Hybrid quantum genetic algorithm for structural damage identification

Abstract

Quantum computing, as an emerging technology, has experienced rapid development and attracted widespread attention across various disciplines over the past four decades. In the field of damage identification, as the demand for damage detection efficiency in real-world engineering continues to rise, the application of this fast computing technology also deserves attention. As a combination of genetic algorithm and quantum computing, the hybrid quantum genetic algorithm (HQGA) utilizes quantum superposition and entanglement to implement evolutionary optimization on quantum computers. Due to its flexibility in performing a global adaptive search without relying on the specific nature of the problem or the form of the objective function, the HQGA demonstrates the potential to solve complex optimization problems. In this paper, a mapping relationship is established between quantum bits and the stiffness reduction factors of elements, leading to the development of the HQGA for solving the damage identification problem based on measured strain responses. The performance of the proposed method is tested by damage identification for three distinct structures. The results show that the proposed method can enhance detection efficiency greatly while maintaining its effectiveness and stability.