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Applied Sciences
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Artificial Intelligence-Based Structural Health Monitoring
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Artificial Intelligence-Based Structural Health Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 12616

Special Issue Editor

Prof. Dr. Seunghee Park

E-Mail Website
Guest Editor
School of Civil, Architectural, and Environmental Engineering, Sungkyunkwan University, 2066, Seoburo, Jangangu, Suwon, Gyeonggido 16419, Republic of Korea
Interests: structural health monitoring; non-destructive evaluation; smart sensors; smart structures; damage detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The next decade is likely to witness a considerable rise of the smart city concept, where Structural Health Monitoring (SHM) and Artificial Intelligence (AI) play a significant role. By combining SHM and AI, the end-users and maintenance teams of a smart city can easily diagnose a “structure state” at every moment during a structure life cycle.

From the AI perspective, problems related to decision-making, such as damage diagnosis, corrosion detection, abnormal detection, etc., which are intellectually difficult to address for a human being,  can be described and solved with high accuracy by AI models. By gathering knowledge from past experience, computers can learn and understand a structure on the basis of a hierarchy of concepts.

Therefore, the focus of these Special Issues is on the achievements made by combining Structural Health Monitoring and Artificial Intelligence techniques. We invite academic researchers and civil engineering specialists to contribute original research articles which discuss issues related, but not limited to, Structural Health Monitoring, AI-based Automated Diagnosis, Internet of Things, Machine Learning, Deep Learning, Data Mining, Smart Structure.

Prof. Dr. Seunghee Park
Guest Editor

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. Applied Sciences 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 2400 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

  • Structural Health Monitoring
  • AI-based Automated Diagnosis
  • Internet of Things
  • Machine Learning
  • Deep Learning
  • Data Mining
  • Smart Structure

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Published Papers (2 papers)

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Research

18 pages, 5875 KiB  
Article
Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
by Muhammad Tanveer, Byunghyun Kim, Jonghwa Hong, Sung-Han Sim and Soojin Cho
Appl. Sci. 2022, 12(24), 12786; https://doi.org/10.3390/app122412786 - 13 Dec 2022
Cited by 6 | Viewed by 2727
Abstract
Innovative concrete structure maintenance now requires automated computer vision inspection. Modern edge computing devices (ECDs), such as smartphones, can serve as sensing and computational platforms and can be integrated with deep learning models to detect on-site damage. Due to the fact that ECDs [...] Read more.
Innovative concrete structure maintenance now requires automated computer vision inspection. Modern edge computing devices (ECDs), such as smartphones, can serve as sensing and computational platforms and can be integrated with deep learning models to detect on-site damage. Due to the fact that ECDs have limited processing power, model sizes should be reduced to improve efficiency. This study compared and analyzed the performance of five semantic segmentation models that can be used for damage detection. These models are categorized as lightweight (ENet, CGNet, ESNet) and heavyweight (DDRNet-Slim23, DeepLabV3+ (ResNet-50)), based on the number of model parameters. All five models were trained and tested on the concrete structure dataset considering four types of damage: cracks, efflorescence, rebar exposure, and spalling. Overall, based on the performance evaluation and computational cost, CGNet outperformed the other models and was considered effective for the on-site damage detection application of ECDs. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Structural Health Monitoring)
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22 pages, 10103 KiB  
Article
Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
by Ronald Roberts, Gaspare Giancontieri, Laura Inzerillo and Gaetano Di Mino
Appl. Sci. 2020, 10(1), 319; https://doi.org/10.3390/app10010319 - 1 Jan 2020
Cited by 47 | Viewed by 6685
Abstract
Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There [...] Read more.
Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Structural Health Monitoring)
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