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Buildings
Special Issues
Autonomous Strategies for Structural Health Monitoring
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Autonomous Strategies for Structural Health Monitoring

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 177

Special Issue Editor

Dr. Mohammad Fotouhi

E-Mail Website
Guest Editor
Assistant Professor, Structural Health Monitoring, TU Delft, Delft, The Netherlands
Interests: structural health monitoring; development of sensor nodes; smart materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue aims to highlight the latest advancements, challenges, and future directions in the application of autonomous systems for structural health monitoring in building environments and related applications. We are seeking original research articles, case studies, and comprehensive review papers that address, but are not limited to, the following topics:

  • Advances in autonomous sensor technologies for structural monitoring.
  • AI and machine learning applications in predictive maintenance, optimization, and structural health assessment.
  • Case studies of autonomous monitoring systems in various structures like bridges, buildings, and industrial facilities, with a focus on AI and optimization applications in improving monitoring efficiency.
  • Challenges and solutions in data acquisition, processing, and interpretation for autonomous structural health monitoring, using AI and optimization techniques.
  • Integration of IoT and smart technologies in structural health monitoring systems.

Dr. Mohammad Fotouhi
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. Buildings is an international peer-reviewed open access monthly 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

  • autonomous sensor
  • structural monitoring
  • AI and optimization

Published Papers (1 paper)

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Research

32 pages, 15369 KiB  
Article
Structural Condition Assessment of Steel Anchorage Using Convolutional Neural Networks and Admittance Response
by Duc-Duy Ho, Jeong-Tae Kim, Nhat-Duc Hoang, Manh-Hung Tran, Ananta Man Singh Pradhan, Gia Toai Truong and Thanh-Canh Huynh
Buildings 2024, 14(6), 1635; https://doi.org/10.3390/buildings14061635 - 3 Jun 2024
Abstract
Structural damage in the steel bridge anchorage, if not diagnosed early, could pose a severe risk of structural collapse. Previous studies have mainly focused on diagnosing prestress loss as a specific type of damage. This study is among the first for the automated [...] Read more.
Structural damage in the steel bridge anchorage, if not diagnosed early, could pose a severe risk of structural collapse. Previous studies have mainly focused on diagnosing prestress loss as a specific type of damage. This study is among the first for the automated identification of multiple types of anchorage damage, including strand damage and bearing plate damage, using deep learning combined with the EMA (electromechanical admittance) technique. The proposed approach employs the 1D CNN (one-dimensional convolutional neural network) algorithm to autonomously learn optimal features from the raw EMA data without complex transformations. The proposed approach is validated using the raw EMA response of a steel bridge anchorage specimen, which contains substantial nonlinearities in damage characteristics. A K-fold cross-validation approach is used to secure a rigorous performance evaluation and generalization across different scenarios. The method demonstrates superior performance compared to established 1D CNN models in assessing multiple damage types in the anchorage specimen, offering a potential alternative paradigm for data-driven damage identification in steel bridge anchorages. Full article
(This article belongs to the Special Issue Autonomous Strategies for Structural Health Monitoring)
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