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Ground deformation poses a serious threat to the safety of subway structures. Consequently, intelligent and efficient automated safety monitoring of ground deformation along the subway has become urgent. Traditional engineering observation methods have the disadvantages of difficulties with datum selection, non-automation, and poor reliability. A ground deformation monitoring system for subway structure safety based on the Global Navigation Satellite System (GNSS) was established and validated through experimental comparisons with traditional precision leveling in this study. Based on the GNSS monitoring points, the continuous kinematic observation GNSS data of ground deformation along the subway line were obtained; a joint robust local mean decomposition (RLMD)–singular value decomposition (SVD) noise-reduction processing method for GNSS signals was proposed to realize the real-time and high-precision monitoring of ground deformation. The results show that the proposed combined noise-reduction method can reduce the maximum noise amplitude by 86%. When compared with the accuracy of the traditional precision leveling method, it was determined that the vertical positioning accuracy of the deformation monitoring system is greater than 2.7 mm, the horizontal positioning accuracy is greater than 1.3 mm, and the measurement error is less than 1.5 mm. The deformation monitoring system has the advantages of convenience, automation, and high accuracy and can be applied to ground deformation monitoring for subway structures.

With the acceleration of urbanization, surface deformation monitoring has become crucial. Existing monitoring systems face several challenges, such as data singularity, the poor nighttime monitoring quality of video surveillance, and fragmented visual data. To address these issues, this paper presents a 3D real-scene (3DRS)-enhanced GNSS/intelligent vision surface deformation monitoring system. The system integrates GNSS monitoring terminals and multi-source meteorological sensors to accurately capture minute displacements at monitoring points and multi-source Internet of Things (IoT) data, which are then automatically stored in MySQL databases. To enhance the functionality of the system, the visual sensor data are fused with 3D models through streaming media technology, enabling 3D real-scene augmented reality to support dynamic deformation monitoring and visual analysis. WebSocket-based remote lighting control is implemented to enhance the quality of video data at night. The spatiotemporal fusion of UAV aerial data with 3D models is achieved through Blender image-based rendering, while edge detection is employed to extract crack parameters from intelligent inspection vehicle data. The 3DRS model is constructed through UAV oblique photography, 3D laser scanning, and the combined use of SVSGeoModeler and SketchUp. A visualization platform for surface deformation monitoring is built on the 3DRS foundation, adopting an “edge collection–cloud fusion–terminal interaction” approach. This platform dynamically superimposes GNSS and multi-source IoT monitoring data onto the 3D spatial base, enabling spatiotemporal correlation analysis of millimeter-level displacements and early risk warning.

The paper describes the fundamental issues of deformation monitoring systems and instrumental methods for measuring the stress-strain state of the rock massif based on the use of three-component strain sensors developed by specialists from the University of Mines and Avangard OJSC. One of the main tasks of the developed systems is the prediction and prevention of possible dynamic manifestations of rock pressure in rockburst-hazardous deposits.

Slope stability is a critical safety and production issue for mining. Major wall failure can occur seemingly without any visual warning, causing loss of lives, damage to equipment, and disruption to the mining process. Monitoring systems, ranging from simple piezometers and extensometers to highly sophisticated radars and global navigation satellite systems, are employed to predict impending instabilities and failure. Here, we provide a review of the available monitoring systems used in slope management and highlight their major advantages and shortcomings. We propose a simple method for evaluating the effectiveness and reliability of monitoring systems to warn of pending slope failures. The method is based on constructing monitoring reliability maps for the slope by evaluating two slope parameters: Expected deformation to failure and critical reading frequency, which depend on the slope characteristics (e.g., geology and design), service condition (e.g., rainfall, blast), and the economic impact of the failure. The reliability of a deformation monitoring system can be subsequently assessed by identifying three parameters of the system: Coverage area (large or discrete), Deformation monitoring precision, and Measurement frequency. The application of the method to most commonly used deformation monitoring systems is demonstrated. The advantages and implications of the proposed method are highlighted.

In view of the present high hardware and communications problems of slope deformation monitoring, design a set based on wireless sensor network (WSN) of the slope deformation monitoring system.The system sets up a platform for wireless sensor network through the TI chip CC2530 and free ZigBee protocol,including the sensor node,the router nodes,the coordinator node,and the use of Kingview realizes the monitoring configuration data of slope deep displacement and ground water.By testing,the system can effectively realize the monitoring of the slope data,reduces the cost of the slope deformation monitoring.

The spatial structure building is a type of building system; it is necessary to monitor deformation to determine its stability and robustness. Under the dynamic deformation of structures, it is challenging to determine appropriate zero image (the reference image) if we use the PST-IM- MP (photograph scale transformation-image matching-motion parallax) method to obtain the deformation of structures. This paper offers the Z-MP (zero-centered motion parallax) method to solve these problems and offers PDMS (Photography Dynamic Monitoring System) based on the digital photography system to monitor the dynamic deformation of the tennis stadium located in Jinan Olympic Sports Center. The results showed that the spatial structures of the tennis stadium were robust, and the deformations were elastic and within the permissible value. Compared with the PST-IM-MP method, the Z-MP method is more suitable for deformation monitoring structures under real-time deformation. This paper indicates PDMS has advantages of the simplicity of operations, automation, and the ability of non-contact dynamic deformation monitoring for multiple points in a short period. In the future, it will have broader application prospects.

Multi-field information monitoring is useful to better understand the deformation and failure behaviour of landslides. Therefore, in this study, a physical slope model under excavation was analysed through multi-field monitoring to ascertain the failure mechanism of the slope. During the process of physical model tests, ① area information including 2D surface displacement, 2D surface strain, velocity, 2D surface temperature, and 3D surface deformation, ② line information including deep displacement and deep strain, and ③ point information of the earth pressure of the model were acquired via multi-field monitoring. The pre-failure, failure, and post-failure stages of the slope model are analysed through multi-field monitoring. The results indicate that the relative displacement between the yielding and stationary parts and a triangular shear plane reflect the deformation behaviour of the slope related to the arching effect. The arch ring expands and becomes elongated during the excavation process. The slope failure time can be effectively predicted via the inverse velocity method. Multi-field monitoring can reveal the behaviour of the slope model from different perspectives and offer new insights into the failure mechanism of the slope.

More than 5000 landslides or potential landslides have been identified in the Three Gorges Reservoir (TGR) region since its impoundment in 2003. These hazards seriously threaten the continuation of reservoir operations and the safety of dams, waterways, and local residents. Understanding the deformation characteristics and kinematic evolution of these landslides can be helpful for assessing their stability and providing long-term landslide predictions. In this study, the deformation characteristics, influencing factors, sliding mechanism, and threshold reservoir level of the Muyubao landslide are revealed. Data from a professional monitoring network implemented in 2006 reveal that the deformation of this landslide is in a stage of creep characterized by overall movement, and the rate of creep is gradually slowing. The deformation mechanism is investigated by combining meteorological, hydrological, and reservoir level data with displacement measurements from surface cracks, manual and automatic GPS monitoring records, and field investigations and drilling surveys. The reservoir level is the main driving factor of deformation, whereas the filling-drawdown rate is not directly related to the deformation of the landslide. Furthermore, rainfall can promote deformation during periods with high reservoir levels. The deformation of the landslide follows a very distinctive pattern featuring large displacements after the reservoir level rises to a high level (from late October to early March of the following year) and a rapid transition to constant deformation as the reservoir level drops. The results indicate that the reservoir level of 172 m is the general threshold reservoir level of the Muyubao landslide. At present, the deformation rate of the Muyubao landslide is gradually decreasing, and the probability of large-scale sliding is low; however, the monitoring system must be strengthened and additional research must be performed on the deformation mechanism.

Traditional surveying methods have various drawbacks in monitoring cable-stayed bridge deformations. Global Navigation Satellite System (GNSS) technology is increasingly recognized for its critical role in structural deformation monitoring, providing precise measurements for various structural applications. Accurate signal extraction is essential for reliable deformation monitoring, as it directly influences the quality of the detected structural changes. However, effective signal extraction from GNSS data remains a challenging task due to the presence of noise and complex signal components. This study integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and wavelet packet decomposition (WPD) to extract GNSS deformation monitoring signals for the ropeway pillar. The proposed approach effectively mitigates high-frequency noise interference and modal mixing in GNSS signals, thereby enhancing the accuracy and reliability of deformation measurements. Simulation experiments and real-world scenario applications with operational field data processing demonstrate the effectiveness of the proposed method. This research contributes to advancing GNSS-based deformation monitoring techniques, offering a robust solution for detecting and analyzing subtle structural changes in various engineering contexts.