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Special Issue "Structural Health Monitoring with Acoustic Emission"

A special issue of Materials (ISSN 1996-1944).

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 141

Special Issue Editors

Dr. Emmanuel Ramasso
E-Mail Website
Guest Editor
Université Bourgogne Franche-Comté, Besancon, France
Interests: formalisms for uncertainty representation; structural health monitoring with acoustic emission; prognostics and health management; image and video analysis
Ms. Neha Chandarana
E-Mail Website
Guest Editor
University of Manchester, Manchester, United Kingdom
Interests: acoustic emission, structural health monitoring, fibre reinforced composite materials, distributed optical fibres, embedded sensing, guided waves, piezoelectric sensors, machine learning, clustering

Special Issue Information

Dear Colleagues,

The present Special Issue focuses on the use of acoustic emission (AE) as a structural health monitoring (SHM) technique for damage classification using data-driven algorithms. The use of such algorithms is well documented in the literature, but the exploitation of the potential of machine learning methods is limited in regards to AE data analysis. One of the inherent challenges with AE time-series data is related to the non-uniform time spacing of the signals obtained from a material.

Many data-driven models have been presented based on various different algorithms. They are able to discriminate between different failure modes in composites, for example, matrix cracking and fibre breakage. However, the number of studies that demonstrate the applicability of these models for on-line SHM—where new data arrive gradually, and are of interest in order to track the initiation and growth of the damage—are limited.

We welcome research papers that aim to address the challenge of on-line AE-based SHM that makes use of machine learning algorithms for data analysis. Contributions can focus on any aspect of the AE data processing chain, from sensors to decision-making, including novel data analysis and interpretation methodologies for SHM. We particularly welcome studies conducted on composite materials, but studies concerning other material systems will also be considered.

The following is a non-exhaustive list of the exemplary subject themes for this Special Issue:

  • New sensors, using a combination or comparison of AE sensors such as PZT, NEMS/MEMS, optical fibres, flexible electronics, and wireless sensing.
  • Data pre-processing, covering topics such as filtering, feature extraction, feature selection, localisation, sensor fault detection, compensation techniques for managing environmental effects and operational modes, and anomaly detection.
  • Pattern recognition and machine learning, covering supervised, unsupervised, and partially supervised classification, as well as prediction and prognostics, and evaluation methods.
  • Challenging application cases with AE data obtained from machinery, materials testing, and process monitoring. Examples include quasi-static and fatigue testing, multi-material and multi-sensor approaches, and application in extreme environments.

Papers focusing on the batch analysis of data without demonstrating applicability for online processing will be considered as less relevant for the Issue. Appropriate submissions should explicitly state the contributions and properly describe related works.

Data and code sharing using supporting websites such as NASA, Harvard, Mendeley, or Github, will be valuable for this Special Issue.

Dr. Emmanuel Ramasso
Ms. Neha Chandarana
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • acoustic emission
  • structural health monitoring
  • damage classification
  • algorithms
  • machine learning methods

Published Papers

There is no accepted submissions to this special issue at this moment.
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