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Sensors in Deformation and Dynamic Response Monitoring for Geotechnical and Civil Engineering

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 977

Special Issue Editors

Prof. Dr. Nan Jiang

E-Mail Website
Guest Editor
State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
Interests: tunnel blasting; rock dynamics; underground engineering technology; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Zhu

E-Mail Website
Guest Editor
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Interests: geotechnical engineering; blasting engineering; underground space development; dynamic response

Special Issue Information

Dear Colleagues,

The safety and resilience of geotechnical and civil infrastructure—such as slopes, tunnels, bridges, and buildings—rely heavily on the accurate monitoring of deformation (e.g., settlement, displacement, strain) and dynamic response (e.g., vibration, acceleration). Advanced sensing technologies enable non-destructive, real-time observations of these parameters, offering early warnings of instability or damage.

Structural health monitoring (SHM) typically involves time-synchronized sensor data acquisition, the extraction of deformation- or damage-sensitive features, and intelligent analysis to assess structural conditions. Future systems must support autonomous, online, continuous operation with minimal human intervention and the ability to report diagnostics remotely. Their effectiveness hinges on sensor performance and robust data processing algorithms suited to complex geotechnical and civil environments.

This Special Issue highlights recent advances in sensors and data analytics for deformation and dynamic response monitoring in geotechnical and civil engineering. Topics include:

  • Novel sensors for deformation and dynamic response;
  • Sensor integration in challenging environments;
  • Anomaly detection using deep learning and time-series analysis;
  • Vision-based and drone-assisted contactless monitoring;
  • Digital twin frameworks with real-time deformation and dynamic data;
  • Multi-source data fusion for comprehensive assessment;
  • Field case studies and long-term validation of monitoring systems.

Prof. Dr. Nan Jiang
Dr. Bin Zhu
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

  • deformation monitoring
  • dynamic response monitoring
  • geotechnical engineering
  • civil infrastructure
  • non-destructive testing sensor
  • wireless sensor network
  • real-time monitoring

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Published Papers (2 papers)

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Research

21 pages, 1563 KB  
Article
Mechanical Property Evolution and Load Monitoring Method of Laminated Elastomeric Bridge Bearings Under Temperature Effects
by Menglong He, Xianhui Liu and Nianchun Deng
Sensors 2026, 26(10), 3046; https://doi.org/10.3390/s26103046 - 12 May 2026
Viewed by 274
Abstract
The mechanical behavior of laminated elastomeric bearings in service is highly sensitive to ambient temperature, whereas conventional monitoring approaches often fail to accurately capture their temperature-dependent load response. To address this issue, this study proposes a multi-temperature framework for identification and load monitoring [...] Read more.
The mechanical behavior of laminated elastomeric bearings in service is highly sensitive to ambient temperature, whereas conventional monitoring approaches often fail to accurately capture their temperature-dependent load response. To address this issue, this study proposes a multi-temperature framework for identification and load monitoring of bridge elastomeric bearings. Using a high-precision laser displacement measurement system, six temperature levels were defined from −20 to 30 °C at 10 °C intervals. Room-temperature load–displacement calibration tests, compressive elastic modulus tests under different temperature conditions, and monitoring accuracy validation tests were then systematically conducted. Based on these experiments, the effects of temperature on the mechanical properties and compressive deformation response of the bearing were quantified, and an inverse load-identification model was developed. The results show that the compressive elastic modulus increases markedly with decreasing temperature, reaching a 32.11% increase at −20 °C relative to that at 30 °C. Under the same applied load, the vertical compressive deformation decreases significantly as temperature decreases, with a 27.76% reduction at −20 °C compared with that at 30 °C, indicating a pronounced low-temperature stiffening effect. The proposed inverse load-identification model achieves a maximum relative error of 4.83% over the full temperature range, demonstrating good accuracy and applicability. The proposed methodology provides a practical basis for mechanical-performance evaluation and high-precision monitoring of bridge bearings under complex thermal environments. Full article
(This article belongs to the Special Issue Sensors in Deformation and Dynamic Response Monitoring for Geotechnical and Civil Engineering)
15 pages, 4000 KB  
Article
Feature Extraction and Unsupervised Classification of Roadway Fracture Signals: A Full-Section Wi-Fi Wireless Monitoring Approach
by Chenghao Zu, Wenlong Zhang, Yaqi Zhou, Cheng Peng, Shibin Teng and Fang Zhao
Sensors 2026, 26(10), 3018; https://doi.org/10.3390/s26103018 - 11 May 2026
Viewed by 325
Abstract
Aiming to address the challenge of the high-precision monitoring of underground coal and rock fractures, this paper proposes and verifies a roadway full-section synchronous monitoring method utilizing a Wi-Fi wireless sensor network. To address the inherent difficulties of detecting complex rock mass fractures [...] Read more.
Aiming to address the challenge of the high-precision monitoring of underground coal and rock fractures, this paper proposes and verifies a roadway full-section synchronous monitoring method utilizing a Wi-Fi wireless sensor network. To address the inherent difficulties of detecting complex rock mass fractures through surface sensors, our methodology employs a synchronized array of surface-mounted vibration sensors covering key mechanical structural points. The feasibility of this approach is technically substantiated through the strict implementation of rigid coupling techniques—utilizing industrial-grade epoxy resin and customized metal mechanical fixtures—combined with hardware low-pass filtering to eliminate air gap attenuation and maximize the signal-to-noise ratio. Using this validated setup, we successfully extracted and manually verified 63 high-fidelity rupture events. The data reliability is further demonstrated through a comprehensive Python-based processing pipeline that calculates 17-dimensional time–frequency characteristics. Statistical analysis confirms that the extracted data strictly conforms to the physical laws of rock fracture, evidenced by a significant negative correlation between maximum amplitude and dominant frequency (r = −0.84, p < 0.001). Unsupervised clustering of these signals reveals excellent inter-class separability. By transparently substantiating the data acquisition and verification process, this study provides a publicly shared pilot dataset and methodology for algorithm evaluation and preliminary dynamic disaster mechanism exploration. Full article
(This article belongs to the Special Issue Sensors in Deformation and Dynamic Response Monitoring for Geotechnical and Civil Engineering)
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