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Buildings
Special Issues
AI-Driven Health Monitoring and Management of Building and Energy Structures
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AI-Driven Health Monitoring and Management of Building and Energy Structures

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 980

Special Issue Editors

Dr. Huaping Wang

E-Mail Website
Guest Editor
School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China
Interests: structural health monitoring; optical fiber sensor; strain transfer analysis; smart composite structures; damage identification; performance assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Pengfei Cao

E-Mail Website
Guest Editor
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Interests: optical fiber sensing technology; vibration sensor; wireless energy transmission; artificial intelligent algorithm
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ping Xiang

E-Mail Website
Guest Editor
School of Civil Engineering, Central South University, Changsha 410083, China
Interests: computing in civil engineering; solid mechanics; structural mechanics; bridge engineering; structural materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled “AI-Driven Health Monitoring and Management of Building and Energy Structures” will cover advances in the structural health monitoring and management of buildings, bridges, and aerospace structures, integrating monitoring information and artificial intelligence (AI) techniques, contributing to the development of smart monitoring and management systems. How to establish the efficient structural monitoring system and how to make full use of the monitoring information have been particularly significant in risk prevention and health state assessment for engineering structures. Reliable sensors, data interpretation and AI algorithms, structural parameter recognition, and physical state assessment and prediction are critical for achieving this goal. Such methods enable straightforward descriptions of the structural operation state, as well as damage warning and potential disaster prediction, making structural management systems smart and efficient. Therefore, this Special Issue will discuss major advances in the development of smart sensors (i.e., optical fiber sensors and acoustic sensors), efficient data processing and interpretation methods, AI-driven structural feature recognition, structural state assessment and life-cycle prediction, potential damage and risk prevention, and smart management. This Special Issue will cover original or review articles exploring innovations in structural health monitoring and management systems. Themes of interests include, but not limited to, the following:

  • Smart monitoring system for buildings, bridges and aerospace structures;
  • Advanced sensors, i.e., optical fiber sensors and acoustic sensors;
  • Structural parameter recognition and physical state assessment;
  • Data fusion and model updating methods;
  • AI driven structural feature identification;
  • Vibration based structural state characterization;
  • Monitoring data motivated model updating;
  • Structural risk prevention and management;
  • Sudden disaster warning techniques;
  • Smart management of structural life-cycle.

Dr. Huaping Wang
Dr. Pengfei Cao
Prof. Dr. Ping Xiang
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-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
  • smart structure and system
  • optical fiber sensor
  • AI techniques
  • smart management of structures
  • data fusion and model updating

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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

31 pages, 14120 KB  
Article
Model Updating of a Tower Type Masonry Structure Using Multi-Criteria Decision-Making Methods and Evaluation of Its Earthquake Performance on 6 February 2023
by Hakan Erkek
Buildings 2026, 16(7), 1452; https://doi.org/10.3390/buildings16071452 - 7 Apr 2026
Viewed by 347
Abstract
This study aims to determine the current seismic resistance of two masonry minarets that were severely damaged during the 6 February 2023 Kahramanmaraş earthquakes, while also evaluating whether a model-updating approach based on experimental dynamic characteristics can reliably capture the actual seismic behavior [...] Read more.
This study aims to determine the current seismic resistance of two masonry minarets that were severely damaged during the 6 February 2023 Kahramanmaraş earthquakes, while also evaluating whether a model-updating approach based on experimental dynamic characteristics can reliably capture the actual seismic behavior and collapse mechanism of such structures under real earthquake conditions. The dynamic characteristics of the minarets were identified using Operational Modal Analysis (OMA) based on previous in-situ vibration measurements. These characteristics were used to calibrate finite element models through a model-updating process employing Multi-Criteria Decision-Making (MCDM) methods. The initial modal analyses revealed discrepancies of up to 13.7% in natural frequencies and 9.7% in mode shapes. After applying MCDM methods to a wide set of model variants, these differences were reduced to 2.0% and 9.2%, respectively, improving the agreement between numerical and experimental results. Once the most representative models were obtained, nonlinear seismic analyses were performed using actual ground motion records from the earthquake. The results included evaluations of peak displacements, base shear forces, and principal stresses. The concentration of principal stresses near the transition zone showed good qualitative agreement with the observed collapse locations, indicating a reasonable consistency between numerical results and observed damage patterns. These findings demonstrate the value of integrating OMA-based model updating with MCDM methods and support a data-driven framework for assessing the seismic performance of historical masonry structures. Full article
(This article belongs to the Special Issue AI-Driven Health Monitoring and Management of Building and Energy Structures)
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19 pages, 2580 KB  
Article
Image-Based Crack Detection Algorithm for Reinforced Concrete Water Tank Based on Improved YOLOv5s
by Yanmei Ma, Junwu Xia, Yu Zhou, Xiaoxi Bi and Huazhang Wei
Buildings 2026, 16(4), 735; https://doi.org/10.3390/buildings16040735 - 11 Feb 2026
Viewed by 381
Abstract
Image-based detection of concrete water tank damage in mining areas holds promise for practical applications. However, current deep learning-based detection algorithms often face challenges in balancing accuracy with computational complexity for real-world deployment. This paper presents an improved YOLOv5 detection method for concrete [...] Read more.
Image-based detection of concrete water tank damage in mining areas holds promise for practical applications. However, current deep learning-based detection algorithms often face challenges in balancing accuracy with computational complexity for real-world deployment. This paper presents an improved YOLOv5 detection method for concrete water tank damage. Firstly, the conventional convolution module in the CSPDarknet backbone is optimized with GSConv (Grouped Shuffle Convolution) to enhance feature extraction while reducing the number of parameters. Secondly, a weight transformation attention mechanism is integrated into the C3 structure to strengthen the feature representation of crack regions. Finally, the Minimum Point Distance IoU (MPDIoU) is employed for precise localization of irregular damage. On a dataset of over 11,000 images, the proposed method achieves a mean average precision (mAP@0.5) of 84.3% (precision: 88.7%; recall: 85.9%). It outperforms the original YOLOv5s, with a 6.5% higher mAP and an 11.1% faster inference speed, while maintaining a compact model size of 7.5M parameters and running at 86 FPS. Ablation studies confirm the individual contributions of each proposed module to these improvements. The algorithm thus provides an efficient and accurate solution that is suitable for deployment on resource-constrained devices. Full article
(This article belongs to the Special Issue AI-Driven Health Monitoring and Management of Building and Energy Structures)
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