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Buildings
Special Issues
Health Monitoring of Building Structures: Emerging Technologies and Approaches
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Health Monitoring of Building Structures: Emerging Technologies and Approaches

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5894

Special Issue Editors

Dr. Xingyu Wang

E-Mail
Guest Editor
Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA
Interests: advanced materials; nanocomposite development; surface engineering; machine learning enriched damage assessment; structural health monitoring; corrosion mitigation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ying Huang

E-Mail Website
Guest Editor
Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA
Interests: advanced; high performance; smart materials; smart cities; autonomous systems
Special Issues, Collections and Topics in MDPI journals
Dr. Chengcheng Tao

E-Mail Website
Guest Editor
School of Construction Management Technology, Purdue University, West Lafayette, IN 47907, USA
Interests: multi-scale modeling of materials; hazard-resilient infrastructure; machine learning application; multi-objective optimization; computational fluid dynamics; sustainable construction materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As urbanization continues to accelerate and the demand for sustainable and resilient infrastructure grows, the effective and reliable health monitoring of building structures becomes increasingly important. By ensuring the safety, serviceability, and durability of structures, Structural health monitoring (SHM) plays a crucial role in preventing structural failures, minimizing maintenance costs, and optimizing the lifespan of structures. In the last few decades, there has been considerable development in technologies and approaches, resulting in enhanced monitoring and evaluation of civil infrastructures. Therefore, this collection intends to provide valuable insights into cutting-edge research that addresses various aspects of health monitoring in building structures.

We invite contributions from researchers, engineers, and industry professionals working on innovative solutions. The submissions may include original research articles, review articles, and case studies; the aim of this collection is to offer a thorough understanding of the current progress in SHM and to promote continued research within the discipline.

The articles in this Special Issue will cover a wide range of topics, including:

  • Novel sensor technologies and monitoring systems for structural health assessment.
  • Application of machine learning and artificial intelligence for real-time monitoring and predictive maintenance in building structures.
  • The role of monitoring systems in mitigating the impacts of natural hazards, such as earthquakes, hurricanes, and floods.
  • Innovative non-destructive evaluation (NDE) techniques for assessing structural integrity.
  • Advanced algorithms, data processing, and analytics for improved damage detection and localization.
  • The future of health monitoring of building structures, including emerging trends and potential challenges.
  • Case studies of health monitoring implementations in various types of structures, including bridges, high-rise buildings, and historical monuments, etc.

Dr. Xingyu Wang
Prof. Dr. Ying Huang
Dr. Chengcheng Tao
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

  • structural health monitoring (SHM)
  • sensor technologies
  • data analytics and processing
  • machine learning and artificial intelligence
  • monitoring systems
  • damage detection and localization
  • non-destructive evaluation (NDE)
  • predictive maintenance

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

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Research

23 pages, 6241 KiB  
Article
Integration of Finite Element Analysis and Machine Learning for Assessing the Spatial-Temporal Conditions of Reinforced Concrete
by Junyi Duan, Huaixiao Yan, Chengcheng Tao, Xingyu Wang, Shanyue Guan and Yuxin Zhang
Buildings 2025, 15(3), 435; https://doi.org/10.3390/buildings15030435 - 30 Jan 2025
Viewed by 1311
Abstract
Composite reinforcements are attracting attention in the reinforced concrete (RC) field for their high corrosion resistance, low thermal conductivity, and low electromagnetic interference behavior. However, compared to metallic reinforcements, composites are less ductile and may lead to brittle failure. Three-point flexural tests provide [...] Read more.
Composite reinforcements are attracting attention in the reinforced concrete (RC) field for their high corrosion resistance, low thermal conductivity, and low electromagnetic interference behavior. However, compared to metallic reinforcements, composites are less ductile and may lead to brittle failure. Three-point flexural tests provide information on the mechanical behavior of metal- and composite-reinforced concrete beams with distinct crack patterns. The structural conditions and failure mechanisms can be defined based on stress change and crack propagation. This study employs finite element analysis (FEA) to simulate the mechanical responses of composite- and metal-reinforced concrete beans under three-point flexural tests and predict the crack propagation in the beams. Machine learning-based algorithms are trained using FEA data to assess the spatial–temporal conditions of the RC beams. The findings indicate that composite rebars provide better reinforcement than metallic rebars in terms of stress fields (30.27% less stress in composite rebars) and crack propagation (fewer cracks in composite RC beams), with the initiation of shear cracks and maximum von Mises stress in rebars being correlated. The findings highlight the effectiveness of the Random Forest Regression (RFR) algorithm (R2=0.96) in assessing RC beam conditions under flexural loads, offering insights for efficient industry applications. Full article
(This article belongs to the Special Issue Health Monitoring of Building Structures: Emerging Technologies and Approaches)
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16 pages, 6752 KiB  
Article
Empirical Case Study on Applying Artificial Intelligence and Unmanned Aerial Vehicles for the Efficient Visual Inspection of Residential Buildings
by Hyunkyu Shin, Jonghoon Kim, Kyonghoon Kim and Sanghyo Lee
Buildings 2023, 13(11), 2754; https://doi.org/10.3390/buildings13112754 - 31 Oct 2023
Cited by 8 | Viewed by 3604
Abstract
Continuous inspections and observations are required to preserve the safety and condition of buildings. Although the number of deteriorated buildings has increased over the years, traditional inspection methods are still used. However, this approach is time-consuming, costly, and carries the risk of poor [...] Read more.
Continuous inspections and observations are required to preserve the safety and condition of buildings. Although the number of deteriorated buildings has increased over the years, traditional inspection methods are still used. However, this approach is time-consuming, costly, and carries the risk of poor inspection owing to the subjective intervention of the inspector. To overcome these limitations, many recent studies have developed advanced inspection methods by integrating unmanned aerial vehicles (UAVs) and artificial intelligence (AI) methods during the visual inspection stage. However, the inspection approach using UAV and AI can vary in operation and data acquisition methods depending on the building structures. Notably, in the case of residential buildings, it is necessary to consider how to operate UAVs and how to apply AI due to privacy issues of residents and various exterior contour shapes. Thus, an empirical case study was adopted in this study to explore the integration of UAVs and artificial intelligence (AI) technology to inspect the condition of structures, focusing on residential buildings. As a result, this study proposed the field-adopted UAV operation method and AI-based defect detection model for adopting the residential buildings. Moreover, the lessons learned from holistic and descriptive analyses, which include drone application limitations, points of improvement of data collection, and items to be considered when AI and UAV based inspection for residential buildings, are summarized in this paper. The discussed problems and results derived from this study can contribute to future AI- and UAV-based building inspections. Full article
(This article belongs to the Special Issue Health Monitoring of Building Structures: Emerging Technologies and Approaches)
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