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.