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Abstract：
Coda waves are highly sensitive to changes in medium properties and can serve as a tool for structural health monitoring (SHM). However, high sensitivity also makes them susceptible to noise, leading to excessive dispersion of monitoring results. In this paper, a coda wave multi-feature extraction method is proposed, in which three parameters, the time shift, the time stretch, and the amplitude variation of the wave trains within the time window, are totally derived. These three parameters are each mapped to the temperature variations of concrete beams, and then combined together with their optimal weight coefficients to give a best-fitted temperature-multi-parameter relationship that has the smallest errors. Coda wave signals were collected from an ultrasonic experiment on concrete beams within an environmental temperature range of 14 °C~21 °C to verify the effectiveness of the proposed method. The results indicate that the combination of multi-features derived from coda wave signals to quantify the medium temperature is feasible. Compared to the relationship established by a single parameter, the goodness-of-fit is improved. During identification, the method effectively reduces the dispersion of identification errors and mitigates the impact of noise interference on structural state assessment. Both the identification accuracy and stability are improved by more than 50%, and the order of magnitude of the identification accuracy is improved from 1 °C to 0.1 °C.
摘要：
尾波对介质特性的变化高度敏感，可以作为结构健康监测（SHM）的工具。然而,高灵敏度也使它们容易受到噪音的影响，导致监测结果过度分散。在本文中,提出了一种尾波多特征提取方法，其中三个参数，时移,时间拉长，以及时间窗口内波列的振幅变化，是完全派生的。这三个参数分别映射到混凝土梁的温度变化，然后结合它们的最优权重系数，给出一个误差最小的最佳拟合温度-多参数关系。在14°C〜21°C的环境温度范围内，从混凝土梁上的超声实验中收集了Coda波信号，以验证所提出方法的有效性。结果表明，从尾波信号中获得的多特征组合来量化介质温度是可行的。与单个参数建立的关系相比，拟合优度得到改善。在识别过程中,该方法有效降低了识别误差的分散性,减轻了噪声干扰对结构状态评估的影响。识别精度和稳定性都提高了50%以上,识别精度的数量级从1℃提高到0.1℃。
