Due to the increasing quest of adopting low-cost sensors in structural health monitoring (SHM) processes, which may lead to detecting signals contaminated by significant levels of noise, the need to devise appropriate and effective denoising strategies, at the post-processing stage, is becoming more and more essential. Among several approaches proposed in the literature, it has been demonstrated that the employment of discrete wavelet transform (DWT) as a multi-rate filter bank, as well as the use of singular value decomposition (SVD), may result to be quite effective in signal denoising within various research fields, as biological, acoustic and mechanical. Here, DWT- and SVD-based denoising techniques are first independently reconsidered and reimplemented, aiming at exploring their optimal calibration in purifying noise-corrupted vibration response signals encountered in civil engineering applications. Then, a systematic performance evaluation is provided within a comparative framework, developed at an increasing level of noise affecting the measurements, in terms of noise-to-signal (N/S) ratio. In the study, two specific classes of synthetic response signals are first considered, namely earthquake and ambient vibration signals, since they may be assumed as representative of more general non-stationary and stationary signal typologies, respectively. To achieve a complete description of the clarified signal, strengths and weaknesses of the two denoising approaches are explored, in both time and frequency domains. The results prove the effectiveness of the analyzed implementations, especially in purifying seismic response signals, while some limitations may arise concerning the treatment of ambient vibration signals, in particular for the DWT-based denoising technique. Finally, a real case study is analyzed, where both denoising approaches are adapted and employed for clarifying acceleration signals detected on a modern short-span railway bridge, with rather satisfactory results, for both techniques.

由于越来越需要在结构健康监测 (SHM) 过程中采用低成本传感器，这可能会导致检测被大量噪声污染的信号，因此需要在后处理阶段设计适当且有效的去噪策略，变得越来越重要。在文献中提出的几种方法中，已经证明使用离散小波变换 (DWT) 作为多速率滤波器组，以及使用奇异值分解 (SVD)，可能会在以下方面非常有效生物、声学和机械等各个研究领域的信号去噪。在这里，首先独立地重新考虑和重新实现基于 DWT 和 SVD 的去噪技术，旨在探索它们在净化土木工程应用中遇到的噪声破坏的振动响应信号方面的最佳校准。然后，在比较框架内提供系统的性能评估，在影响测量的噪声水平不断提高的情况下，就噪声到信号而言（N / S） 比率。在研究中，首先考虑两类特定的合成响应信号，即地震和环境振动信号，因为它们可以分别被假定为更一般的非平稳和平稳信号类型的代表。为了实现对清晰信号的完整描述，在时域和频域中探索了两种去噪方法的优缺点。结果证明了所分析实现的有效性，特别是在净化地震响应信号方面，而在处理环境振动信号方面可能会出现一些限制，特别是对于基于 DWT 的去噪技术。最后，分析一个真实的案例研究，