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 data-driven 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, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring 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 health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications.

Vortex-induced vibration (VIV) has been occasionally observed on a few long-span steel box-girder suspension bridges. The underlying mechanism of VIV is very complicated and reliable theoretical methods for prediction of VIV have not been established yet. Structural health monitoring (SHM) technology can provide a large amount of data for further understanding of VIV. Automatic identification of VIV events from massive, continuous long-term monitoring data is a non-trivial task. In this study, a method based on the random decrement technique (RDT) is proposed to identify the VIV response automatically from the massive acceleration response without manual intervention. The raw acceleration data is first processed by RDT and it is found that the RDT-processed data show different characteristics for the VIV response and conventional random response. A threshold based on the coefficient of variation (COV) of peak values of processed data is defined to distinguish between the two kinds of responses. Both random vibration and VIV for a three-DOF (degree-of-freedom) mass-spring-damper system are obtained by numerical simulation to verify the proposed method. The method is finally applied to the Xihoumen suspension bridge for identifying VIV response from three-month monitoring data. It is shown that the proposed method performs comparably with the method of novelty detection. A total of 60 VIV events have been successfully identified. Vortex-induced vibrations for the second to ninth vertical modes with modal frequency within 0.1~0.5 Hz occurs at wind velocity 5–18 m/s, with wind direction nearly perpendicular to bridge axis. Amplitude of VIV generally decreases with increase of wind turbulence intensity; however, noticeable VIV amplitude are still observed for turbulence intensity up to 13% in some cases.

https://www.mdpi.com/2076-3417/9/10/2049/pdf

Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method

This review identifies current machine-learning algorithms implemented in building structural health monitoring systems and their success in determining the level of damage in a hierarchical classification. The integration of physical models, feature extraction techniques, uncertainty management, parameter estimation, and finite element model analysis are used to implement data-driven model detection systems for SHM system design. A total of 68 articles using ANN, CNN and SVM, in combination with preprocessing techniques, were analyzed corresponding to the period 2011–2022. The application of these techniques in structural condition monitoring improves the reliability and performance of these systems.

https://www.mdpi.com/2076-3417/12/21/10754/pdf?version=1666619943

Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures

............................................................................................................... 3 ANOTĀCIJA ................................................................................................................ 4 ACKNOWLEDGEMENTS ....................................................................................................... 5 NOMENCLATURE AND ABBREVIATIONS ........................................................................ 9 INTRODUCTION .............................................................................................................. 10 Motivation and scope............................................................................................................ 10 Aims of the thesis ................................................................................................................. 11 Tasks of the thesis................................................................................................................. 11 Scientific novelty .................................................................................................................. 11 Practical significance of the thesis ........................................................................................ 12 Methodology of the research ................................................................................................ 12 Applicability limitations of the developed methods ............................................................. 13 The arguments for defence of the thesis ............................................................................... 13 Structure of the thesis ........................................................................................................... 14 1. TRENDS IN STRUCTURAL DAMAGE ASSESSMENT ................................................. 15 1.1. Failure modes in polymeric composite structures and pre-stressed concrete sleepers .. 15 1.2. Structural Health Monitoring......................................................................................... 18 1.2.1. Active SHM ............................................................................................................ 21 1.2.2. Passive SHM ........................................................................................................... 22 1.3. Vibration-based damage identification .......................................................................... 25 1.3.1. Modal methods ........................................................................................................ 26 1.3.2. Continuous wavelet transform ................................................................................ 29 1.4. Supervised learning using extraction of damage-sensitive features .............................. 33 1.4.1. Classification algorithms k-nearest neighbours .................................................... 35 1.4.2. Classification algorithms – Linear discriminant ..................................................... 36 1.4.3. Classification algorithms – Decision trees .............................................................. 37 1.4.4. Data partitioning ..................................................................................................... 38 1.4.5. Performance optimization and evaluation .............................................................. 39 2. DAMAGE LOCALIZATION ALGORITHMS................................................................... 41 2.1 Continuous spatial wavelet transform ............................................................................ 41 2.2 Thresholded variance of normalized wavelet scalogram ............................................... 45 2.3. Mode shape curvature square ........................................................................................ 46 2.4. Data classification .......................................................................................................... 48 PART I – MODE SHAPE TRANSFORMATIONS ................................................................ 50 3. DAMAGE LOCALIZATION IN 1-D STRUCTURES ....................................................... 51

Structural Damage Identification based on Mode Shape Transformations and Supervised Learning

Damage detection of in-service steel railway bridges using a fine k-nearest neighbor machine learning classifier

Structural health monitoring (SHM) is an important research area, which interest is the damage identification process. Different information about the state of the structure can be obtained in the process, among them, detection, localization and classification of damages are mainly studied in order to avoid unnecessary maintenance procedures in civilian and military structures in several applications. To carry out SHM in practice, two different approaches are used, the first is based on modelling which requires to build a very detailed model of the structure, while the second is by means of data-driven approaches which use information collected from the structure under different structural states and perform an analysis by means of data analysis . For the latter, statistical analysis and pattern recognition have demonstrated its effectiveness in the damage identification process because real information is obtained from the structure through sensors installed permanently to the observed object allowing a real-time monitoring. This chapter describes a damage detection and classification methodology, which makes use of a piezoelectric active system which works in several actuation phases and that is attached to the structure under evaluation, principal component analysis, and machine learning algorithms working as a pattern recognition methodology. In the chapter, the description of the developed approach and the results when it is tested in one aluminum plate are also included.

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Diego Alexander TibaduizaBurgos

Papers

Data-driven methodologies for structural damage detection based on machine learning applications