scholarly journals A Hybrid Intelligent Model for Urban Seismic Risk Assessment from the Perspective of Possibility and Vulnerability Based on Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jinlong Chu ◽  
Qiang Zhang ◽  
Ai Wang ◽  
Haoran Yu

Assessing seismic risk is an essential element of urban risk management and urban spatial security work. In response to the issues posed by the complexity and openness of urban systems, the nonlinearity of driving factors, and sudden changes in geological processes that affect urban seismic research, this paper is based on a variety of intelligent algorithms to develop a hybrid intelligent model that integrates probability and vulnerability to evaluate and quantify the difference in the urban spatial units distribution of earthquake risk. We applied this model to Hefei, one of the few superlarge provincial capital cities on the “Tancheng-Lujiang” fault zone, one of the four major earthquake zones in China, which suffers frequent earthquakes. Our method combined the genetic algorithm (GA), particle swarm optimization (PSO), and backpropagation neural network methods (BP) to automatically calculate rules from inputted data on known seismic events and predict the probability of seismic events in unknown areas. Then, based on the analytic hierarchy process (AHP), spatial appraisal and valuation of environment and ecosystems method (SAVEE), and EMYCIN model, an urban seismic vulnerability was evaluated from the four perspectives of buildings, risk of secondary disasters, socioeconomic conditions, and urban emergency response capabilities. In the next step, the overall urban seismic risk was obtained by standardizing and superimposing seismic probability and vulnerability. Using the hybrid intelligent model, earthquake probability, seismic vulnerability, and overall seismic risk were obtained for Hefei, and the spatial characteristics of its overall seismic risk were examined. This study concludes that areas with very high, high, low, and very low earthquake risk in Hefei account for 8.10%, 31.90%, 40.94%, and 19.06% of its total area, respectively. Areas with very high earthquake risk are concentrated in the old city, the government affairs district, Science City, and Xinzhan District. This study concludes that government authorities of Hefei should target earthquake safety measures consisting of basic earthquake mitigation measures and pre- and postearthquake emergency measures. In the face of regional disasters such as earthquakes, coordinating and governing should be strengthened between cities and regions.

2012 ◽  
Vol 160 ◽  
pp. 130-134
Author(s):  
Xu Yang ◽  
Xue Yi You ◽  
Min Ji

The optimization effects of the particle swarm optimization (PSO) on the parameters of Van Genuchten (VG) equation for the soil water characteristic curve were investigated. The results indicated that the VG parameters determined by the PSO were very close to those of experiments with a very high accuracy. The PSO was proven to be used as an optimization method for estimating VG parameters for the soil water characteristic curve. The PSO had the higher parameter precision than the damped least squares method and the nonlinear simplex method. The particle swarm optimization was improved and its convergence accuracy was higher than that of constriction factor particle swarm optimization (CFPSO) and PSO of Filedsend.


2011 ◽  
Vol 130-134 ◽  
pp. 3181-3184
Author(s):  
Chuan Jiang Li ◽  
Zi Qiang Zhang ◽  
Li Li Wan ◽  
Yi Li

This paper presents a least square influential coefficient based on particle swarm optimization, putting balance weight as optimizing object, which can make residual vibration meet the expected demand. Experimental result shows that this method has high performance optimizing effect, and high percent of removed unbalance amount one correction, which is over 95%. So this method has very high practical value.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Gan-Nan Yuan ◽  
Li-Na Zhang ◽  
Li-Qiang Liu ◽  
Kan Wang

A new intelligent model to simulate evacuation behavior in ships called neighborhood particle swarm optimization is proposed. This model determines the rules of behavior and velocity updating formulas to solve staff conflicts. The individuals in evacuation are taken as particles in PSO and update their behaviors by individual attributes, neighborhood attributes, and social attributes. Putting the degree of freedom movement of ships into environment factor and using the real Ro-Ro ship information and IMO test scenarios to simulate the evacuation process, the model in this paper can truly simulate the behavior of persons in emergency and provide a new idea to design excellent evacuation model.


2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.


Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory


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