Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of DataDriven Applications The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses datadriven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, datadriven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a realtime solution for decision making; however, these approaches involve highperformance processors due to the high computational cost. As a contribution to the researchers working with datadriven algorithms and applications, this work presents a brief review of datadriven algorithms for damage identification in structural healthmonitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural healthmonitoring system. This review also includes information on the types of sensors used as well as on the development of datadriven algorithms for damage identification.

A DataDriven Damage Identification Framework Based on Transmissibility Function Datasets and OneDimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure Vibrationbased datadriven structural damage identification methods have gained large popularity because of their independence of highfidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damagesensitive features and suffer from high instability under the interference of random excitations. Moreover, the capability of conventional intelligent algorithms in damage feature extraction and noise influence suppression is limited. To address the above issues, a novel damage identification framework was established in this study by integrating massive datasets constructed by structural transmissibility functions (TFs) and a deep learning strategy based on onedimensional convolutional neural networks (1D CNNs). The effectiveness and efficiency of the TF1D CNN framework were verified using an American Society of Civil Engineers (ASCE) structural health monitoring benchmark structure, from which dynamic responses were captured, subject to white noise random excitations and a number of different damage scenarios. The damage identification accuracy of the framework was examined and compared with others by using different dataset types and intelligent algorithms. Specifically, compared with time series (TS) and fast Fourier transform (FFT)based frequencydomain signals, the TF signals exhibited more significant damagesensitive features and stronger stability under excitation interference. The utilization of 1D CNN, on the other hand, exhibited some unique advantages over other machine learning algorithms (e.g., traditional artificial neural networks (ANNs)), particularly in aspects of computation efficiency, generalization ability, and noise immunity when treating massive, highdimensional datasets. The developed TF1D CNN damage identification framework was demonstrated to have practical value in future applications.