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<p>The paper presents a comprehensive study, based on more than 100 million analyses, to investigate the effectiveness of damage identification in relation to the formulation of the objective function and the configuration of damage in a one-dimensional solid body, which indicates a strong scale effect. The analysis is conducted in the framework of space-Fractional Euler-Bernoulli Beams (s-FEBB), previously identified for many different materials at nano/micro scales, based on parameters identified for silver nanowires. Additionally, it aims to elucidate the variations associated with different fractional model parameters, and it endeavours to clarify the challenges in damage identification for particular scenarios. Furthermore, the research offers a statistical measure of effectiveness across three noise levels. The fundamental result is that integrating static and dynamic responses is crucial for proper damage identification, specifically displacements, rotations, eigenvectors, and eigenvalues, resulting in the highest effectiveness. Furthermore, the analysis reveals that displacements are the sole objective that enhances efficiency when noise levels in input data are increased.</p>

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Comprehensive analysis of complexity of damage identification in micro-beam like structures based on silver nanowires experimental data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月1日

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  • Krzysztof Szajek,
  • Paulina Stempin,
  • Wojciech Sumelka

摘要

The paper presents a comprehensive study, based on more than 100 million analyses, to investigate the effectiveness of damage identification in relation to the formulation of the objective function and the configuration of damage in a one-dimensional solid body, which indicates a strong scale effect. The analysis is conducted in the framework of space-Fractional Euler-Bernoulli Beams (s-FEBB), previously identified for many different materials at nano/micro scales, based on parameters identified for silver nanowires. Additionally, it aims to elucidate the variations associated with different fractional model parameters, and it endeavours to clarify the challenges in damage identification for particular scenarios. Furthermore, the research offers a statistical measure of effectiveness across three noise levels. The fundamental result is that integrating static and dynamic responses is crucial for proper damage identification, specifically displacements, rotations, eigenvectors, and eigenvalues, resulting in the highest effectiveness. Furthermore, the analysis reveals that displacements are the sole objective that enhances efficiency when noise levels in input data are increased.