Analytical impedance model for piezoelectric‐based smart Strand and its feasibility for prestress force prediction

Thanh-Truong Nguyen, Nhat-Duc Hoang, Trung-Hau Nguyen, T. Huynh
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引用次数: 4

Abstract

The concept of the piezoelectric‐based smart strand has been recently developed for low‐cost prestress force monitoring of post‐tensioned structures. However, the previous study uses the single‐degree‐of‐freedom system that is inadequate to describe multiple resonances in a realistic electromechanical impedance (EMI) signature. Furthermore, the EMI‐based prestress force prediction has mostly relied on statistical regression models that are difficult to apply to existing prestressed structures, where the collection of EMI data in failure cases is almost impossible. In this study, we newly propose a high‐fidelity analytical EMI model for the smart strand technique and develop a simple model‐update‐based method for prestress force prediction. The experimental result shows that the proposed analytical model is capable to generate the realistic EMI response of multiple modes at a similar frequency band with identical patterns. Also, the prestress force in the smart strand can be reliably predicted by minimizing the gap between the analytical EMI response and the experimental data through a genetic algorithm‐based model‐updating process.
基于压电智能钢绞线的解析阻抗模型及其预应力预测的可行性
基于压电的智能链的概念最近被开发用于低成本的后张拉结构预应力监测。然而,先前的研究使用的单自由度系统不足以描述现实机电阻抗(EMI)特征中的多个共振。此外,基于电磁干扰的预应力预测主要依赖于统计回归模型,这些模型很难应用于现有的预应力结构,在这些结构中,在失效情况下收集电磁干扰数据几乎是不可能的。在这项研究中,我们为智能链技术提出了一个高保真度的电磁干扰分析模型,并开发了一个简单的基于模型更新的预应力预测方法。实验结果表明,所提出的分析模型能够在相似频带内产生具有相同模式的多模真实的电磁干扰响应。此外,通过基于遗传算法的模型更新过程,通过最小化分析EMI响应与实验数据之间的差距,可以可靠地预测智能链中的预应力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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