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Buildings
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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: closed (31 March 2025) | Viewed by 4402

Special Issue Editors

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
Dr. Siamak Pedrammehr

E-Mail Website
Guest Editor
1. Faculty of Design, Tabriz Islamic Art University, Tabriz, Iran
2. Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, Australia
Interests: modelling and simulation; manufacturing; applied AI; socio-industrial studies
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
Dr. Siamak Pedrammehr
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. Buildings 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

  • autonomous sensor
  • structural monitoring
  • AI and optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

18 pages, 5741 KiB  
Article
Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks
by Abbas Ghaffari, Yaser Shahbazi, Mohsen Mokhtari Kashavar, Mohammad Fotouhi and Siamak Pedrammehr
Buildings 2024, 14(10), 3261; https://doi.org/10.3390/buildings14103261 - 15 Oct 2024
Cited by 4 | Viewed by 2498
Abstract
This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse [...] Read more.
This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas. Full article
(This article belongs to the Special Issue Autonomous Strategies for Structural Health Monitoring)
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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
Viewed by 1209
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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