With the help of Structural Health Monitoring (SHM) methods, it is possible to identify the occurrence of damage at its early stages and prevent fatality and financial damages. Great advances in signal processing methods in combination with Machine learning tools have led to better achieve this goal. In the present paper, the two major techniques, that is, Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are combined with Artificial Neural Network (ANN) through processing raw acceleration responses measured on a scaled jacket type offshore platform which was constructed and tested as a benchmark structure at K.N. Toosi University of Technology. In this way, ANN was trained by the signals obtained from EMD and DWT for three different conditions of the jacket platform to determine the relative damage severity. The envelope of the obtained signal’s energy (ENV) as an appropriate damage index was used to determine the damage location. The results of the application of this procedure on the case study indicated that DWT, compared to EMD, is a more reliable signal processing method in damage detection due to better noise reduction.

借助结构健康监测（SHM）方法，可以在早期阶段识别损坏的发生并防止死亡和经济损失。信号处理方法与机器学习工具的结合取得了巨大进步，从而更好地实现了这一目标。本文将经验模态分解（EMD）和离散小波变换（DWT）这两种主要技术与人工神经网络（ANN）相结合，通过处理在比例尺夹套式海上平台上测得的原始加速度响应来实现。在KN Toosi理工大学进行了构建并作为基准结构进行了测试。这样，通过从EMD和DWT获得的信号对夹克平台的三种不同条件训练了ANN，以确定相对损伤的严重程度。使用获得的信号能量（ENV）的包络作为适当的损伤指数来确定损伤位置。该程序在案例研究中的应用结果表明，与EMD相比，DWT由于具有更好的降噪效果，因此在损伤检测中是一种更可靠的信号处理方法。