The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

Abstract

Abstract:Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

“…The original data of the FRF waveform has real and imaginary values, which means that a total of four basic data sets for one FRF waveform are constructed. Next, 20 characteristics were derived by calculating area, root mean square (RMS), cross-sectional primary moment, center, and standard deviation using the four basic data sets [29]. Removal of trends implies subtracting the mean or the best fit line from the data (meaning the least squares).…”

Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection

“…Understanding structural performance, and assessing structural condition, and providing real-time decision making are crucial components in structural health monitoring (SHM), in order to avoid catastrophic events, and improve public safety [1]. As compared to conventionally vision-based techniques [2,3] or vibration-based techniques [4][5][6] that are mostly sensitive only to severe damage, guided wave-based techniques are often capable of identifying more minute damage and a tiny anomaly in active manner [7]. Guided waves display in different forms, such as the axial wave, flexural wave, shear wave, Rayleigh wave and Lamb wave.…”

Vibration‐based structural condition assessment using convolution neural networks

“…More recent reviews can be found in previous studies [4][5][6] and the references therein. More recently, Kong et al 7 provided a state-of-the-art review on vibration-based structural damage identification methods and concluded that there was no agreement among researchers regarding the suitability of available methodologies. In general, the scope of SHM is broad and requires understanding of and contributions from many disciplines.…”

“…A detailed breakdown of the various methods including those derived from statistical and probabilistic approaches is presented in previous studies. 2,7 In the late 1980s, notable contributions emerged that demonstrated the feasibility of using NNs to detect damage in civil structural systems. Early studies [13][14][15][16][17] introduced various approaches and demonstrated implementations of NNs to detect changes in system dynamics (particularly due to damage) from a series of direct or processed measurements.…”