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Buildings
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Advanced Technologies for Structural Health Monitoring in Engineering Structure
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Advanced Technologies for Structural Health Monitoring in Engineering Structure

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2201

Special Issue Editors

Prof. Dr. Yiqiang Xiang

E-Mail Website
Guest Editor
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: bridge engineering; submerged floating tunnel; structural optimization design; numerical simulation; structural health monitoring; life cycle design; durability of structure; reinforcement, repair and maintenance of structures; assessment and methods of structural condition; fluid-structure interaction; theoretical analysis of mechanics
Prof. Dr. Yong Xia

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 100872, China
Interests: structural health monitoring; structural damage identification; finite element model updating; nonlinear vibration of cables
Dr. Xinqun Zhu

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: structural health monitoring; vehicle-bridge interaction; physics-informed neural network
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinfeng Wang

E-Mail Website
Guest Editor
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: structural health monitoring and intelligent maintenance of bridge structures; structural analysis and multi-scale modeling of bridge structures; construction method and control technology of bridges; evaluation of performance on materials and structures of bridges; testing and experimental study of bridges
Dr. Jixing Cao

E-Mail Website
Guest Editor
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: structural health monitoring and digital-twin technologies for civil and infrastructure systems; advanced sensing; data-driven modeling; computational methods condition assessment; anomaly detection; life cycle performance of complex structural systems

Special Issue Information

Dear Colleagues,

In recent years, due to advancements in artificial intelligence, various sensing monitoring technologies, and communication technologies, traditional structural health monitoring (SHM)  has gradually evolved toward intelligent structural health monitoring (ISHM), integration of sky–space–ground monitoring technologies, and deep integration of big data and artificial intelligence, among other developments. As a result, the real-time perception of monitoring data, multi-scale and three-dimensional assessment and analysis, decision-making, and the speed of the entire processing process have all become more efficient than traditional structural health monitoring. This has effectively promoted the application of monitoring technology in disaster field monitoring and early warning, significantly improving the safety operation and resilience enhancement of structures. This Special Issue will focus on the main issues of structural engineering health monitoring technology, discuss theories and methods for intelligent structural health monitoring driven by artificial intelligence technology, including robot, unmanned aerial vehicle visualization and other automatic monitoring technologies, big data analysis and processing, and deep integration with artificial intelligence, providing a basis and methods for the early warning of various disasters, contributing to on-demand maintenance and rapid recovery of structural engineering, and generating new technologies and application scenarios for intelligent structural health monitoring. This Special Issue encourages researchers, practitioners and managers to move from the traditional “periodic monitoring and detection, with passive response” model to the intelligent paradigm of “real-time perception, integration of sky–space–ground monitoring technologies, intelligent early alarms, and active maintenance and prevention”, creating more durable and safe, as well as more sustainable, buildings and infrastructure.

Prof. Dr. Yiqiang Xiang
Prof. Dr. Yong Xia
Dr. Xinqun Zhu
Prof. Dr. Jinfeng Wang
Dr. Jixing Cao
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 (SHM)
  • bridges, buildings, dams, tunnels, space structures, heritage structures and other structures
  • real-time perception of monitoring data and integration of sky–ground–space monitoring technologies
  • methods for assessment of structural conditions
  • optimization design and layout of SHM systems
  • deep integration of big data and artificial intelligence
  • SHM-aided life-cycle performance assessment
  • digital twin technology and applications
  • monitoring and early alarming of various disasters
  • advanced sensing approaches
  • lightweight structure monitoring systems for in-service bridge groups
  • intelligent safety operation and maintenance and resilience enhancement
  • innovation and field applications of SHM
  • use of robotics, drones visualisation and other automatic technologies in monitoring
  • key issues in SHM

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Published Papers (4 papers)

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Research

26 pages, 4251 KB  
Article
Reliability-Aware Robust Optimization for Multi-Type Sensor Placement Under Sensor Failures
by Shenghuan Zeng, Ding Luo, Pujingru Yan, Naiwei Lu, Ke Huang and Lei Wang
Buildings 2026, 16(5), 1024; https://doi.org/10.3390/buildings16051024 - 5 Mar 2026
Viewed by 344
Abstract
In the field of structural health monitoring systems, sensors serve as the fundamental components for assessing infrastructure integrity. The rationality of their spatial configuration significantly influences the precision of structural performance assessment, the efficacy of damage detection algorithms, and the operational reliability of [...] Read more.
In the field of structural health monitoring systems, sensors serve as the fundamental components for assessing infrastructure integrity. The rationality of their spatial configuration significantly influences the precision of structural performance assessment, the efficacy of damage detection algorithms, and the operational reliability of the system throughout its designated lifecycle. A robust optimization methodology for the placement of multi-type sensors is proposed in this study, explicitly formulated to mitigate the negative impact of sensor malfunctions during long-term operation. First, a rigorous evaluation framework for sensor placement schemes is established based on Bayesian inference and the minimization of information entropy, thereby quantifying the uncertainty inherent in parameter identification. Then, a probabilistic model of sensor failure is developed utilizing the Weibull distribution to capture time-dependent reliability characteristics, combined with a modified information entropy calculation method that mathematically assimilates these failure probabilities into the optimization objective. Finally, a heuristic search strategy is employed to achieve the robust optimal placement of multi-type sensors, efficiently navigating the complex combinatorial search space. In contrast to deterministic information entropy (DIE) methodologies, which assume ideal sensor functionality, the robust information entropy (RIE) approach comprehensively accounts for the stochastic nature of sensor failures and their impact on the information content of the monitoring network, thereby significantly augmenting the robustness and redundancy of the sensor configuration. Validations utilizing a numerical frame structure and a finite element bridge model demonstrate that the RIE method effectively integrates the sensor failure probability model to yield robust optimal placement schemes, minimizing the risk of information loss and ensuring reliable structural health monitoring throughout the engineering lifecycle. Full article
(This article belongs to the Special Issue Advanced Technologies for Structural Health Monitoring in Engineering Structure)
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22 pages, 5263 KB  
Article
Field Application of FBG-Instrumented CFRP Pressure-Dispersed Anchor Cables in Slope Reinforcement: A Case Study on Dangerous Rock Stabilization at Guangyang Island
by Qiang Wang, Junjie Li, Kui Huang, Jinyu Hu, Zijian Wang, Gang He, Wenping Lan and Shuangqing Tang
Buildings 2026, 16(5), 1016; https://doi.org/10.3390/buildings16051016 - 5 Mar 2026
Viewed by 323
Abstract
To address force uniformity and corrosion issues in slope reinforcement, this paper presents a field implementation of pressure-uniformly-dispersed Carbon-Fiber-Reinforced Polymer (CFRP) anchor cables integrated with Fiber Bragg Grating (FBG) sensing technology. A tensioning trial was conducted on a dangerous rock project on Guangyang [...] Read more.
To address force uniformity and corrosion issues in slope reinforcement, this paper presents a field implementation of pressure-uniformly-dispersed Carbon-Fiber-Reinforced Polymer (CFRP) anchor cables integrated with Fiber Bragg Grating (FBG) sensing technology. A tensioning trial was conducted on a dangerous rock project on Guangyang Island, Chongqing, utilizing a distributed FBG array (0.5 m spacing) for full-length strain monitoring. The results confirm that, under the specific conditions of this project, the anchorage segment exhibits the characteristic two-stage behavior of “delayed activation–uniform bearing” previously documented in bonded anchor systems, with a critical transition observed at approximately 120 kN. Beyond this threshold, the anchorage efficiency reached approximately 85%, validating the three-stage uniformly dispersed design for this specific geological context. While the load transfer mechanism aligns with established bonded anchor mechanics, this study demonstrates the practical feasibility of high-resolution distributed sensing in CFRP anchor systems, providing benchmark data for construction quality control and long-term health monitoring of similar slope reinforcement projects. Full article
(This article belongs to the Special Issue Advanced Technologies for Structural Health Monitoring in Engineering Structure)
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22 pages, 5222 KB  
Article
A Two-Stage Concrete Crack Segmentation Method Based on the Improved YOLOv11 and Segment Anything Model
by Ru Zhang, Chaodong Guan, Yi Fang, Yuanfeng Duan and Xiaodong Sui
Buildings 2026, 16(4), 794; https://doi.org/10.3390/buildings16040794 - 14 Feb 2026
Cited by 1 | Viewed by 653
Abstract
During long-term service, concrete structures are exposed to various adverse factors, which often lead to the formation of numerous surface cracks. These cracks pose serious threats to structural safety and durability. Therefore, accurately identifying crack characteristics is essential for evaluating the service performance [...] Read more.
During long-term service, concrete structures are exposed to various adverse factors, which often lead to the formation of numerous surface cracks. These cracks pose serious threats to structural safety and durability. Therefore, accurately identifying crack characteristics is essential for evaluating the service performance of concrete structures. A two-stage concrete crack segmentation method is presented in this study. The crack is initially located by the improved YOLOv11 that integrates three novel modules, namely Multi-scale Edge Information Enhancement, Efficient-Detection, and P2-Level Feature Integration, to form the MEP-YOLOv11 model. Then, the detected region is taken as input prompts for Segment Anything Model (SAM) to achieve precise crack segmentation. This approach eliminates the need for manual prompting in SAM, enabling automatic crack feature identification. The average Accuracy, precision, and Intersection over Union (IoU) for crack segmentation are 95.98%, 92.60%, and 0.77, respectively. To further enhance the robustness of the two-stage segmentation method under non-uniform illumination conditions, a mask re-input strategy is introduced. The crack mask generated by SAM using bounding-box prompts is fed back into SAM to guide a second round of segmentation. Experimental results demonstrate that the improved method maintains high segmentation performance, with an average Accuracy of 92.38%, precision of 85.70%, and IoU of 0.64. Overall, the proposed method meets engineering requirements for high-precision and efficient crack detection and segmentation, showing strong potential for practical inspection tasks. Full article
(This article belongs to the Special Issue Advanced Technologies for Structural Health Monitoring in Engineering Structure)
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25 pages, 6025 KB  
Article
Advanced Computer Vision Technology for Real-Time Rotation Angle Monitoring of Bridge Bearings in Structural Health Assessment
by Liangbo Wang, Ming Li, Rongxin Zhao, Zhaoyuan Xu, Maotai Sun, Delang Peng, Xuewen Yu and Yabin Liang
Buildings 2026, 16(4), 734; https://doi.org/10.3390/buildings16040734 - 11 Feb 2026
Viewed by 401
Abstract
Real-time perception of bearing rotation angles is essential for structural health assessment of bridges. However, existing vision-based rotation angle measurement methods exhibit limited robustness to time-varying operational conditions and tracking errors, particularly in practical applications of bridge monitoring. To address this limitation, this [...] Read more.
Real-time perception of bearing rotation angles is essential for structural health assessment of bridges. However, existing vision-based rotation angle measurement methods exhibit limited robustness to time-varying operational conditions and tracking errors, particularly in practical applications of bridge monitoring. To address this limitation, this study presents an advanced computer vision-based monitoring technology for bridge bearing rotation angles by incorporating specifically configured retroreflective targets, an efficient target tracking approach, and a rotation angle calculation algorithm. Firstly, under LED illumination, retroreflective targets appear as bright, high-contrast features in the images, facilitating precise detection and tracking. Secondly, target centroids are tracked with sub-pixel accuracy through thresholding, edge extraction, and ellipse fitting. Lastly, the bearing rotation angle is calculated by analyzing the angle between the two characteristic lines formed by the target centroids. To validate the effectiveness of the proposed method, comprehensive numerical investigations were conducted, and the results showed that the proposed method maintained high accuracy across various imaging conditions. Additionally, comparative analysis with an existing advanced method also revealed that the proposed method exhibits superior measurement performance even under target tracking uncertainties. To investigate its feasibility and validate its practical effectiveness, a field application on an 80 m + 80 m continuous beam was conducted, and minute rotation angle measurements during 23 railway train drive-by events were obtained using the proposed method, yielding a root mean square error of 0.0008° and mean absolute error of 0.0007°. The successful development and field deployment demonstrate significant potential for advancing structural health monitoring technologies, contributing to intelligent infrastructure management through automated monitoring and early warning capabilities. Full article
(This article belongs to the Special Issue Advanced Technologies for Structural Health Monitoring in Engineering Structure)
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