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Advanced Sensors for Monitoring and Detection in Geotechnical Engineering

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 5155

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Special Issue Editors

Prof. Dr. Kuihua Wang

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Guest Editor

College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: sensors; machine learning; damage detection; defect analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Juntao Wu

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Guest Editor

College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: ultrasonic tomography; sensors; machine learning; damage detection; defect analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In geotechnical engineering, monitoring and detection technology is the essence of engineering design, long-term safety, and post-disaster reinforcement. In the past decade, with the rapid development of deep-earth and deep-sea resources, the construction and service environment of underground structures have become even more severe, which accordingly poses more challenges for geotechnical testing. Fortunately, benefiting from the emergence of high-performance materials, the invention of new instruments in the field of artificial intelligence algorithms, as well as monitoring and detection technology applied to geotechnical engineering, has led to significant progress in this regard.

In this Special Issue, we sincerely invite you to submit articles exploring cutting-edge research and recent advances in the field of monitoring and detection techniques applied to geotechnical engineering. Theoretical and experimental studies are welcome, as well as comprehensive reviews and survey papers.

Prof. Dr. Kuihua Wang
Dr. Juntao Wu
Guest Editors

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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.

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Keywords

geotechnical engineering
monitoring and detection
advanced sensors
non-destructive testing
on-site testing
damage identification

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Published Papers (9 papers)

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Research

18 pages, 33366 KiB

Open AccessArticle

Identification and Stability Analysis of Mine Goafs in Mineral Engineering Based on Multi-Survey Data

by Huihui Jia, Mengxi Zhang, Qiaoling Min, Shuai Han, Jingyi Zhang and Mingchao Li

Sensors 2025, 25(9), 2776; https://doi.org/10.3390/s25092776 - 28 Apr 2025

Viewed by 103

Abstract

Unregulated underground group mining in China has led to problems such as unclear locations and complex shapes of mine goafs in mineral engineering, posing serious safety hazards for subsequent mining operations. This paper takes mineral engineering with complex mine goafs as the research object, integrates multi-survey data from surface deformation remote sensing monitoring and 3D laser scanning measurement to survey the area where the surface deformation rate reaches 14cm/ year, accurately identifies the location of risky mine goafs, and constructs detailed representations of the real shapes of the complex mine goafs inside the mineral engineering. The FLAC^3D 6.0 software is used to establish a 3D numerical simulation model of the mine goafs, fully considering the mining process, and conducting characteristic analysis of the stress distribution, failure range and surface deformation response of the mine goafs, revealing the impact of void deformation on the stability of the mine. The numerical simulation results are combined with on-site investigations to verify whether geological disasters have been caused by mine goafs. The research methods and results can provide effective technical means for the detailed survey and stability assessment of mineral engineering with complex mine goafs, which can help to reduce the risk of geological disasters in mines and improve the safety of mineral engineering. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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15 pages, 5681 KiB

Open AccessArticle

Comprehensive Monitoring Method for Diaphragm Wall Deformation Combining Distributed and Point Monitoring in Key Areas

by Chun Lan, Hui Zhang, Guangqing Hu, Feng Han and Heming Han

Sensors 2025, 25(7), 2232; https://doi.org/10.3390/s25072232 - 2 Apr 2025

Viewed by 266

Abstract

The diaphragm wall plays an important role in the safe construction of foundation pits, and it is crucial to accurately monitor its deformation in real time. Traditional monitoring methods often face challenges in achieving distributed monitoring, and the cost of using fiber optic sensors for real-time and distributed monitoring can be prohibitively high. To improve the monitoring efficiency and accuracy of the deep deformation of the diaphragm wall, this paper proposes a hybrid monitoring method that combines ultra-weak fiber Bragg grating (UWFBG) technology and traditional FBG sensors. This distributed–discrete optical fiber monitoring approach allows for continuous, high-resolution data collection along the diaphragm wall while providing targeted, real-time measurements at critical locations. Fiber optic crack testing of concrete beam structures was carried out to verify the method of evaluating the health status of structures using distributed fiber optic data. An engineering case study was developed to validate the feasibility of this method. The results demonstrated that the hybrid approach effectively captures the overall deformation distribution of the diaphragm wall while enabling real-time monitoring of key areas, including the detection of crack initiation and propagation. The proposed method offers a significant advancement in deformation monitoring, providing enhanced accuracy, spatial coverage, and the ability to detect both macro-scale trends and micro-scale anomalies, which is particularly beneficial for complex underground structures. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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22 pages, 7080 KiB

Open AccessArticle

A Thermo–TDR Sensor for Simultaneous Measurement of Unfrozen Water Content and Thermal Conductivity of Frozen Soil

by Panting Liu, Simao Fan, Qingyi Mu, Qifan Zhang, Linlin Tang, Jine Liu, Fuqing Cui, Zhiyun Liu and Xuna Wang

Sensors 2025, 25(7), 2155; https://doi.org/10.3390/s25072155 - 28 Mar 2025

Viewed by 164

Abstract

Due to increasing human engineering activities in cold regions, the precise measurement of frozen soil’s physical property parameters has become particularly important. Traditional measurements of thermal conductivity and unfrozen water content of frozen soil are usually tested separately, leading to errors in accurately understanding the dynamic variation law of permafrost’s hydrothermal parameters in the near-phase transition zone. To address this, a multi-sensor fusion technology–thermo time domain reflectometry (thermo-TDR) sensor was designed and optimized for measuring the unfrozen water content and thermal conductivity of frozen soil. Three-dimensional thermal and electromagnetic numerical models were developed to analyze and validate the design parameters of the proposed sensor. Furthermore, a corresponding validation experiment was carried out to confirm the usability and accuracy of the designed sensor. The results show that (1) under the optimized probe parameters, the deviation between the theoretical thermal conductivity and the numerical preset value is 2.94%, verifying the accuracy of the sensor in thermal physical testing. (2) With a 10 mm probe spacing design, the test area of the thermo-TDR significantly increased, and the skin effect coefficient reached 25.54%, satisfying the electromagnetic design requirements of the TDR method. (3) The designed thermo-TDR sensor realizes the simultaneous measurement of unfrozen water and thermal conductivity of frozen soil, and the experimental results present a good consistency with that of the nuclear magnetic resonance (NMR) and transient planar heat source methods. (4) Additionally, due to the drastic changes in the soil’s physical properties due to the probe’s heating process, testing errors of the thermo-TDR sensor will significantly increase in the near-phase transition range, especially in the range of −2~−1 °C. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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17 pages, 14549 KiB

Open AccessArticle

Measurement of TBM Disc Cutter Wear Using Eddy-Current Sensor in Different TBM Chamber Conditions: Insights from Laboratory Tests

by Minsung Park, Minseok Ju, Jungjoo Kim and Hoyoung Jeong

Sensors 2025, 25(7), 2045; https://doi.org/10.3390/s25072045 - 25 Mar 2025

Viewed by 201

Abstract

The TBM disc cutter, which is the main cutting tool of tunnel boring machines (TBMs), is replaced when it is excessively worn during the boring process. Disc cutters are usually monitored by workers at cutterhead chambers, and they check the status and wear of disc cutters. Manual measurement occasionally results in inaccurate measurement results. In order to overcome these limitations, real-time disc cutter monitoring techniques have been developed with different types of sensors. This study evaluates the distance measurement performance of an eddy-current sensor for measuring disc cutter wear via a series of laboratory experiments. This study focused on identifying the effects of various measurement environments on the sensor’s accuracy. The study considered conditions that the eddy-current sensor may encounter in shield TBM chambers, including air, water, slurry, and excavated muck. Experiments were conducted using both a small-scale disc cutter and a 17-inch full-scale disc cutter. The results indicate that the eddy-current sensor can accurately measure the distance to the disc cutter within a specific range and that its performance remains unaffected by different measurement environments. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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23 pages, 36239 KiB

Open AccessArticle

Modeling Debris Flow Events in the Rio Inferno Watershed (Italy) Through UAV-Based Geomorphological Survey and Rainfall Data Analysis

by Laura Turbessi, Battista Taboni, Gessica Umili, Giandomenico Fubelli and Anna Maria Ferrero

Sensors 2025, 25(7), 1980; https://doi.org/10.3390/s25071980 - 22 Mar 2025

Viewed by 250

Abstract

This paper presents an analysis of the debris flow phenomena in the Rio Inferno watershed (Municipality of Cesana Torinese, Western Alps, Italy). The annual frequency and magnitude of these events have caused significant damage to the viability of the historic Chaberton Military Road, which is now closed to transit. This study delved into the processes governing debris flows in the Rio Inferno watershed through detailed geomorphological analysis, an unmanned aerial vehicle (UAV) photogrammetric survey, and the elaboration of rainfall data from the nearby weather monitoring stations. The Hydrologic Engineering Center’s River Analysis System (HEC-RAS) code was used to simulate debris flow events considering critical precipitations associated with return periods of 20, 50, 100, and 200 years, based on the highly detailed topographical model obtained by means of photogrammetry. The paper highlights the importance of studying debris flow phenomena to implement effective risk mitigation and management strategies, especially in the context of climate change and the increased vulnerability of mountain territories. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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20 pages, 6389 KiB

Open AccessArticle

Evaluation of Data Acquisition Areas in Geotechnical Seismic Tests: Insights from Field Applications

by Gunwoong Kim

Sensors 2025, 25(6), 1757; https://doi.org/10.3390/s25061757 - 12 Mar 2025

Viewed by 388

Abstract

Geotechnical field testing evaluates soil, rock, and groundwater conditions in their natural states, offering critical information about subsurface properties such as the density, strength, permeability, and groundwater flow. These tests are essential in ensuring the safety, reliability, and performance of civil engineering projects and are increasingly used for 3D geographical visualization and subsurface modeling. While point-based tests like the cone penetration test (CPT) and standard penetration test (SPT) are widely used, area-based methods such as the spectral analysis of surface waves (SASW) and electrical resistivity testing significantly enhance the accuracy of such models by providing broader coverage. Furthermore, these non-destructive techniques are particularly effective in identifying subsurface defects. This study focuses on analyzing the data acquisition areas of various field seismic tests, including SASW, downhole, crosshole, and suspension logging (PS logging). While other tests clearly define data acquisition areas based on their array paths, the SASW test posed challenges due to the complexity of data reconstruction. To address this, 69 datasets from four different sites were analyzed to predict the data acquisition areas for SASW as a function of depth. Moreover, a case study demonstrates the practical application of the SASW method in detecting cavities near a dam spillway. The findings of this research improve the understanding and interpretation of geotechnical seismic test data, enabling more precise geotechnical investigations and advancing the detection of subsurface defects using non-destructive methods. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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28 pages, 7816 KiB

Open AccessArticle

Machine Learning-Based Measurement and Prediction of Ground Settlement Induced by Shield Tunneling Undercrossing Existing Tunnels in Composite Strata

by Mei Dong, Mingzhe Guan, Kuihua Wang, Yeyao Wu and Yuhan Fu

Sensors 2025, 25(5), 1600; https://doi.org/10.3390/s25051600 - 5 Mar 2025

Viewed by 539

Abstract

To address the issue of insufficient accuracy in traditional settlement prediction methods for shield tunneling undercrossing in composite strata in Hangzhou, this paper proposes a particle swarm optimization (PSO)-based Bidirectional Long Short-Term Memory neural network (Bi-LSTM) prediction model for high-precision dynamic prediction of ground settlement under small-sample conditions. Shield tunneling is a key method for urban tunnel construction. This paper presents the measurement and prediction of ground settlement caused by shield tunneling undercrossing existing tunnels in composite strata in Hangzhou. The longitudinal ground settlement curve resulting from shield tunnel excavation was analyzed using measured data, and the measured lateral ground settlement was compared with the Peck empirical formula. Using PSO, the performance of three machine learning models in predicting the maximum ground settlement at monitoring points was compared: Long Short-Term Memory neural network (LSTM), Gated Recurrent Unit neural network (GRU), and Bi-LSTM. The linear relationships between different input parameters and between input parameters and the output parameter were analyzed using the Pearson correlation coefficient. Based on this analysis, the model was optimized, and its prediction performance before and after optimization was compared. The results show that the Bi-LSTM model optimized with the PSO algorithm demonstrates superior performance, achieving both accuracy and stability. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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19 pages, 39317 KiB

Open AccessArticle

A Low-Cost Sensor Network for Monitoring Peatland

by Hazel Louise Mitchell, Simon J. Cox and Hugh G. Lewis

Sensors 2024, 24(18), 6019; https://doi.org/10.3390/s24186019 - 18 Sep 2024

Viewed by 1875

Abstract

Peatlands across the world are vital carbon stores. However, human activities have caused the degradation of many sites, increasing their greenhouse gas emissions and vulnerability to wildfires. Comprehensive monitoring of peatlands is essential for their protection, tracking degradation and restoration, but current techniques are limited by cost, poor reliability and low spatial or temporal resolution. This paper covers the research, development, deployment and performance of a resilient and modular multi-purpose wireless sensor network as an alternative means of monitoring peatlands. The sensor network consists of four sensor nodes and a gateway and measures temperature, humidity, soil moisture, carbon dioxide and methane. The sensor nodes transmit measured data over LoRaWAN to The Things Network every 30 min. To increase the maximum possible deployment duration, a novel datastring encoder was implemented which reduced the transmitted datastring length by 23%. This system was deployed in a New Forest (Hampshire, UK) peatland site for two months and collected more than 7500 measurements. This deployment demonstrated that low-cost sensor networks have the potential to improve the temporal and spatial resolution of peatland emission monitoring beyond what is achievable with traditional monitoring techniques. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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17 pages, 15719 KiB

Open AccessArticle

An Optimization Method for Lightweight Rock Classification Models: Transferred Rich Fine-Grained Knowledge

by Mingshuo Ma, Zhiming Gui, Zhenji Gao and Bin Wang

Sensors 2024, 24(13), 4127; https://doi.org/10.3390/s24134127 - 25 Jun 2024

Viewed by 1147

Abstract

Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image classification restrict the model’s capacity to discern critical features contrastively from image pairs, and are typically too large for deployment on mobile devices used for in situ rock identification. In this work, we introduce an innovative and compact model generation framework anchored by the design of a Feature Positioning Comparison Network (FPCN). The FPCN facilitates interaction between feature vectors from localized regions within image pairs, capturing both shared and distinctive features. Further, it accommodates the variable scales of objects depicted in images, which correspond to differing quantities of inherent object information, directing the network’s attention to additional contextual details based on object size variability. Leveraging knowledge distillation, the architecture is streamlined, with a focus on nuanced information at activation boundaries to master the precise fine-grained decision boundaries, thereby enhancing the small model’s accuracy. Empirical evidence demonstrates that our proposed method based on FPCN improves the classification accuracy mobile lightweight models by nearly 2% while maintaining the same time and space consumption. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

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