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Zongling landslides are the typical karst landslides located in the southwest of China, which have severely threatened the lives of local people. It is of great significance to monitor the real surface deformation of the unstable slopes for the hazard prevention by using the InSAR technology owing to its high precision and frequent revisit. However, the decorrelation effect and atmospheric delay caused by dense vegetation and alpine terrain result in less effective targets to monitor the full deformation of karst landslides. Besides, the derived deformation along one line-of-sight (LOS) direction can result in the misunderstanding of the real kinematics landslide process. In this study, we combine the multi-temporal distributed scatterer InSAR technology (DS-InSAR) and the multi-platform SAR measurements to derive the multi-dimension deformation time series of Zongling landslides. First, both ALOS/PALSAR-2 datasets and Sentinel-1A images from ascending and descending orbits are employed to acquire the deformation along the LOS direction using the DS-InSAR method, where dense effective targets can be achieved, and the deformation patterns can be revealed. Then, the multi-dimensional small baseline subset (MSBAS) method is used to retrieve the horizontal and vertical deformation of Zongling landslides, which further improve the temporal sampling of the deformation time series. Finally, the comprehensive analyses of driving factors and failure mode are conducted according to the spatial–temporal deformation characteristics, and field investigation. The results indicate that this technical route can not only retrieve the landslide deformation with high spatial–temporal sampling density, but also provide new insight into the driven effect of natural factors and mining activities on karst landslides.

The Xi’an region of China has been suffering from groundwater depletion, ground fissure hazards, and surface subsidence for a long time. Due to the complex tectonics and frequent human and natural activities, land deformation in the region is aggravated, posing a threat to infrastructure and human life. This study adopted the multi-orbit and multi-temporal InSAR technology to measure multi-dimensional displacements and time-series displacements in Xi’an City. Through the multi-dimensional deformation verification, it was found that the control of groundwater flow direction by ground fissures is the cause of horizontal deformation. On the contrary, the flow direction of groundwater from west to east was inferred using multi-dimensional deformation. Further analysis was performed by calculating the deformation gradient of the cumulative deformation to obtain differential land subsidence and angular distortions, and it was quantitatively determined that the threshold for the generation of ground fissures caused by differential subsidence is 1/500. Then, through the mutual verification of the time series data and the groundwater level, a positive correlation was obtained. However, due to the inconsistent geological conditions and soil layers at the monitoring positions of Well 2 and Well 3, the lag time was 64 days and 4 days, respectively. Finally, the relationship between the surface deformation and the groundwater in the sustained uplift areas was explored. The Well 1 groundwater-level data with a monitoring period of 22 years and the corresponding monitoring points’ time series data were modeled; it was concluded that, in the future, the groundwater level will continue to rise and surface deformation will mainly increase, without a slowing trend. Therefore, research on the impact of surface uplift on infrastructure should be strengthened. By quantifying the relationship between land subsidence, ground fissures, and the groundwater level in Xi’an, the results of this study provide a reference for groundwater monitoring and management.

Structural health monitoring of long-span bridges is critical to their safe operation and ensuring efficient daily traffic. Ground-based interferometric radar (GBIR) and inertial vision-based measurement (IVM) can capture linear and point deformation of long-span bridges, respectively. In this paper, we propose a framework to obtain a multi-dimensional dynamic deformation time series by fusing these two datasets with procedures of spatial-temporal alignment, interpolating, established deformation spatial-temporal correlation models, and weighting. To our knowledge, it was experimented on the Xijiang Railway Bridge, located in Guangdong, China, which is the first combination of these two data. Deformations along the vertical and lateral directions were derived when trains crossed the bridge. To validate the effectiveness of the derived results, static leveling sensors and vibrometers were employed on the bridge to obtain instantaneous measurements. The results show that the derived deformation is consistent with these in-situ measurements and the accuracy has improved by 27.4% and 27.0% compared with GBIR and IVM, respectively. The framework combining GBIR and IVM performs well in multi-dimensional dynamic deformation monitoring of long-span bridges and can play an important role in structural health monitoring of similar structures.

In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, we can map the real-time output light intensity of POF sensors to the spatial morphology of the elastomer. By leveraging the intrinsic correlations of neighboring sensors and machine learning algorithms, we realize the spatially resolved detection of the pressing and multi-dimensional deformation of elastomers. Specifically, the developed intelligent sensing system can effectively recognize the two-dimensional indentation position with a prediction accuracy as large as ~99.17%. The average prediction accuracy of combined strain and twist is ~98.4% using the random forest algorithm. In addition, we demonstrate an integrated intelligent glove for the recognition of hand gestures with a high recognition accuracy of 99.38%. Our work holds promise for applications in soft robots for interactive tasks in complex environments, providing robots with multidimensional proprioceptive perception. And it also can be applied in smart wearable sensing, human prosthetics, and human–machine interaction interfaces.

The experimental study of shale fracture development is very important. As a channel of permeability, a fracture has a great influence on the development of shale gas. This study presents the results of a fracture evaluation in the Silurian Longmaxi Shale using the laboratory triaxial compression experiments and CT reconstruction, considering both mechanical properties and fracture network multi-dimensional quantitative characterization. The results indicate that the plastic deformation stage of shale lasts longer under high confining pressure, whereas radial deformation is restricted. Confining pressure has a nice linear connection with both compressive strength and elastic modulus. The 2D fractal dimension of radial and vertical cracks is 1.09–1.28 when the confining pressure is between 5 and 25 MPa. The 3D fractal dimension of the fracture is 2.08–2.16. There is a linear negative correlation at high confining pressure (R2 > 0.80) and a weak linear association between the 3D fractal dimension of the fracture and confining pressure at low confining pressure. The fracture angle calculated by the volume weight of multiple main cracks has a linear relationship with the confining pressure (R2 > 0.89), and its value is 73.90°–52.76°. The fracture rupture rate and fracture complexity coefficient are linearly negatively correlated with confining pressure (R2 > 0.82). The Euler number can well characterize the connectivity of shale fractures, and the two show a strong linear positive correlation (R2 = 0.98). We suggest that the bedding plane gap compression, radial deformation limitation, and interlayer effect weakening are efficient mechanisms for the formation of shale fracture networks induced by confining pressure, and that confining pressure plays a significant role in limiting and weakening the development of shale fractures, based on the quantitative characterization results of fractures.

The Sichuan-Yunnan region is located on the southeastern edge of the Tibetan Plateau. The region has long been characterized by frequent and intense seismic activity. Small and medium-sized earthquakes are found almost everywhere in the region, making it one of the most seismically active areas in mainland China. We have established a multi-dimensional monitoring and evaluation system based on long-term geological data, seismic data, geodetic data, etc. We combine underground activities with surface monitoring data and are constrained by high-density observation data from the Global Navigation Satellite System (GNSS) autonomous network construction. Finally, based on the factor analysis method, the weights of evaluation indicators are established, and a crustal stability evaluation model is constructed using the multi-factor weighted superposition method to quantitatively evaluate the crustal stability of the Sichuan Yunnan region. The evaluation results show that the Sichuan-Yunnan area is mainly a relatively unstable and unstable region, accounting for 50.32% of the total area of the total area. The level of east–west stability is obvious, and the stability of the west-to-east has gradually increased. Among them, the unstable zone is mainly located in Xianshuihe district, Longmenshan district, Anning river-Daliangshan-Xiaojiang district, Jinsha river-Longpan Qiaohou-Red River district. Overall, the Y-shaped structure zone has the worst crustal stability near the Y-shaped structure belt. The west is second and the east is the best. This evaluation is of great significance for urban construction, engineering development planning, major engineering site selection, and safety evaluation in the Sichuan-Yunnan region. The construction site provides scientific support. Graphical Abstract

Structural biology has evolved greatly due to the advances introduced in fields like electron microscopy. This image-capturing technique, combined with improved algorithms and current data processing software, allows the recovery of different conformational states of a macromolecule, opening new possibilities for the study of its flexibility and dynamic events. However, the ensemble analysis of these different conformations, and in particular their placement into a common variable space in which the differences and similarities can be easily recognized, is not an easy matter. To simplify the analysis of continuous heterogeneity data, this work proposes a new automatic algorithm that relies on a mathematical basis defined over the sphere to estimate the deformation fields describing conformational transitions among different structures. Thanks to the approximation of these deformation fields, it is possible to describe the forces acting on the molecules due to the presence of different motions. It is also possible to represent and compare several structures in a low-dimensional mapping, which summarizes the structural characteristics of different states. All these analyses are integrated into a common framework, providing the user with the ability to combine them seamlessly. In addition, this new approach is a significant step forward compared with principal component analysis and normal mode analysis of cryo-electron microscopy maps, avoiding the need to select components or modes and producing localized analysis.

Successfully detecting ground deformation, especially landslides, using InSAR has not always been possible. Improvements to existing InSAR tools are needed to address this issue. This study develops and evaluates two novel approaches that use multidimensional InSAR products to detect surface displacements in the landslide-prone region of Büyükalan, Antalya. Multi-temporal InSAR analysis of Sentinel-1 data (2015–2020) is performed using LiCSAR–LiCSBAS, followed by two novel approaches: multi-dimensional InSAR research and analysis (MIRA) and Crosta’s InSAR application (InCROSS). Cumulative LOS velocity maps reveal deformation rates of −1.1 cm/year to 1.0 cm/year for descending tracks and −3.8 cm/year to 3.8 cm/year for ascending tracks. Vertical displacements range from −1.9 cm/year to 2.3 cm/year and east–west components from −2.8 cm/year to 2.9 cm/year. MIRA uses an n-Dimensional Visualizer and SVM classifier to identify deformation clusters, and InCROSS applies PCA to enhance deformation features. MIRA increases the deformation detection capacity compared to conventional InSAR products, and InCROSS integrates these products. A comparison of the results reveals 80.48% consistency between them. Overall, the integration of InSAR with statistical and multidimensional analysis significantly enhances the detection and interpretation of ground deformation patterns in landslide-prone areas.

Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope to its critical state preceding landslides. Hence, analyzing landslide safety risks over time demonstrates a significant degree of hysteresis, highlighting the necessity for a comprehensive approach to risk assessment that encompasses both gradual and sudden precursors to landslide events. This study analyzes the factors affecting slope stability and establishes a slope evaluation indicator system that includes terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestation. It proposes a quantitative model for slope landslide risk assessment based on a fuzzy broad learning system, aiming to accurately assess slopes with different risk levels. The overall assessment accuracy rate reaches 92.08%. This multi-dimensional risk assessment model provides long-term monitoring of slope conditions and scientific guidance on landslide risk management and disaster prevention and mitigation on a long time scale for risky slopes in reservoir areas.

In composite structures or complex concrete members, some concrete bears multiple forces, called core concrete. The properties of the core concrete are variable under complex stress conditions, which will influence the structure performance analysis. Therefore, it is necessary to establish an accurate and theoretical constitutive model of concrete under complex stress conditions. The elastic–plastic properties of concrete in complex stress conditions were analyzed first. Then, the failure criterion of concrete in complex stress conditions was discussed to identify the key parameters. And the relationship between the stress–strain curve and failure criterion was analyzed through mathematical derivation. Finally, the multi-dimensional iterative constitutive model of concrete under complex stress conditions was established and verified. Based on the analysis results, the concrete under multi-axial stress conditions shows a spindle-shape stress envelope diagram. The failure criterion should be established by the analysis of concrete under high multi-axial compression conditions, tension–compression conditions, and shear–compression conditions. The plastic modulus is the key to reflecting the plastic strain development trend and the stress–strain relationship.