Chao Sun

The initial firefighting capacity of complex commercial areas depends on the service level and the location of micro firefighting facilities. In response to the issue of coverage by micro firefighting facilities in complex commercial areas, a graded progressive coverage model is established. This model includes distance-progressive coverage and firefighting service level attenuation coverage. The former integrates fairness and efficiency in fire planning, while the latter considers the adaptability between the demand points and the fire service levels. The objectives include maximizing the coverage matching degree, effective coverage rate, medium- and high-risk coverage rate, and overall coverage rate of the fire service point and the demand points. A genetic algorithm is designed to solve the model, where the construction level and the number of micro fire stations are varied to analyze changes in various indicators. Central Street, characterized by complex buildings and high pedestrian traffic, is selected as a case for the experiment. The results show that simply adjusting the construction level of micro fire stations increases the effective coverage rate by 2.5%. The graded progressive coverage model shows a turning point in the effective coverage rate and the overall coverage rate when the number of new micro fire stations is 14 or 10, with coverage rates of 76.1% and 93.2%, respectively. The maximum progressive coverage model shows a turning point in the overall coverage rate when 9 new stations are added, which is 10.5% lower than that of the graded coverage model; when 10 stations are added, the overall coverage rate is 11.2% lower. These findings demonstrate the effectiveness of the graded progressive coverage model.

An effective safety evacuation program is an important basis for safeguarding the lives of people, and reasonable planning of evacuation routes is of great significance for formulating personnel evacuation plans. This article considers the global search ability of the ant colony algorithm and the local search ability of the artificial potential field. The artificial potential field is integrated into the ant colony algorithm, and combined with the extended Moore type cellular automata, an extended cellular automata model based on the potential field ant colony algorithm is proposed to optimize the calculation of personnel evacuation and path planning in the evacuation area. Analyze the performance of different algorithms in planning path smoothness, total path length, and calculation time from the same location in single exit and multi exit evacuation areas. And to verify the effectiveness of the algorithm, we use part of a teaching building as an evacuation scenario. The results show that combining the potential field ant colony algorithm with the extended Moore type cellular automata for path planning can reduce the number of invalid nodes and redundant turning points in the shortest path, improve the smoothness of the path, improve planning efficiency, and provide a design basis for emergency evacuation of buildings.

Magnetic flux leakage (MFL) testing technology is widely employed in non-destructive testing of pipelines, and the analysis of leakage signals plays a crucial role in assessing safety. This paper introduces a novel approach for MFL testing, which combines finite element simulation with artificial neural networks. Firstly, a simulation model is developed to study MFL testing in defective pipelines, with a focus on investigating how magnetization state and defect dimensions impact the leakage signal. Signal features are subsequently defined and extracted through an analysis of the distribution curve of MFL signals. Finally, a Kernel Extreme Learning Machine (KELM) model is constructed, and optimization is carried out on the influential parameters to predict defect dimensions. The results demonstrate that the effect of magnetization intensity on the magnetization state can be categorized into two stages: a nonlinear growth stage and a saturated linear growth stage. Additionally, an increase in defect size has the effect of delaying the magnetization process in pipelines. Notably, changes in defect dimensions have a pronounced impact on the distribution of MFL signals. And KELM prediction model is demonstrated to be capable of accurately and efficiently predicting the depth, length, and area of defects.

This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine improved by genetic algorithm (GA-KELM) is proposed. Firstly, the variation of the corrosion defect dimension and profile with time is obtained by numerical simulation. At the same time, the distribution of the magnetic flux leakage signal under different defect conditions is analyzed and studied. Finally, feature selection is carried out on the magnetic flux leakage signal distribution curve, and GA-KELM is used to predict the depth and length of corrosion defects so as to realize the non-destructive testing of the pipeline defects. The results show that different geometric features result in different magnetic flux leakage signal distributions. There is a corresponding relationship between the defect dimension and extreme value, area, and peak width of the magnetic flux leakage signal distribution curve. The GA-KELM prediction model can effectively predict the depth and length of corrosion defects, and the prediction accuracy is better than the traditional extreme learning machine prediction model.

It is well known that the safety and reliability of pipeline transportation are crucial. We are aiming at the problem that the residual life and residual strength of the defective elbow pipes are difficult to predict and usually need to be obtained through experiments. Consequently, a combined method of numerical simulation technology combined with a genetic algorithm to optimize neural network extreme learning machine (GA-ELM) is proposed. Firstly, the erosion characteristics of elbow pipes with different defects under the conditions of different impurity particle flow rates, particle sizes, and mass flow rates are analyzed by numerical simulation. At the same time, the effects of erosion defects of different sizes on the equivalent stress and residual strength of elbow pipes are also studied. Based on numerical simulation data, the extreme learning machine prediction model optimized by a genetic algorithm is used to predict the erosion rate, residual life, and residual strength and compared with the traditional ELM network model. The results show that residual strength of the elbow pipes with the increase of the depth and length of the defect, and increases with the increase of the width of the defect; the GA-ELM model can not only effectively predict the erosion rate, residual life and residual strength of defective elbow pipes, moreover its prediction accuracy is better than the traditional ELM model.