Dictionary learning-based damage detection under varying environmental conditions using only vibration responses of numerical model and real intact State: Verification on an experimental offshore jacket model

Abstract

Monitoring structural damage is critical for preserving the service life of engineering systems. In varying operational environments, the working loads are changing all the time and they are typically unknown; in such environments, access to damage data is difficult and sometimes even impossible, and generally, intact data of the system is available. From this standpoint, this study aims to propose a novel vibration-based method for damage detection of real systems using Dictionary Learning (DL) based on a FE model and real intact state under different uncertainties such as varying working loads. A Frequency Domain Sparse Representation-based Classification (FDSRC) model is designed via DL to learn the features and damage detection of real systems. The main contribution of this study is the training process of the proposed FDSRC using the raw frequency data of a real intact system and various states of the related Finite Element (FE) model under simple working loads (one working load), which is then tested using the raw frequency data of the various states of the real system under complex working loads (varying working loads), to make more realistic assumptions and simulate the operational conditions. The raw frequency data is generated using the Frequency Domain Decomposition (FDD) method. The performance of the proposed method is validated using real measurements from an experimental offshore jacket model. The results of the study indicate that in the case of changing the working load, the proposed method can achieve higher accuracy than other comparative methods. As a result, the proposed method is robust for structural damage monitoring under different uncertainties.