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RISS 인기검색어
- 검색키워드
- 저자 : Sida Lian (검색결과 3 건)
- 무료
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Yancang Li,Sida Lian 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.12
In order to find a more effective method for the structural optimization, an improved fruit fly Optimization Algorithm was proposed. The dynamic adjustment search, the inertia weight function and the tabu search theory were employed to overcome the premature flaw of the basic algorithm. Then, the improved algorithm was introduced to the structural optimization of the tube- type trestle. After the setup of the optimization model, the improved algorithm was used. Optimization results and comparison with other algorithms show that the stability of improved fruit fly Optimization Algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems.
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Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm
Chunbao Xiong,Sida Lian 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.3
To locate the damage of the structure efficiently and judge the damage degree, this paper proposes an improved fruit fly optimization algorithm (IFOA). Aiming at the problem of poor convergence of the standard fruit fly optimization algorithm in the face of complex structure damage identification, the IFOA introduces the concept of collaborative search of two subpopulations. The IFOA divides the entire population into positive subgroups and negative subgroups based on the individual taste concentration results. Among them, the positive subgroup uses the improved dynamic adaptive search step size to perform a fine search locally to improve its local search ability. Negative subgroups continue to use the standard fruit fly optimization algorithm for optimization, taking advantage of the powerful global search capabilities of the standard fruit fly optimization algorithm. It enables the algorithm to balance global and local search capabilities, prevents the algorithm from falling into local optimum, and speeds up the convergence speed and accuracy of the algorithm. Simulation results show that IFOA can effectively identify the damage location and damage degree of the structure, and it still performs well when facing the complex steel truss damage identification.
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Chunbao Xiong,Sida Lian,Wen Chen 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.3
Because the steel structure trestle has been in service under heavy load for a long time, the steel structure trestle is prone to cracks around the welds or bolt holes, which can lead to structural collapse in severe cases. Aiming at the characteristics of stable and high-quality images obtained by the unmanned consumer-grade camera monitoring system, this paper proposed structure health monitoring (SHM) system which is based on consumer-grade camera. The SHM system can identify crack damage and locate steadily in long term, which provides the technical support of practical application in intelligent SHM system. The method first performed edge detection on the trestle structure, followed by pixel-level semantic segmentation and crack localization. Canny edge detection algorithm was used to identify trestle structures in the camera image. The panorama trestle structure was divided into areas of suitable size, and the camera focused on each divided area one by one. Then the improved DeepLab V3+ model was trained by constructing global and local datasets. Then the improved DeepLab V3+ model was used to perform pixel-level semantic segmentation on the trestle images of the divided regions. Finally, based on the Speeded Up Robust Features and combined with the image, a panorama crack location output method was proposed. The system was used to test a section of a trestle in a coal mining industrial park, and the system showed that the method could efficiently and accurately identify and locate the crack damage.
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