Aprendizado de Máquina Quântica para Monitoramento Estrutural
Estudos pioneiros
DOI:
https://doi.org/10.34019/2179-3700.2024.v24.46058Keywords:
Monitoramento de Integridade Estrutural, Aprendizado de Máquina Quântico, Detecção de danosAbstract
This paper proposes a novel method for Structural Health Monitoring (SHM) utilizing unsupervised Quantum Machine Learning (QML). The suggested process entails taking raw acceleration data, extracting characteristics from them, and embedding them into quantum states such that a quantum classifier can analyze them. Potential structural abnormalities are detected by evaluating an anomaly score function, which is derived via training the model with known intact situations and identifying variations from typical behaviors. Experimental implementations on a laboratory two-story frame to confirm the proposed framework, showing promising results in anomaly identification and quantification. This work establishes the groundwork for future investigations at the nexus of quantum computing and civil engineering through relevant experiments, analyses, and discussions, expanding the boundaries of SHM research.
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Copyright (c) 2024 Victor Higino Meneguitte Alves, Alexandre Abrahão Cury, Raphael Gomes
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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