Health Monitoring of Cement-Based Structures/Materials: Signal Processing and Artificial Intelligence Techniques

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2286

Special Issue Editors


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Guest Editor
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, v. Brecce Bianche 12, 60131 Ancona, Italy
Interests: non-invasive measurement techniques; measurement procedures; measurement uncertainty; wearable sensors; physiological signals; comfort and wellbeing
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Guest Editor
Department of Industrial Engineering and Mathematical Sciences (DIISM), Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: measurement techniques; signal and image processing; structural dynamics; array acoustics; sensor technology; non-destructive testing

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Guest Editor
Department of Materials, Environmental Sciences and Urban Planning SIMAU, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: materials engineering; sustainability; durable materials; building materials; mortar and concrete technologies; alternative binders; recycling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The costs for repairing and maintaining cement-based structures currently represent a significant proportion of the gross national product in developed countries. Monitoring the health status of these structures is of utmost importance in order to plan timely management interventions and avoid the eventual failure of the structure itself. Many different measurement techniques can be adopted to monitor the health status of cement-based structures, including: electrical impedance measurements (to detect the ingress of contaminants, monitor the curing period, detect strain and crack formation, identify corrosion processes, sense temperature changes, etc.), computer vision techniques (e.g., to detect cracks or surface defects), ultrasound methods and/or thermal imaging (e.g., to evaluate the moisture content and the presence of delamination phenomena), and strain gauges (widely used in structural health monitoring (SHM) to monitor loads and related strain). In addition, various sensors can be combined to obtain more accurate results (sensor fusion).

Regardless of the technique, suitable signal processing techniques are needed to increase the robustness of the approach and safely exploit data provided as useful information for labelling the health status of the structures/materials under investigation. These processing strategies also involve artificial intelligence (AI), which in recent years has gained a lot of importance on the interpretation of big data collected in monitoring activities (e.g., to search for patterns typical of certain events like crack formation or to predict the strength or the durability of a certain type of concrete).

This Special Issue is intended to publish original research and review papers dealing with the analysis and interpretation of data collected in the monitoring/inspection of cement-based structures or elements.

Suitable topics include, but are not limited to, the following:

  • Signal processing techniques for different types of signals acquired on cement-based structures and materials;
  • Signal analysis related to non-destructive techniques (NDTs);
  • Analysis of electrical impedance/resistivity measurements in cement-based structures and materials;
  • AI techniques for the monitoring of cement-based structures and materials;
  • AI techniques for crack detection;
  • Signal processing techniques for SHM;
  • Image processing for computer vision and thermal imaging on cement-based structures and materials;
  • Analysis of correlations between electrical resistivity and ingress of contaminants;
  • Data processing in sensor fusion applications.

Dr. Gloria Cosoli
Dr. Paolo Chiariotti
Dr. Eng. Alessandra Mobili
Guest Editors

Manuscript Submission Information

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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. Signals is an international peer-reviewed open access quarterly 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 1000 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.

Keywords

  • Cement-based structures/materials 
  • Monitoring techniques 
  • Non-destructive techniques (NDTs) 
  • Structural health monitoring (SHM) 
  • Signal processing 
  • Self-sensing materials 
  • Sensor fusion 
  • Artificial Intelligence 
  • Durability

Published Papers (2 papers)

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Research

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23 pages, 10816 KiB  
Article
Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck
by Mustafa Khudhair and Nenad Gucunski
Signals 2023, 4(4), 836-858; https://doi.org/10.3390/signals4040046 - 4 Dec 2023
Cited by 2 | Viewed by 776
Abstract
Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues [...] Read more.
Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of the ER and HCP measurements using data from multiple NDE technologies. Full article
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Review

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36 pages, 2452 KiB  
Review
Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications
by Claudia Ferraris, Gianluca Amprimo and Giuseppe Pettiti
Signals 2023, 4(3), 539-574; https://doi.org/10.3390/signals4030029 - 24 Jul 2023
Cited by 1 | Viewed by 2464
Abstract
Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement-based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, [...] Read more.
Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement-based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, manual visual inspection has been the only viable structural health monitoring (SHM) solution. Technological advances have led to the development of sensors and devices suitable for the early detection of changes in structures and materials using automated or semi-automated approaches. Recently, solutions based on computer vision, imaging, and video signal analysis have gained momentum in SHM due to increased processing and storage performance, the ability to easily monitor inaccessible areas (e.g., through drones and robots), and recent progress in artificial intelligence fueling automated recognition and classification processes. This paper summarizes the most recent studies (2018–2022) that have proposed solutions for the SHM of infrastructures based on optical devices, computer vision, and image processing approaches. The preliminary analysis revealed an initial subdivision into two macro-categories: studies that implemented vision systems and studies that accessed image datasets. Each study was then analyzed in more detail to present a qualitative description related to the target structures, type of monitoring, instrumentation and data source, methodological approach, and main results, thus providing a more comprehensive overview of the recent applications in SHM and facilitating comparisons between the studies. Full article
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