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Sensors for Structural Health Monitoring of Structures and Infrastructures
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Sensors for Structural Health Monitoring of Structures and Infrastructures

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 562

Special Issue Editors

Dr. Kevin Kuang

E-Mail Website
Guest Editor
Civil and Environmental Engineering, College of Design and Engineering, National University of Singapore, Singapore 117576, Singapore
Interests: structural health monitoring; optical fiber sensors; smart paint; self-healing concrete; damage detection; civil engineering
Prof. Dr. Wing Chiu

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Monash University, Wellington Rd, Clayton, VIC 3800, Australia
Interests: structural health monitoring; non-destructive testing; evaluation stress wave research; composite structures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Seunghee Park

E-Mail Website
Guest Editor
Department of Civil Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Republic of Korea
Interests: SHM/NDT techniques; disaster mitigation/monitoring; artificial intelligence (AI) app; internet of things (IoT) app; smart construction
Prof. Dr. Honghu Zhu

E-Mail Website
Guest Editor
School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
Interests: structural health monitoring; multi-physical monitoring, assessment, and mitigation of geohazards; performance evaluation of underground pipelines and tunnels
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural Health Monitoring (SHM) presents a systematic, objective, and predictive approach to structural assessment that enables the early detection of potential issues, informed decision-making in maintenance practices, and the enhancement of overall structural safety and durability. SHM is a multidisciplinary field that has attracted professionals and researchers from diverse industries; each brings unique expertise in areas such as sensors, advanced materials, data communication, artificial intelligence, and system integration. The implementation of SHM projects involves the deployment of a diverse array of sensors, ranging from conventional ones, like accelerometers, electrical strain gauges, and displacement transducers, to more advanced options, like distributed optical fiber sensors, smart paints, computer-vision-based sensing, and acoustic emission sensors. Extracting damage features from sensor data allows for the assessment of structural integrity and the host structure's condition, providing crucial information about the presence, location, severity, and prognosis of any damage.

The specific operating constraints related to a particular engineering domain where SHM is to be implemented dictate the ideal type of sensing for the application. Designing and adapting sensors to meet the distinct sensing requirements of each use case is crucial in ensuring a successful SHM project. Here, the application of innovative sensing principles from diverse disciplines and the cross-fertilization of ideas across various engineering fields will significantly contribute to realizing the objectives of Structural Health Monitoring (SHM).

This Special Issue invites contributions that address the use of innovative sensors, sensing principles, or the innovative use of traditional sensors and transducers to solve the sensing needs of a particular SHM project. In particular, papers should clearly show novel contributions and innovative applications covering any of the following topics, or others, within Structural Health Monitoring:

  • Novel sensors.
  • Optical fiber sensors.
  • Smart paints.
  • Luminescence-based sensing systems, e.g., chemiluminescence.
  • Large strain sensors.
  • Sensors for extreme environments.
  • Vision-based sensors.
  • IoT and cloud-linked sensors.
  • Pattern recognition and deep learning with smart sensor systems.
  • Self-sensing composite materials.
  • Bio-inspired sensor networks.
  • Self-sensing and self-healing concrete materials.

Dr. Kevin Kuang
Prof. Dr. Wing Chiu
Prof. Dr. Seunghee Park
Prof. Dr. Honghu Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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. Sensors 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 2600 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

  • novel sensors
  • optical fiber sensors
  • smart paints
  • luminescence-based sensing systems, e.g., chemiluminescence
  • large strain sensors
  • sensors for extreme environments
  • vision-based sensors
  • IoT and cloud-linked sensors
  • pattern recognition and deep learning with smart sensor systems
  • self-sensing composite materials
  • bio-inspired sensor networks
  • self-sensing and self-healing concrete materials

Published Papers (1 paper)

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Research

25 pages, 5486 KiB  
Article
Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon
by Benjamin Steven Vien, Thomas Kuen, Louis Raymond Francis Rose and Wing Kong Chiu
Sensors 2024, 24(8), 2537; https://doi.org/10.3390/s24082537 - 15 Apr 2024
Viewed by 371
Abstract
This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water’s Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas [...] Read more.
This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water’s Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model’s accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model’s predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors. Full article
(This article belongs to the Special Issue Sensors for Structural Health Monitoring of Structures and Infrastructures)
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