作者：
Yu, LingLi, ChengChina Three Gorges Univ
Coll Civil Engn & Architecture Yichang 443002 Peoples R China Jinan Univ
Dept Mech & Civil Engn Guangzhou 510632 Guangdong Peoples R China Jinan Univ
MOE Key Lab Disaster Forecast & Control Engn Guangzhou 510632 Guangdong Peoples R China Bridge Sci Res Inst Ltd
China Railway Major Bridge Engn Grp Wuhan 430034 Peoples R China
Structural damage detection (SDD) is an important but still challenging task in the structural health monitoring (SHM) field. Many methodologies have been developed and broad application prospect are expected. However...
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Structural damage detection (SDD) is an important but still challenging task in the structural health monitoring (SHM) field. Many methodologies have been developed and broad application prospect are expected. However, there are still some difficulties when they are applied to the real structures. In this study, a novel global artificial fish swarm algorithm (GAFSA) is proposed for exploring a new solution to the SDD problem in the SHM field. Firstly, the basic theory of the GAFSA is introduced. The fishswarm behaviours inside water are simulated by the following four steps: random, preying, swarming and following behaviours, respectively. The artificialfish parameters are defined, the implementing procedure of GAFSA is expressed, and the computing performance of GAFSA is evaluated and compared with the basic artificialfishswarmalgorithm by three test functions. Secondly, the SDD problem is modelled as a constrained optimization problem in mathematics, an objective function on optimization problem is defined, and the model updating-based SDD is hopefully solved by the proposed GAFSA, which is based on swarm intelligence and uses a population (or swarm) of fish to identify promising regions looking for a global solution. Some numerical simulations on single and multiple damage cases of both an ASCE 4-storey benchmark frame structure and a 2-storey rigid frame have been conducted for assessing the effectiveness and robustness of the proposed GAFSA. Finally, a laboratory experimental study on damage detection of a 3storey building model with four damage patterns was performed. The illustrated results show that the proposed GAFSA can not only locate the structural damage but also quantify the severity of damage with a good noise immunity.
In order to overcome local convergence of constant modulus blind equalization algorithm(CMA), the momentum constant modulus blind equalization algorithm(MCMA) based on globalartificialfishswarm optimization algorit...
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ISBN:
(纸本)9781510813465
In order to overcome local convergence of constant modulus blind equalization algorithm(CMA), the momentum constant modulus blind equalization algorithm(MCMA) based on globalartificialfishswarm optimization algorithm(GAFSA-MCMA) is proposed. In this proposed algorithm, on the basis of making full use of the global artificial fish swarm algorithm(AFSA) with the fast convergence and global search ability, the position vector of the artificialfish is optimized and the global optimal position vector is used as the initial optimal weight vector of the MCMA. Compared with the CMA and MCMA, the proposed algorithm has fastest convergence rate and minimum mean square error(MSE).
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