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Structural Health Monitoring and Smart Disaster Prevention
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Structural Health Monitoring and Smart Disaster Prevention

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3961

Special Issue Editors

Dr. Hongjuan Chen

E-Mail Website
Guest Editor
Institute of Geophysics, China Earthquake Administration, Beijing, China
Interests: earthquake engineering; structural dynamic analysis; smart disaster prevention; artificial intelligence
Dr. Xiaojiang Liu

E-Mail Website
Guest Editor
School of Resources and Civil Engineering, Northeastern University, Shenyang, China
Interests: disaster prevention; fire disaster; utility tunnel; machine learning
Prof. Dr. Zhao-Dong Xu

E-Mail Website
Guest Editor
School of Civil Engineering, Southeast University, Nanjing, China
Interests: structural vibration control and safety monitoring; structural aseismic; multi-disaster and smart disaster prevention; smart material
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the acceleration of the urbanization process, the maintenance and management of large complex structures need to be more robust. Therefore, structural health monitoring has become an important research direction in the field of engineering. Structural health monitoring technology has been widely used in various structural types, such as high-rise buildings, long-span bridges, underground spaces, and so on. Structural health monitoring includes collecting real-time or regular data on structural response, such as displacement, strain, crack width, etc.; analyzing the data to predict potential defects and the degradation of the structure; assessing the state of structural performance; and developing maintenance or repair plans.

The rapid development of computer technology makes artificial intelligence technology become increasingly mature, and artificial intelligence plays an increasingly important role in disaster prevention and reduction. Through the rational use of artificial intelligence technology, the accuracy and timeliness of disaster warning and monitoring can be greatly improved, the decision-making effect of post-disaster reconstruction and assessment can be optimized, better social networks and information services can be provided, and the safety of people's lives and property can be better guaranteed.

Potential topics include, but are not limited to, the following:

  • Intelligent sensing technology;
  • Health inspection for civil engineering structure;
  • Data processing techniques;
  • Dynamic analysis and calculation of civil engineering;
  • Application of artificial intelligence in disaster prevention and reduction;
  • Computer vision-based infrastructure inspection, damage measurement, and assessment;
  • Application of multi-modal sensor fusion in disaster prevention and reduction engineering;
  • Engineering structure safety assessment and optimization;
  • Intelligent inspection and testing of engineering structures;
  • Application of digital twin, big data, Internet of Things, and other technologies in engineering disaster prevention and reduction.

Dr. Hongjuan Chen
Dr. Xiaojiang Liu
Prof. Dr. Zhao-Dong Xu
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. Sensors 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

  • sensing technology
  • structural health monitoring
  • smart
  • damage detection
  • safety assessment
  • disaster prevention
  • dynamic analysis
  • artificial intelligence
  • data processing
  • multiple disasters

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Published Papers (5 papers)

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Research

20 pages, 5151 KB  
Article
Experimental Analysis of Seismic Damage to the Frame Structure–Site System Crossing a Reverse Fault
by Jing Tian, Haonan Zhang, Shihang Qu, Jianyi Zhang, Hongjuan Chen, Zhijie Xu, Yijie Song and Ran Zhang
Sensors 2025, 25(22), 6866; https://doi.org/10.3390/s25226866 - 10 Nov 2025
Viewed by 454
Abstract
Buildings crossing active faults often suffer severe damage due to fault dislocation during direct-type urban earthquakes. This study employs physical model tests to systematically investigate the dynamic response mechanisms of the integrated “surface rupture zone–overburden–foundation–superstructure” system subjected to bedrock dislocation. A testing apparatus [...] Read more.
Buildings crossing active faults often suffer severe damage due to fault dislocation during direct-type urban earthquakes. This study employs physical model tests to systematically investigate the dynamic response mechanisms of the integrated “surface rupture zone–overburden–foundation–superstructure” system subjected to bedrock dislocation. A testing apparatus capable of simulating reverse faults with adjustable dip angles (45° and 70°) was developed. Using both sand and clay as representative overburden materials, the experiments simulated the processes of surface rupture evolution, foundation deformation, and structural response under varying fault dislocation magnitudes. Results indicate that the fault rupture pattern is governed by the bedrock dislocation magnitude, soil type, and fault dip angle. The failure process can be categorized into three distinct stages: initial rupture, rupture propagation, and rupture penetration. The severity and progression of structural damage are primarily determined by the building’s location relative to the fault trace. Structures located entirely on the hanging wall exhibited tilting angles that remained below the specified code limit throughout the dislocation process, demonstrating behavior dominated by rigid-body translation. In contrast, buildings crossing the fault exceeded this limit even at low dislocation levels, developing significant tilt and strain concentration due to differential foundation settlement. The most severe damage occurred in high-angle dip sand sites, where the maximum structural tilt reached 5.5°. This research elucidates the phased evolution of seismic damage in straddle-fault structures, providing experimental evidence and theoretical support for the seismic design of buildings in near-fault regions. The principal theoretical and methodological contributions are (1) developing a systematic “fault–soil–structure” testing methodology that reveals the propagation of fault dislocation through the system; (2) clarifying the distinct failure mechanisms between straddle-fault and hanging-wall structures, providing a quantitative basis for targeted seismic design; and (3) quantifying the controlling influence of fault dip angle and soil type combinations on structural damage severity, identifying high-angle dip sand sites as the most critical scenario. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Smart Disaster Prevention)
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22 pages, 11599 KB  
Article
Development and Modeling of a Novel Magnetorheological Elastomer Isolator in Hybrid Mode with a Compression–Shear Hybrid Fractional-Derivative Parametric Model
by Yun Tian, Zhongwei Hu, Yingqing Guo, Lihua Zhu, Jun Dai, Yuxuan Tao and Xin Wang
Sensors 2025, 25(20), 6376; https://doi.org/10.3390/s25206376 - 15 Oct 2025
Viewed by 973
Abstract
Magnetorheological elastomers (MREs) are composed of soft silicone rubber, carbonyl iron particles (CIPs), and various additives. This study designs and fabricates a novel hybrid-mode MRE isolator that can operate in both compression and shear modes simultaneously. Experimental and modeling investigations are conducted to [...] Read more.
Magnetorheological elastomers (MREs) are composed of soft silicone rubber, carbonyl iron particles (CIPs), and various additives. This study designs and fabricates a novel hybrid-mode MRE isolator that can operate in both compression and shear modes simultaneously. Experimental and modeling investigations are conducted to examine the dynamic mechanical properties of the hybrid-mode MRE isolator under varying excitation frequencies, displacement amplitudes, and magnetic field strengths. The equivalent stiffness, energy dissipation, and equivalent damping of the MRE isolator are determined. Experimental results reveal that the hybrid-mode MRE isolator exhibits a pronounced MR effect by utilizing a hybrid magnetic field generation system, with all three parameters significantly increasing as the magnetic field strength increases. However, as the excitation frequency increases, the equivalent stiffness and energy dissipation increase, while the equivalent damping decreases. Based on the experimental findings, a compression–shear hybrid fractional-derivative parametric (CSHF) model is proposed to describe the impact of different operating conditions on the dynamic viscoelastic properties of the MRE isolator. A comparative analysis of the experimental results and model predictions indicates that the proposed mechanical model can accurately describe the dynamic mechanical characteristics of the hybrid-mode MRE isolator. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Smart Disaster Prevention)
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22 pages, 6269 KB  
Article
A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction
by Jinhua Wu, Chengdu Cao, Liang Fei, Xiangyang Han, Yuli Wang and Ting On Chan
Sensors 2025, 25(19), 6041; https://doi.org/10.3390/s25196041 - 1 Oct 2025
Viewed by 468
Abstract
Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid predicting framework that integrates Past Decomposable Mixing with an inverted [...] Read more.
Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid predicting framework that integrates Past Decomposable Mixing with an inverted Transformer architecture (PDM-iTransformer). The PDM module decomposes the original sequence into multi-resolution trend and seasonal components, using structured bottom-up and top-down mixing strategies to enhance feature representation. The iTransformer then models each variable’s time series independently, applying cross-variable self-attention to capture latent dependencies and using feed-forward networks to extract local dynamic features. This design enables simultaneous modeling of long-term trends and short-term fluctuations. Experimental results on GNSS monitoring data demonstrate that the proposed method significantly outperforms traditional models, with R2 increased by 16.2–48.3% and RMSE and MAE reduced by up to 1.33 mm and 1.08 mm, respectively. These findings validate the framework’s effectiveness and robustness in predicting landslide displacement under complex terrain conditions. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Smart Disaster Prevention)
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27 pages, 4604 KB  
Article
Identification of Static Loads in Wharf Mooring Cables Using the Influence Coefficient Method
by Jia Zhou, Changshi Xiao, Langxiong Gan, Bo Jiao, Haojie Pan and Haiwen Yuan
Sensors 2025, 25(18), 5867; https://doi.org/10.3390/s25185867 - 19 Sep 2025
Viewed by 583
Abstract
Directly measuring the mooring cable load while a ship is moored at a wharf poses significant practical challenges. This paper proposes an indirect load measurement method to identify mooring cable static loads based on the Influence Coefficient Matrix (ICM) method. First, a finite [...] Read more.
Directly measuring the mooring cable load while a ship is moored at a wharf poses significant practical challenges. This paper proposes an indirect load measurement method to identify mooring cable static loads based on the Influence Coefficient Matrix (ICM) method. First, a finite element analysis of the bollard is conducted to obtain the full-field strains under each unit load. A solution procedure based on the genetic algorithm (GA) is then implemented to determine the optimal placement and orientation of strain gauges, aiming to improve load identification accuracy. An optimal load coefficient matrix is derived to establish the correlation between cable loads and bollard strains. Subsequently, following the established measured point placement scheme, strain gauges are installed on the bollard surface to capture the strains, enabling inverse identification of mooring cable loads through the measured strains and the pre-established load–strain relationship. A numerical case study validated the feasibility of this method, demonstrating high identification accuracy. Furthermore, experimental verification was conducted to assess its reliability under different conditions. Results confirmed the effectiveness of this indirect approach for mooring cable static loads measurement. The research findings provide a technical framework for real-time monitoring of mooring cable loads. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Smart Disaster Prevention)
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23 pages, 3810 KB  
Article
Theoretical and Numerical Analysis of Impact Forces on Blocking Piles Within Embankment Breaches Using Flow Velocity Signals
by Xing-Huai Huang, Yu Fang, Sheng-Yu Chang and Ying-Qing Guo
Sensors 2025, 25(11), 3333; https://doi.org/10.3390/s25113333 - 26 May 2025
Viewed by 587
Abstract
In the realm of structural health monitoring (SHM) and smart disaster prevention, accurately assessing the impact forces on emergency structures during natural disasters is crucial for a timely and effective response. Therefore, a theoretical method for the water flow impact force on embankment [...] Read more.
In the realm of structural health monitoring (SHM) and smart disaster prevention, accurately assessing the impact forces on emergency structures during natural disasters is crucial for a timely and effective response. Therefore, a theoretical method for the water flow impact force on embankment breach piles was established by combining the numerical model of breach hydraulics with the Morison equation. To assess the accuracy and validity of the proposed theoretical calculation method, a 3D finite element model considering the coupling effect of water flow and pile arrangement was established, and the effects of flow velocity, water depth, and other factors on the force of the plugging structure were studied. A comparative analysis was conducted and indicated that the Morison equation method based on the flow velocity signals can calculate the impact force of the structure within a certain error range when the value of drag force coefficient CD is set to 1.0 and the value of inertia force coefficient CM is set to 2.0, providing a reference for emergency plugging decisions for embankment breaches. The findings provide essential theoretical references for data-driven emergency plugging decisions, thereby enhancing the effectiveness of smart disaster prevention strategies for embankment breaches. Full article
(This article belongs to the Special Issue Structural Health Monitoring and Smart Disaster Prevention)
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