A proposal of classification for machine-learning vibration-based damage identification methods - Review focus

Aug 5, 2026

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Recent advances in computing power and sensing technology have accelerated Structural Health Monitoring (SHM), turning damage identification into a data-intensive (“Big Data”) challenge. Data-driven Machine Learning (ML) methods are used across the SHM workflow, including pre- and post-processing of inputs, extraction of damage-sensitive features, and pattern recognition on measured responses. This paper discusses ML’s role in SHM, introduces a new classification scheme for ML applications, emphasizes vibration-based monitoring, and evaluates implications for historic structures using recent literature examples.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Objectives

Definitions

ML and data requirements

Learning paradigms and training sets

Concepts related to AI and data mining

ML in SHM and vibration-based monitoring

Classification scheme for ML applications

Implications for historic structures

Case studies in cultural heritage

Materials Research Proceedings 26 (2023) 593-598 [https://doi.org/10.21741/9781644902431-96](https://doi.org/10.21741/9781644902431-96) [A proposal of classification for machine-learning vibration-based](A proposal of classification for machine-learning vibration-based) damage identification methods Francesca Marafini 1, a * , Michele Betti 1, b , Gianni Bartoli 1,c , Giacomo Zini 1,d , Alberto Barontini 2,e , Nuno Mendes 2,f 1 Department of Civil and Environmental Engineering (DICEA), Florence, Italy 2 ISISE, Department of Civil Engineering, University of Minho, Guimarães, Portugal afrancesca. marafini@unifi. it bmichele. betti@unifi. it cgianni. bartoli@unifi. it, [d](dgiacomo.zini@unifi.it)[giacomo.zini@unifi.it](dgiacomo.zini@unifi.it) , [e](ealbe.barontini@gmail.com)[albe.barontini@gmail.com](ealbe.barontini@gmail.com) , [f](fnunomendes@civil.uminho.pt)[nunomendes@civil.uminho.pt](fnunomendes@civil.uminho.pt) Keywords: Machine Learning, Damage Identification, Vibration-Based SHM Abstract. Recent advances in computing power and sensing technology led to a significant evolution of Structural Health Monitoring (SHM) techniques, transforming SHM into a “Big Data”problem. The use of data-driven approaches for damage identification purposes, specifically Machine Learning (ML) methods, has gained popularity. ML can help at various levels of the SHM process: to pre-and post-process input data, extract damage sensitive features, and operate pattern recognition in measured data and output valuable information for damage identification. In this paper, the role of ML in SHM applications is discussed together with a new scheme for classifying ML applications in SHM, especially focusing on vibration-based monitoring, given its consolidated theoretical base. Finally, the implications of the application of these methods to historic structures are discussed, with a brief account of existing case studies. The proposed classification is exemplified using the most recent studies available in the literature on cultural heritage structures. Introduction Structural Health Monitoring (SHM), as the process of implementing strategies for Damage Identification (DI) [6], is an interdisciplinary field which has been successfully investigated over the last decades. Meanwhile, advancements in computational power and data science have opened new avenues for the development of data-driven approaches for SHM and Machine-Learning (ML) [7] . The research effort in this direction led to a literature explosion in ML, with the number of papers published on the topic rapidly increasing in the last 20 years (Figure 1) . Figure 1 – Number ofpublications per year in the last two decades. Research operated on Scopus for words in Title, Keywords and Abstract. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC. Materials Research Proceedings 26 (2023) 593-598 [https://doi.org/10.21741/9781644902431-96](https://doi.org/10.21741/9781644902431-96) Since the knowledge and methodologies on Data Science (DS) and Artificial Intelligence (AI) are increasingly being transferred to Civil Engineering, the need for a shared, clear glossary and framework is arising, to allow professionals and researchers to explore all capabilities of ML algorithms and connect transversal topics in the growing interdisciplinarity of SHM. Numerous published reviews have successfully formulated guidelines to approach the state-of-the-art research on ML applications to SHM [2],[3],[4],[8],[9], [10],[11] . However, differences in the classification of existing studies can still be found. The objectives ofthis paper are: (i) to provide basic notions to approach the study of ML applications to SHM, focusing on the use of vibration signatures,

FAQs

What is the main purpose of this paper?

To present basic notions for studying ML applications to SHM, propose a new classification methodology for reviewing existing studies, and briefly discuss issues for historic structures in vibration-based, data-driven SHM.

How does machine learning support vibration-based damage identification in SHM?

ML supports SHM by pre- and post-processing input data, extracting damage-sensitive features, and performing pattern recognition on measured responses to produce useful information for damage identification.

How are machine learning training processes categorized in this paper?

Training is classified as supervised or unsupervised depending on whether data are labelled or unlabelled, and it can also be semi-supervised when both labelled and unlabelled data are used; reinforcement learning is described as correcting predictions via trial-and-error with agents.

Aug 5, 2026

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