Damage detection of offshore jacket structures using structural vibration measurements: Application of a new hybrid machine learning method

The artificial intelligence (AI) technologies, such as meta-heuristic computing and deep learning, have provides solid technical support for structural health monitoring (SHM) of offshore jackets. In this paper, a physics-enhanced AI method based on the parametric damage identification is developed for SHM of the offshore jacket structures. In this new method, a hybrid kernel function-based kernel extreme learning machine (HKELM) is proposed to construct an AI structure to enhance the SHM detection capacity on the structural modal parameters extracted by the parametric damage identification technique. Simulation analysis is carried out to verify the feasibility of the HKELM-based method using the response signals of a jacket structure under impact force, and the result demonstrates good damage location capability of the proposed method. Furthermore, to select proper parameters for the HKELM, the whale optimization algorithm (WOA) is applied to optimize the values of the regularization coefficient and kernel parameter array. Then, the wavelet denoising (WD) is introduced to preprocess the vibration signals to improve the damage detection ability of the WOA-HKELM. Lastly, experimental tests are performed to validate the effectiveness of the proposed method in utilizing the structural modal parameters for identifying the structural damage. The analysis results illustrate that the proposed method produces satisfactory damage location ability under the influence of actual noise in the vibration signals. Meanwhile, this method has broad application prospects in the SHM of other offshore structures.