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A Quantum-Inspired Genetic Algorithm-Based Optimization Method for Mobile Impact Test Data Integration
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DOI：10.1111/mice.12352.png)
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摘要
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The traditional impact test method needs a large number of sensors deployed on the entire structure, which cannot meet the requirements of rapid bridge testing. A new mobile impact test method is proposed by sequentially testing the substructures then integrating the test data of all substructures for flexibility identification of the entire structure. The novelty of the proposed method is that the quantum-inspired genetic algorithm (QIGA) is proposed to improve computational efficiency by transforming the scaling factor sign determination problem to an optimization problem. Experimental example of a steel-concrete composite slab and numerical example of a three-span continuous rigid-frame bridge are studied which successfully verify the effectiveness of the proposed method.
Keyword:
FLEXIBILITY IDENTIFICATION
MODAL-ANALYSIS
BRIDGE
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