urban communities. Although academic research has focused on assessing cities' ability to withstand these natural disasters, there is a lack of established frameworks for evaluating resilience against multiple hazards. This research integrates the earthquake and flood resilience indicators to create a comprehensive resilience assessment framework. This study integrated seismic and flood resilience indicators and categorized them into 27 clusters. It also used the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method to determine the significance of clusters and prioritize them. This research identified 13 cause clusters and 14 effect clusters of indicators for seismic and flood resilience. The ‘response capacity’ cluster has the most impact on seismic and flood resilience, while the ‘transportation and accessibility’ cluster is the most affected cluster. This article presents a framework for evaluating flood and seismic resilience and makes suggestions for future research.

Safety pictograms are essential tools for identifying workplace hazards by providing critical information about health hazard risks, fire safety, emergency evacuation, and accident prevention. Effective safety pictogram training programs are necessary to enhance workers' knowledge of these pictograms. This study evaluates the effectiveness of a safety pictogram training program on the comprehension and retention of knowledge among engineering students. A total of 262 participants were asked to predict the meaning of 22 safety pictograms regulated by International Organization for Standardization (ISO) 7010 before and after a one-hour online training session. A follow-up test was administered six months later to assess long-term knowledge retention. Results showed that the average comprehension rate increased from 60.1 % before training to 68.3 % after training, with a retention rate of 66.0 % six months after training. The study found that training positively affected comprehension of emergency and mandatory action pictograms, while lower scores were observed for warning pictograms. Statistical tests revealed a significant effect of training on comprehension levels 16 out of 22 pictograms, with an average increase in comprehension of 11.2 %. Of these 16 pictograms, the comprehension level of 10 pictograms increased after training and remained at the same level six months later. However, the scores decreased slightly six months after the intervention, indicating the need for continued reinforcement or retraining. These findings have important implications for safety education and training programs, particularly in industries where safety hazards are widespread. The positive impact of training on comprehension scores highlights the ongoing need to improve safety pictogram comprehension to consistently meet standard acceptance criteria. Future training programs may need to focus on categories such as warning pictograms and fire equipment and fire action pictograms, which exhibited lower comprehension scores, to ensure better employee understanding.

Urban terrorism is a significant global concern, prompting extensive scholarly inquiry into its underlying causes and effects. However, a comprehensive literature review summarizing this body of knowledge is notably absent. Thus, this study seeks to address this gap by conducting a thorough examination of existing literature on terrorism, particularly focusing on urban contexts, to identify key patterns and recurring themes. The study identified 515 research articles using the keywords "urban" and "terrorism" through the Web of Science and Scopus databases. A bibliometric review was conducted, which included a historical background, author keywords, country and institution, citation, and co-citation analyses. The findings revealed an increase in the number of studies on urban terrorism following the 9/11 attacks in the United States, which accounted for the highest number of publications in the country. Most studies were conducted in government law, international relations, and urban studies. Keyword analysis revealed that counterterrorism, security, and disasters were more closely linked to terrorism. Thematic analysis identified six main themes related to urban spaces and terrorism: tourism, governance, resilience, public health, economy, security, and counterterrorism. This study emphasizes the importance of involving the public in counterterrorism efforts in addition to traditional approaches to addressing urban terrorism.

Excessive dependence on fossil fuels has precipitated various challenges, including Greenhouse Gas (GHG) emissions, health hazards, and the depletion of natural resources. Such perils underscore the importance of conducting requisite risk assessments for alternative fuel sources like Compressed Natural Gas (CNG) to ensure their safe utilization. This study undertakes a quantitative risk assessment encompassing diverse facets of the CNG sector holistically, aiming to pinpoint, analyze, and appraise risks, thus empowering policymakers to devise targeted mitigation strategies. To achieve this goal, the collected data undergoes analysis via an integrated approach combining the Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Technique of Order Preference by Similarity to Ideal Solution (F-TOPSIS). The study's findings reveal a heightened risk of explosion within the CNG sector owing to its highly combustible nature. Additionally, it computes an overall risk index of 0.266 for the CNG sector in a developing nation like Pakistan, indicating a relatively lower risk level compared to other fuel sources. Policymakers are thus advised to undertake requisite measures concerning infrastructure, customer safety, and environmental and economic stability to accrue both immediate and long-term benefits. The application of hybrid techniques for the risk assessment of the CNG sector in the case of a developing country marks the novelty of this study and a study of the first of its kind.

The global economic crisis of 2008–2013 led to the emergence of the concept of resilience, which focuses on the ability of socio-economic system store cover socially, economically, and environmentally after external impacts. The COVID-19 pandemic spurred scholarly interest in regional resilience as a new conceptual framework for the sustainability theory. This paper aims to examine the influence of the pandemic on the trends and geography of regional resilience studies. We analyzed data derived from Science Direct and used VOSviewer to perform clustering and bibliometric network analysis. The countries that suffered the most from the pandemic and showed the largest regional socioeconomic disparities have become new centers of knowledge on regional resilience. Moreover, the pandemic has led to a visible shift in the research focus. Thus, after 2020, more attention has been paid to the structural and topological characteristics of regions that enable them to reorganize their resources more effectively in times of crisis. This study investigates the potential of the resilient development concept as a framework for gaining insights into the factors supporting regional adaptability.