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Smart Structures and IoT-Based Health Monitoring for Buildings
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Smart Structures and IoT-Based Health Monitoring for Buildings

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1038

Editors

Dr. Paweł Karol Frankowski

E-Mail Website
Guest Editor
Faculty of Computer Science and Telecommunications, Maritime University of Szczecin, Wały Chrobrego 1-2, 70-500 Szczecin, Poland
Interests: NDT; SHM; reinforced concrete; machine learning; eddy current; MFL; IoT
Dr. Marcin Mąka

E-Mail Website
Guest Editor
Faculty of Computer Science and Telecommunications, Maritime University of Szczecin, Wały Chrobrego 1-2, 70-500 Szczecin, Poland
Interests: IoT; sensors; communication protocols; modulations; radio communication

Special Issue Information

Dear Colleagues,

The integration of smart structures, non-destructive testing, and IoT-based health monitoring is transforming building performance, safety, and sustainability. By embedding sensors and real-time data analytics into structural systems, these technologies enable proactive damage detection, adaptive responses to environmental loads, and predictive maintenance, extending service life while minimizing costs and downtime.

This Special Issue, "Smart Structures and IoT-Based Health Monitoring for Buildings," invites cutting-edge research on self-sensing materials, wireless sensor networks, edge computing, and AI-driven analytics for resilient built environments. We welcome contributions on:

  • NDT methods for building evaluation
  • structural health monitoring (SHM)
  • Self-sensing and multifunctional materials
  • IoT architectures and low-power WSNs for SHM
  • AI/ML algorithms for damage identification
  • Smart sensors
  • Validation studies on buildings under various conditions
  • Life-cycle assessments and case studies demonstrating enhanced resilience

We encourage original research articles, review papers, and practical case studies that highlight performance improvements and field applicability. This Special Issue aims to foster interdisciplinary dialogue among engineers, materials scientists, data experts, and architects to advance intelligent buildings.

Dr. Paweł Karol Frankowski
Dr. Marcin Mąka
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • reinforced concrete
  • civil engineering
  • non-destructive testing (NDT)
  • structural health monitoring (SHM)
  • Internet of Things (IoT)
  • machine learning
  • artificial intelligence

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Published Papers (1 paper)

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Review

47 pages, 7116 KB  
Review
Vision-Based Displacement Measurement for Structural Health Monitoring: A Metrology-Oriented Review of Uncertainty Quantification
by Arman Neyestani, Francesco Picariello, Ioan Tudosa, Michela Monaco, Luca De Vito and Mauro D’Arco
Buildings 2026, 16(13), 2659; https://doi.org/10.3390/buildings16132659 - 4 Jul 2026
Viewed by 683
Abstract
This paper presents a metrology-oriented review of vision-based displacement and deformation measurement for civil structural health monitoring (SHM), with an emphasis on field robustness and uncertainty quantification (UQ). The review focuses on image- and video-based methods that convert visual information into quantitative physical [...] Read more.
This paper presents a metrology-oriented review of vision-based displacement and deformation measurement for civil structural health monitoring (SHM), with an emphasis on field robustness and uncertainty quantification (UQ). The review focuses on image- and video-based methods that convert visual information into quantitative physical measurements, such as displacement, strain, or derived dynamic indicators. The literature is organized according to the main stages of the measurement chain: image formation, image-plane motion estimation, and geometric conversion to metric motion. Within this framework, measurement pipelines are interpreted through three levels of geometric mapping, namely, a scalar scale-factor model, a planar homography-based model, and a full Jacobian-based model. The review synthesizes major method families, including marker-based and markerless tracking, feature-based tracking, optical flow, digital image correlation (DIC), phase-based motion magnification, edge-based estimators, fixed- and moving-camera configurations, UAV-based acquisition with ego-motion compensation, hybrid vision–sensor fusion, and deep-learning-enhanced pipelines. A structured taxonomy of uncertainty sources is then presented along the processing chain, covering camera geometry and calibration, imaging noise and blur, quantization, timing and synchronization, environmental disturbances, optical turbulence and heat haze, platform motion, algorithmic failure modes, and reference-sensor uncertainty. The paper also compares UQ practices, including GUM-aligned analytical propagation, Monte Carlo methods, DIC-specific error budgets, bootstrap and resampling strategies, and probabilistic deep learning. The main contribution of this review is to connect computer-vision-based displacement pipelines with metrological requirements by explicitly linking measurement models, uncertainty sources, UQ methods, and field-validation evidence within a unified framework. A practical uncertainty-budget template is compiled to support traceable reporting across different pipelines and deployment scenarios. The paper concludes with prioritized research gaps and future directions, including standardized benchmarks and datasets, traceable UQ for moving-camera systems, multi-sensor fusion with end-to-end uncertainty propagation, long-term drift characterization, optical-turbulence and adverse-weather modeling, validated subpixel limits at extreme range, probabilistic deep learning–metrology integration, and standardized reporting practices. Full article
(This article belongs to the Special Issue Smart Structures and IoT-Based Health Monitoring for Buildings)
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