Temperature-insensitive shape sensor for realtime deformation monitoring of discontinuous spaceborne antenna truss

BDS PPP-RTK for deformation monitoring of Hong Kong-Zhuhai-Macao bridge

The article is devoted to the intellectualization of electronic geodetic instruments (EGI) and the development of conceptual foundations for integrating artificial intelligence (AI) technologies into the geoinformation environment (GIS) to enhance the efficiency of spatial management systems. The study presents a developed architectural model of an intelligent spatial management system, which includes the interaction of electronic instruments, sensor modules, GIS platforms, and analytical AI services. The proposed concept of EGI intellectualization is based on three main vectors: autonomy of the measurement process (through machine learning, ML, for object recognition and self-diagnostics), adaptability to environmental conditions (via environmental impact correction and noise reduction), and integrativity with GIS. The research describes the use of AI methods, including deep neural networks (YOLO, Mask R-CNN, U-Net, PointNet) for automatic detection and classification of objects in images and point clouds, as well as for real-time evaluation and correction of GNSS errors using neuro-Kalman filters. The practical directions of model implementation include automated monitoring of engineering structure deformations and intelligent UAV data processing for updating topographic maps. According to the findings, the phased integration of AI transforms EGI into intelligent sensors capable of autonomously assessing data quality and interacting with GIS, thereby providing a reliable foundation for smart cities and sustainable territorial development.

The Synthetic Aperture Radar Interferometry (InSAR) technique enables researchers to generate Digital Elevation Models (DEMs) from SAR data, which researchers widely apply in multi-temporal analyses, including ground deformation monitoring, susceptibility mapping, and analysis of spatial changes in erosion basins. In this study, we generated two interferometric DEMs from Sentinel-1 (S1, VV polarization) and TerraSAR-X (TSX, HH polarization, ascending orbit) data, processed in SNAP, over a mountainous sector of the central Andes in Chile. We assessed the accuracy of the DEMs against two reference datasets: the SRTM DEM and a high-resolution LiDAR-derived DEM. We selected 150 randomly distributed points across different slope classes to compute statistical metrics, including RMSE and MedAE. Relative to the LiDAR DEM, both sensors yielded rMSE values of approximately 20 m, increasing to 23–24 m when compared with the SRTM DEM. The MedAE, a metric less sensitive to outliers, was 3.97 m for S1 and 3.26 m for TSX with respect to LiDAR, and 7.07 m for S1 and 7.49 m for TSX relative to SRTM. We observed a clear positive correlation between elevation error and terrain slope. In areas with slopes greater than 45°, the MedAE exceeded 14 m relative to the LiDAR DEM and reached ~15 m relative to the SRTM for both S1 and TSX.

This study introduces an amplitude-based method that applies Spatially Variant Apodization (SVA) to reduce sidelobes in Synthetic Aperture Radar (SAR) data. Unlike conventional approaches, the filter is applied only to the amplitude while preserving the original interferometric phase, thereby enabling accurate Persistent Scatterer Interferometry (PSI) for dam deformation monitoring in Stanford Method for Persistent Scatterers (StaMPS) software. The SVA filter is integrated as an additional processing step within the Sentinel Application Platform (SNAP) for the SentiNel Application Platform to Stanford Method for Persistent Scatterers (SNAP2StaMPS) workflow. Filtering is performed in two dimensions (azimuth and range) separately on the In-phase (I) and Quadrature (Q) components of the coregistered data using a Python-based implementation via SNAP-Python (snappy). By recombining the SVA-filtered and original I and Q components, the method modifies only the amplitude while leaving the phase unchanged. The approach is evaluated in a proof-of-concept case study of the Sorpe Dam in Germany, where an Electronic Corner Reflector - C Band (ECR-C) produces sidelobe artifacts that degrade the Sentinel-1 (S-1) descending time series. The results demonstrated a successful integration of SVA filtering into the SNAP2StaMPS framework, achieving sidelobe reduction and improved Persistent Scatterer (PS) detection without compromising phase quality. The number of sidelobe-affected PS decreased by 39.26% after SVA filtering, while the amplitude-based approach preserved the original phase and deformation values, with a Root Mean Square Error (RMSE) of approximately 0.38 mm. Overall, this novel amplitude-based SVA approach extends the SNAP2StaMPS workflow by reducing strong sidelobes while preserving phase information for dam monitoring at the Sorpe dam site.

Abstract Ground deformation source modeling and muographic mass density monitoring were applied for studying the plumbing system of Sakurajima volcano, Kyusu Japan using data collected by Interferometric Synthetic Aperture Radar and Sakurajima Muography Observatory. Lateral movement of ground deformation source was observed to east beneath the active craters around sea level that resulted in the shift of eruption frequency between the Minamidake craters. During the same period, muography showed opposite trends in mass changes for adjacent craters: mass decreased beneath the Minamidake A and B craters and mass increased beneath the Showa crater that also suggests the lateral movements of materials towards east. Thereafter, the ground deformation source started to rise and the eruption sequence of Showa crater started when the deformation source reached a depth of about 350–450 m beneath the craters. The muographically measured mass increased beneath Showa crater before the start of the eruption sequence. During eruption episodes of Minamidake A and B craters, the mass did not change beneath these craters and decreased beneath Showa crater, suggesting a connection between the adjacent craters. These observations suggest the presence of a deep magma channel around sea level which feeds Minamidake A and B craters and the existence of a shallow magma chamber about 350–450 m beneath the active craters which feeds all craters. Joint measurement of ground surface deformations and cosmic-ray muons allows simultaneous monitoring of shallow volcanic processes that may allow more reliable assessment of impending eruption sequences of Showa crater of Sakurajima volcano. Graphical Abstract

Purpose. Development of a methodology for monitoring small deformations of austenitic medium-manganese steels using magnetometric methods. Determination of the paramagnetic parameter of austenitic steel, the value of which uniquely correlates with the degree of plastic deformation by compression. Research methods. Determination of the specific magnetic susceptibility of the sample, the resulting specific magnetic susceptibility of the paramagnetic austenite of the sample, and the paraprocess of α-phase of the sample were performed on an automated Faraday magnetometric balance. Uniaxial plastic compression deformation at room temperature was performed on a laboratory setup. Results. Based on the results of experimental studies, the amounts of α’-martensite arising during plastic deformation by compression of 110G8L steel were determined. The resulting (paramagnetic austenite of the sample and the paraprocess of α-phase of the sample) specific magnetic susceptibility of deformed samples of 110G8L steel was experimentally found. Scientific novelty. The idea of the relationship between the degree of deformation of austenitic steel and the value of the resulting specific magnetic susceptibility cµ (paramagnetic austenite of the sample and the paraprocess of α-phase of the sample) is proposed and experimentally confirmed. Practical value. During operation, parts made of austenitic medium-manganese steels are subjected to static or dynamic loads under abrasive wear conditions. This operating mode is accompanied by plastic deformation due to compression. The degree of deformation is an important parameter for assessing the reliability of the product. Determining the degree of deformation by measuring geometric dimensions is not always appropriate, as the configuration of cast parts can be quite complex. At the same time, magnetometric deformation monitoring, specifically the correlation found between the degree of plastic deformation due to compression and the resulting specific magnetic susceptibility, allows to determine the degree of deformation of a part of any configuration.

The current mainstream methods for online detection of transformers all have shortcomings such as low sensitivity and susceptibility to interference from the testing environment. Aiming at the shortcomings of the existing online detection methods for transformer winding deformation in terms of feature sensitivity and diagnostic accuracy, this paper proposes a fault intelligent diagnosis method based on high sensitivity multimodal feature fusion. First, the winding deformation experiment is designed for typical fault data, which is obtained to extract multiple frequency and time domain response features and construct a multidimensional feature library. Subsequently, principal component analysis is used to evaluate the sensitivity of each feature to different faults and establish a highly sensitive multimodal feature system. On this basis, a TCN-BiGRU-PHA diagnostic model combining time convolutional network, bidirectional gated loop unit and attention mechanism is constructed to realize accurate identification of winding deformation faults. The experimental results show that the method has higher recognition accuracy under multiple types of faults, which provides feasible ideas and methodological support for realizing online intelligent monitoring of transformer winding deformation.

This study investigates the applicability of a compact terrestrial laser scanner (Leica BLK360) for assessing the verticality of a tall industrial chimney, and compares its performance with high-precision total station measurements. In the first phase, a reference control network was established and observed using the tangential envelope method with a total station, providing a precise benchmark. Terrestrial laser scanning was then carried out, and circles were fitted to horizontal cross-sections extracted from the point cloud. A least-squares approach was used to calculate chimney-axis deviations and evaluate verticality along the height of the structure. The results of both methods revealed a consistent trend of deviations increasing with height. Maximum differences between the total station and TLS measurements did not exceed 5 mm, which remains within acceptable geodetic tolerance. This demonstrates that the BLK360 is capable of providing sufficiently accurate data for preliminary deformation monitoring of tall engineering structures. The main advantage of the BLK360 scanner lies in its rapid and automated data acquisition, which allows for more frequent observations, reduced fieldwork time, and early detection of structural irregularities. However, limitations such as a reduced measurement range, lower sensitivity under unfavorable conditions, and dependency on surface reflectivity were also identified. Despite these constraints, the study confirms that the BLK360 can serve as a valuable supplementary tool to conventional total station surveys, offering practical support for ongoing monitoring, and contributing to improved safety in engineering practice.

Accurate quantification of ocean tide loading (OTL) is essential for sustainable coastal geodetic monitoring, infrastructure stability assessment, and the interpretation of GNSS vertical displacement time series. This study analyzes long-term vertical displacements observed at the Palmido GNSS station, located in Korea’s largest tidal-range environment, to resolve dominant semi-diurnal and diurnal tidal constituents. Coherent-gain–corrected Fast Fourier Transform (FFT) and continuous wavelet analysis were applied to decompose the GNSS time series, with particular emphasis on the principal lunar (M2) and principal elliptical lunar (N2) constituents. The extracted tidal amplitudes and phases were benchmarked against the NAO99 ocean tide loading model after applying load Love number (LLN) and site-scale corrections. Quantitative evaluation demonstrates that the corrected NAO99 predictions reduce the root mean square difference (RMSD) of the M2 constituent from approximately 14.5 mm to 13.3 mm (≈8% improvement) and that of the N2 constituent from about 2.1 mm to 1.2 mm (≈40% improvement), compared to uncorrected model outputs. Linear regression analyses further show that amplitude scaling improves toward unity for M2 after correction, while maintaining strong phase coherence. Continuous wavelet scalograms reveal persistent semi-diurnal energy with a clear fortnightly modulation, whereas diurnal components appear intermittently and are more sensitive to local environmental conditions. These results demonstrate that combining coherent-gain–corrected FFT, time–frequency wavelet diagnostics, and physics-based NAO99 benchmarking significantly enhances the reliability and interpretability of GNSS-derived tidal loading estimates. The proposed workflow provides a transferable and reproducible framework for high-precision coastal deformation monitoring and long-term sustainability assessments in macrotidal environments.

Accurate deformation monitoring is essential for ensuring the stability of deep vertical shafts. In this study, a temperature-compensated fiber Bragg grating (FBG) sensing system was deployed in the 882 m deep Guotun Coal Mine shaft to measure circumferential and vertical strains at six depths. A site-specific mechanical model integrating stratigraphy, dual-layer concrete lining, and the influence radius was developed to analyze shaft wall stresses. The monitoring results reveal pronounced spatial anisotropy, with circumferential compressive and tensile strains at deeper levels nearly twice those at shallow levels. Strain variation also increases over time, reflecting the combined effects of groundwater fluctuations and overburden consolidation. The stresses inferred from measured strains agree well with the analytical solution in both magnitude and depth-dependent trend, with deviations remaining within a reasonable engineering margin. All stresses are below the strength limits of the C70/C50 concrete lining, confirming that the shaft is in a safe stress state. The proposed monitoring–analysis framework provides a reliable basis for evaluating shaft wall behavior under complex hydrogeological conditions.

Hard coal seams in the Ordos region are key geological factors contributing to rock bursts. To overcome the limitations of conventional drilling pressure-relief techniques—such as insufficient unloading efficiency, reliance on high-density multi-round drilling, and support failure—this study establishes a non-isobaric stress field model and analyzes the influence of coal seam strength and drilling diameter on the radius of the drilling-induced plastic zone. Based on this analysis, a coupled “shallow support and deep pressure relief” unloading-support technology was proposed. A 70-m on-site comparative industrial test was conducted using coal-powder monitoring, coal-cannon monitoring, stress monitoring, and surrounding-rock deformation monitoring to evaluate pressure-relief and support performance. The results show that the plastic zone radius is mainly controlled by coal strength and borehole diameter, with diminishing benefits when enlarging the diameter beyond a threshold. The enhanced pressure-relief zone produced 3.1 times more coal powder than traditional drilling, and coal-cannon events concentrated in the 7–14 m range effectively released accumulated elastic energy. Post-relief stress peaks were significantly reduced and recovered more slowly. In terms of roadway stability, anchor-cable stress remained lower and more stable than under conventional drilling, with roadway side convergence and roof–floor convergence reduced by 63% and 51%, respectively. A comprehensive mechanical drilling–based anti-burst technology and equipment system was developed and successfully applied in engineering practice. These findings provide theoretical support and practical guidance for pressure relief and support strategies in hard coal seams of the Ordos region and similar mining conditions.

Terrestrial laser scanning (TLS) point clouds require high-precision registration and georeferencing to be used effectively. Only then can data from multiple stations be integrated and transformed from the instrument’s local coordinate system into a common, stable reference frame that ensures temporal consistency for further analyses of displacement and deformation. The article demonstrates the validation of an innovative referencing system devised to improve the reliability and accuracy of registering and georeferencing TLS point clouds. The primary component of the system is openable reference spheres, whose centroids can be directly and precisely determined using surveying methods. It also includes dedicated adapters: tripods and adjustable F-clamps with which the spheres can be securely mounted on various structural components, facilitating the optimal distribution of the reference markers. Laboratory tests with four modern laser scanners (Z+F Imager 5010C, Riegl VZ-400, Leica ScanStation P40, and Trimble TX8) revealed sub-millimetre accuracy of sphere fit and form errors, along with the sphere distance error within the acceptance threshold. This confirms that there are no significant systematic errors and that the system is fully compatible with various TLS technologies. The registration and georeferencing quality parameters demonstrate the system’s stability and repeatability. They were additionally verified with independent control points and geodetic levelling of the centres of the spheres. The system overcomes the critical limitations of traditional reference spheres because their centres can be measured directly using surveying methods. This facilitates registration and georeferencing accuracy on par with, or even better than, that of commercial targets. The proposed system serves as a stable and repeatable reference frame suitable for high-precision engineering applications, deformation monitoring, and longitudinal analyses.

This study proposes a scalable method for regional bridge health monitoring by combining Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) with unsupervised machine learning. Among various non-contact monitoring techniques, such as drone- or LiDAR-based methods, MT-InSAR provides unique advantages for large-scale bridge assessment by offering continuous, weather-independent, and cost-free deformation measurements across extensive regions. The objective of this study is to automatically detect bridges exhibiting unusual displacement behaviour using publicly available satellite imagery. A total of 48 Sentinel-1A satellite images (January 2020–December 2023) were processed using SARPROZ software to extract line-of-sight (LOS) displacements at the mid-span of 14 bridges over the Illinois River corridor in the United States. The selected bridges represent diverse superstructure types (truss, girder, arch) and transportation modes (highway, rail). MT-InSAR analysis reveals seasonal displacement trends ranging from +12 mm to −18 mm, with thermal and traffic data used to contextualise patterns. K-means clustering identifies a behavioural outlier for one of the bridges. Isolation Forest confirms this anomaly for the same bridge. The cumulative displacement of the bridge exceeds 20 mm, diverging from seasonal norms. Cross-validation using the most recent bridge inspection report reveals section loss on truss members and exposed footing at piers.

NIR-II-responsive conductive silk fibroin microneedle patches for postoperative melanoma ablation, infected-wound healing, and real-time monitoring

HighlightsWhat is the main finding?The study developed a spatial 3D scale that provides high-precision scale infor-mation and proposed a UAV visual deformation monitoring method with 3D scale constraints.What are the implications of the main findings?This method effectively addresses the scale difference between the survey area model and the real model caused by low-quality images and low-precision control points, meeting the needs of high-precision engineering defor-mation monitoring.The spatial 3D scale has low production cost and simple on-site deployment, which enhances the applicability of UAV visual deformation monitoring and pro-vides a reliable solution for large-scale deformation monitoring.Aiming at the problem that low-quality images and low-precision control points lead to scale differences between the survey area model and the real model in UAV (Unmanned Aerial Vehicle) vision-based 3D deformation monitoring, which impairs the accuracy of deformation monitoring, this paper develops a spatial 3D scale for providing high-precision scale information and proposes a UAV vision-based deformation monitoring method with 3D scale constraints, thereby improving the deformation monitoring accuracy in large-scale survey areas. Experimental results show that compared with the monitoring method using only control points as constraints, the proposed method achieves accuracy (RMSE) improvement rates of 38.6% and 48.1% in the horizontal and elevation directions respectively during four phases of UAV operations, and the 3D deformation accuracy (RMSE) improvement rate remains at approximately 42.3% during seven phases of UAV operations. This verifies the effectiveness and reliability of the UAV vision-based deformation monitoring method with 3D scale constraints.

In response to the bottleneck problems of weak landslide crack morphology, hidden features, and limited extraction accuracy in complex terrain masking and dense vegetation coverage environments, as well as the shortcomings of existing methods in cross scale and multi-source heterogeneous data fusion, this study proposes an automatic landslide crack extraction algorithm based on InSAR and UAV LiDAR point cloud collaboration. This algorithm relies on SBAS InSAR technology to achieve large-scale, long-term surface deformation monitoring, and identifies landslide deformation active areas through deformation rate threshold division and spatial clustering. In terms of fusion mechanism, a combination of control point matching and ICP (Iterative Closest Point) algorithm is adopted to accurately register the deformation zone data obtained by InSAR monitoring with the point cloud data obtained by UAV LiDAR, achieving effective fusion of cross scale and multi-source heterogeneous data. On this basis, guide the UAV LiDAR to conduct targeted fine scanning and obtain high-resolution 3D point cloud data. Based on point cloud, a three-dimensional model of landslide crack development area is constructed, and multidimensional morphological features such as width, direction, slope, and curvature are extracted. Discriminant feature vectors are constructed, and a probabilistic neural network (PNN) model is introduced to achieve probability classification of crack pixels through Gaussian kernel density estimation and Bayesian decision mechanism. Finally, edge extraction is optimized by Canny operator to achieve automated and high-precision recognition of crack contours. Fifty independent test cases were selected for the experiment, covering various types of landslides such as shallow soil landslides and rock landslides. The results showed that the proposed method performed well in multi vegetation covered environments, with IoU stability above 0.94, significantly better than existing mainstream methods, and had good robustness and engineering applicability.

Fast Semiautomatic Change Point Detection in Deformation Monitoring Using Kullback–Leibler Divergence

Abstract. The article is devoted to the analysis of the role of geodetic works as a direct element of the surveying concept in the life cycle management of real estate objects. Geodetic works are considered as an integral part of ensuring the accuracy, safety and efficiency of construction processes, which cont-ributes to the extension of the service life of objects and increasing their socio-economic value. Modern approaches to the performance of geodetic works at the stages of construction and operation of buil-dings and structures are considered in the context of integration into the surveying concept. It is empha-sized that geodetic support ceases to be only an auxiliary function in the construction process and turns into an integral part of the object management system throughout its entire life cycle. The main tasks performed by geodesists during the construction of structures are analyzed, in particular, the creation of a geodetic base, transferring the project to nature, executive surveying, control of installation accuracy, as well as deformation monitoring. Special attention is paid to geodetic monitoring during the operational period, which allows for timely detection of dangerous changes in the spatial position of structural elements. The article substantiates the feasibility of using modern surveying tools - 3D scanning, robotic total stations, satellite positioning systems (GNSS), unmanned aerial vehicles and geoinformation technologies (GIS) and integration with BIM modeling, which significantly expand the capabilities of engineering and geodetic control. It is pro-posed to consider a surveyor as a participant in management decisions in the field of construction, who operates with spatial data, forms analytical conclusions and provides digital management of objects. Using the example of research by SE "NIIBV", organizational and technological solutions for the use of instrumental monitoring systems in the construction and operation of buildings of various types, inclu-ding historical buildings and objects located in difficult engineering and geological conditions are sub-stantiated. The results of domestic and foreign studies on the implementation of the surveying concept are summarized, confirming its effectiveness both in increasing the reliability of structures and in opti-mizing costs, duration and legal support of construction processes. The conclusions emphasize that the integration of geodetic works into the surveying concept contributes to increased safety, reduced acci-dent risks and optimization of operating costs, which is critically important for modern construction in the context of urbanization and complex geological challenges.

Abstract Infrastructure, the economy, and the environment are all seriously threatened by slope instability, particularly considering the intensifying effects of climate change and extreme weather. Malaysia, with its persistently high annual rainfall, faces an increasing risk of rainfall-induced landslides that severely affect transportation networks and public safety. This study investigates the integration of the TerraLink System, a hybrid reinforced earth technology, and the Senceive FlatMesh Triaxial Tilt Sensor Network as a dual approach for climate-resilient slope rehabilitation and sustainable real-time monitoring. The selected case study, located along FT185 Section 44.1, Jalan Simpang Pulai–Blue Valley, Perak, represents slope failure affecting a major interstate highway connecting Perak and Pahang. Comprehensive geotechnical investigations were carried out, including subsurface exploration, laboratory testing, and slope stability analysis using the GeoStudio platform based on the Bishop’s Modified Limit Equilibrium Method. The resulting Factors of Safety (FoS) for the three critical valleys are 1.537, 1.643, and 1.556 is all exceeded the minimum design requirement of 1.50 stipulated by the Jabatan Kerja Raya (JKR), confirming the TerraLink system’s effectiveness in achieving slope stability. Concurrently, forty triaxial tilt sensors were deployed at the upslope and downslope regions to enable real-time deformation monitoring and automated early warning dissemination via SMS and email. These systems effectively detected ground movements and triggered proactive mitigation actions, ensuring site safety during and after construction. The results affirm that the integration of climate-resilient infrastructure with intelligent monitoring technologies provides a viable and scalable solution for mitigating climate-induced slope failures. This combined approach enhances structural resilience, minimizes disaster risks, and promotes sustainable, adaptive infrastructure in landslide-prone tropical regions.