A Particle Swarm Optimization Algorithm with Sigmoid Increasing Inertia Weight for Structural Damage Identification
Source abstract
Abstract
In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Infrastructure Maintenance and Monitoring · Water Systems and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
School of Civil and Environmental Engineering, Queensland University of Technology (QUT), Brisbane 4000, Australia
Collaborative Innovation Center for Efficient Utilization of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Funding
National Natural Science Foundation of China · Training Plan for Young Key Teachers in Colleges and Universities in Henan Province · Fund of Innovative Education Program for Graduate Students at North China University of Water Resources and Electric Power
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