The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors.
In order to improve the convergence accuracy and speed of social group optimization algorithm, so as to improve the overall performance of the algorithm, a social group optimization algorithm with dynamic disturbance strategy( DDSGO) is proposed. The proposed algorithm improved the initialization population and the two learning stages in the SGO algorithm respectively. When initializing the population, the DDSGO algorithm replaces the initial population generated randomly with a reverse learning strategy to ensure that the diversity of the population is improved; In the improvement stage, the dynamic self-reflection coefficient is used to expand the search range of the optimal solution in the initial stage, and accelerate the speed of the population convergence to the optimal solution in the later stage, so that the population as a whole can quickly converge to the optimal solution. In the acquisition stage, the tent mapping is used to generate chaotic disturbance to increase the diversity of the population, which can increase the possibility of the algorithm jumping out of the local optimal solution. The experimental results based on the standard test function show that the proposed DDSGO algorithm is significantly improved in terms of convergence speed, convergence accuracy and stability compared with the comparison algorithms, and the overall performance of the algorithm is improved. The DDSGO algorithm has been used to solve the pressure vessel design optimization problem in order to further verify the effectiveness of the DDSGO algorithm. The experimental results show that the DDSGO algorithm is superior to the comparison algorithms, which proves that the DDSGO algorithm can be used to optimize the actual engineering design optimization problem.
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