## 6 Vibration-Based Damage Detection on Offshore Structures

\end{table}
Table 6: _Cont._simulated values are in good agreement with the measured values, thereby indicating that the modified model is reliable and the updating method for determining unknown parameters in a complex offshore structure is reliable. Figure 21: The layout and the in situ measurement points: (**a**) case 1; (**b**) case 2–4; (**c**) cases 5 and 6; (**d**) case 7 [118]. In a recent development, machine learning (ML) application for vibration-based damage detection in a wind turbine has been highlighted. [PERSON] et al. [119] introduced a structural damage methodology in a jacket-type offshore wind turbine using a vibration response-only case. This methodology stems from the fact that in the offshore structure, excitation cannot be imposed and is often not measurable. In this work, two different machine learning algorithms, namely, k nearest neighbor (k-NN) and quadratic kernel support vector machine (SVM), are investigated. The proposed damage detection and localization method at four different crack locations was conducted in a small-scale laboratory structure excited by a steady-state wind load. A general overview of the experimental test is illustrated in Figure 23. Firstly, Gaussian white noise was applied to simulate the wind load, given by a functional generator (GW INSTEKAF-2005 model). The signal was amplified and passed to the inertial shaker (model GW-IV47). The response was monitored by triaxial accelerometers (model 356A17, PCB Piezotronics), which were linked to a data acquisition system (cDAQ-9188 chassis and six NI-9234 modules from National Instruments). Furthermore, the raw data were pre-processed using group reshape and column scaling, and principal component analysis (PCA) was applied as a feature selection technique. After that, the two different machine learning algorithms were compared. As seen from Tables 7 and 8, the damage detection and localization method using the quadratic SVM classifier obtained a better result than that from the k-NN algorithm, achieving a higher result with high prediction speed and a short training time. In future work, the proposed method should be explored in a more realistic environment. Figure 22: A comparison of the vibration displacement amplitude at the main frequency of 16.5 Hz [118]. Similar work to that of [PERSON] et al. [119] was performed by [PERSON] et al. [120], who investigated vibration response-only damage detection in a similar structure using different machine learning applications. A vibration response-only methodology using an accelerometer and a convolutional neural network (CNN) was proposed and tested on an experimental laboratory setup. A laboratory experiment test was conducted on a steel jacket-type offshore wind turbine under different damage scenarios to obtain accelerometer data; see Figure 24a. Several SHM tools and instruments were used in the experimental test. A white noise signal was generated by employing a function generator (GWINSTEK AF-2005 model), and the signal was amplified to a modal shaker (GW-IV47) that induces vibration. Eight triaxial accelerometers (PCB R Piezotronics, model 356A17), as illustrated in Figure 24b, were set and connected to six National InstrumentsTM cartridges (NI 9234 model) to gather raw data. After raw time series data were obtained, the data were pre-processed (signal-to-image conversion) with sensors to obtain a dataset of 24-channel gray-scale images. A 24-channel-input CNN was designed and trained for classification of the different structural states. The proposed SHM strategy using a deep CNN with data augmentation demonstrated outstanding performance with all considered metrics, with an overall accuracy of 99.90% (see Table 9). In future research, the proposed SHM strategy of experimental laboratory testing in a water tank needs to be validated using a more realistic environment.