New Technologies in Structural Health Monitoring of Buildings and Infrastructure

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

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 5087

Special Issue Editors


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Guest Editor
Associate Professor, Engineering Faculty, Lusófona University, Campo Grande 376, 1749-024 Lisboa, Portugal
Interests: structural health monitoring; finite element method; computational mechanics

E-Mail Website
Guest Editor
Engineering Faculty, Lusófona University, Campo Grande 376, 1749-024 Lisboa, Portugal
Interests: structural health monitoring; management of bridges; machine learning; damage identification
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Civil Engineering and Management, Faculty of Engineering Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Interests: structural health monitoring; computer-vision-based measurement; signal processing and interpretation; structural dynamics; bridge thermal response; data-driven modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ionut Moldovan, Elói Figueiredo, Rolands Kromanis, and Yun-Lai Zhou are organizing a Special Issue (SI) new technologies in structural health monitoring (SHM) of buildings and infrastructure. This SI will be published in the journal Buildings, which is ranked Q1 according to Scimago and has an impact factor of 3.324 (2021). You can find all the details in the following link: https://www.mdpi.com/journal/buildings/special_issues/E09145I0FG.

The submission deadline is October  31, 2023.

SHM of buildings and infrastructure has been an exceedingly active research topic in the last decade, leading to the emergence of new technologies in basically all involved areas. On the sensing side, conventional sensing technologies have been complemented by distributed fibre optic and wireless sensor networks, accelerometers mounted on vehicles and smartphones, and a vast array of non-contact sensors, such as cameras and lasers. Civil engineering structures are one-of-a-kind structures that cannot be intentionally damaged to gain information on their behaviour when damage is present; to cope with this issue, the recent concepts of hybrid SHM and transfer learning are aimed at obtaining data corresponding to structural conditions that rarely occur (extreme environmental and operational conditions, damage) from numerical models of the structure and from similar structures where such data are available, respectively. Moreover, concerns about the impact of climate change on buildings and infrastructure have led to the integration of SHM and risk assessment technology to better identify vulnerable structures. Such advances have been supported by large improvements in machine learning technology, where the continued development of unsupervised and supervised algorithms has been boosted by new artificial intelligence technologies such as deep learning.

Therefore, this SI intends to bring together publications involving new technologies for the SHM of buildings and infrastructure, acting at all levels (sensing, data augmentation, machine learning etc) to highlight the current capabilities and future trends.

Dr. Ionut Dragos Moldovan
Prof. Dr. Eloi Figueiredo
Dr. Rolands Kromanis
Prof. Dr. Yunlai Zhou
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 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

  • sensors
  • hybrid SHM
  • transfer learning
  • machine learning algorithms
  • smartphone sensing
  • digital twins
  • finite element modelling
  • augmented reality
  • virtual reality
  • advanced sensing technologies

Published Papers (2 papers)

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Research

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13 pages, 4696 KiB  
Article
Transfer Learning for Structural Health Monitoring in Bridges That Underwent Retrofitting
by Marcus Omori Yano, Eloi Figueiredo, Samuel da Silva, Alexandre Cury and Ionut Moldovan
Buildings 2023, 13(9), 2323; https://doi.org/10.3390/buildings13092323 - 13 Sep 2023
Cited by 1 | Viewed by 1452
Abstract
Bridges are built to last more than 100 years, spanning many human generations. Throughout their lifetime, their service requirements may change, or they age and often suffer a material degradation process that can lead to the need of retrofitting. In bridge engineering, retrofitting [...] Read more.
Bridges are built to last more than 100 years, spanning many human generations. Throughout their lifetime, their service requirements may change, or they age and often suffer a material degradation process that can lead to the need of retrofitting. In bridge engineering, retrofitting refers to the strengthening of existing structures to make them more resistant and to increase the lifespan of bridges. Retrofitting normally increases the stiffness of bridge components, which can cause significant changes in the global modal properties. In the context of structural health monitoring, a classifier trained with datasets before retrofitting will most likely output many outliers after retrofitting, based on the premise that the new observations do not share the same underlying distribution. Therefore, how can long-term monitoring data from one bridge (labeled source domain) be reused to create a classifier that generalizes to the same bridge after retrofitting (unlabeled target domain)? This paper presents a novel approach based on transfer learning in the context of domain adaptation on datasets from two real bridges subjected to retrofit and under-monitoring programs. Based on the assumption that both bridges are undamaged before retrofitting, the results show that transfer learning can support the long-term damage detection process based on a classification using an outlier detection strategy. Full article
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Review

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22 pages, 2152 KiB  
Review
Development of Intelligent Technologies in SHM on the Innovative Diagnosis in Civil Engineering—A Comprehensive Review
by Dhanasingh Sivalinga Vijayan, Arvindan Sivasuriyan, Parthiban Devarajan, Martin Krejsa, Marek Chalecki, Mariusz Żółtowski, Alicja Kozarzewska and Eugeniusz Koda
Buildings 2023, 13(8), 1903; https://doi.org/10.3390/buildings13081903 - 26 Jul 2023
Cited by 2 | Viewed by 2833
Abstract
This comprehensive review focuses on the integration of intelligent technologies, such as the Internet of Things (IoT), Artificial intelligence (AI), and Nondestructive Testing (NDT), in the Structural Health Monitoring (SHM) field of civil engineering. The article discusses intelligent technologies in SHM for residential, [...] Read more.
This comprehensive review focuses on the integration of intelligent technologies, such as the Internet of Things (IoT), Artificial intelligence (AI), and Nondestructive Testing (NDT), in the Structural Health Monitoring (SHM) field of civil engineering. The article discusses intelligent technologies in SHM for residential, commercial, industrial, historical, and special buildings, such as nuclear power plants (NPPs). With the incorporation of intelligent technologies, there have been remarkable advancements in SHM, a crucial aspect of infrastructure safety, reliability, and durability. The combination of SHM and intelligent technologies provides a cost-effective and efficient building monitoring approach, significantly contributing to energy and resource conservation. This article explores using electronic instruments, such as sensors, microcontrollers, and embedded systems, to measure displacement, force, strain, and temperature, which are crucial for detecting structural damage. Implementing intelligent technologies in SHM reduces the reliance on manual and hazardous inspection practices, simplifying and reducing the cost of building monitoring. The article highlights the social, economic, and environmental benefits of adopting intelligent technologies in SHM by presenting key findings from existing research. This review aims to increase the reader’s understanding of the significance of these technologies in enhancing the efficiency of SHM in civil engineering by illuminating their advancements and applications. Full article
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