Displacement Monitoring Research Articles

Application of Machine Learning in Forecasting the Impact of Mining Deformation: A Case Study of Underground Copper Mines in Poland

Open access to SAR data from the Sentinel 1 missions allows analyses of long-term ground surface changes. The current data-acquisition frequency of 12 days facilitates the continuous monitoring of phenomena such as volcanic and tectonic activity or mining-related deformations. SAR data are increasingly also used as input data in forecasting phenomena on the basis of machine learning. This article presents the possibility of using selected machine learning algorithms in forecasting the influence of underground mining activity on the ground surface. The study was performed for a mining protective area with a surface of over 500 km2 and located in western Poland. The ground surface displacements were calculated for the period from November 2014 to July 2021, with the use of the Small Baseline Subset (SBAS) method. The forecasts were performed for a total of 22 identified subsidence troughs. Each of the troughs was provided with two profiles, with a total of more than 10,000 identified points. The selected algorithms served to prepare 180-day displacement forecasts. The best results (significantly better than the baseline) were obtained with the ARIMA and Holt models. Linear models also provided better results than the baseline and their performance was very good at up to 2 months forecasting. Tree-based models including their sophisticated ensemble versions: bagging (Random Forest, Extra Trees) and boosting (XGBoost, LightGBM, CatBoost, Gradient Boosting, Hist Gradient Boosting) cannot be used for this type of predictions since Decision Trees are not able to extrapolate and thus are not a valid stand-alone tool for forecasting in this type of problems. A combination of satellite remote sensing data and machine learning facilitated both the simultaneous quasi-permanent monitoring of ground surface displacements and their forecasting in a relatively long time period.

Influencing factors, deformation mechanism and failure process prediction for reservoir rock landslides: Tanjiahe landslide, three gorges reservoir area

Compared with terrestrial rock landslides, reservoir rock landslides are also affected by the rise and fall of the reservoir water level, and these landslides are more threatening. High-speed debris flows may form once they lose stability, and once they enter the water a surge is formed. This endangers the safe operation of reservoirs. This study explored the deformation characteristics and influencing factors of the Tanjiahe reservoir rock landslide in the Three Gorges Reservoir using field investigations, GPS surface displacement monitoring, and groundwater level monitoring. The discrete element system MatDEM was used to simulate failure motion, and predict the hazard area affected by the Tanjiahe landslide. The results show that within the reservoir water variation section (145–175 m), the Tanjiahe landslide mass was composed of surface soil (156–175 m) with low permeability and deep cataclastic rock (145–156 m) with high permeability. Due to the difference in permeability between the deep and surface layers, the response of landslide deformation to water level rise is not obvious. The high-level (175 m) operation of the reservoir and the decline in the reservoir water level (175–145 m) are key factors affecting the landslide deformation. Rainfall had a positive effect on landslide deformation. Under their combined action, the stability of the front gentle anti-sliding section of the landslide decreases, and the displacement of the middle and rear steeper sliding section increases under the driving force, which may lead to slope failure. The simulation results show that the upper part of the Tanjiahe landslide slides first and pushes the lower part to move, which is a typical of thrust load-caused failure. The speed of the sliding mass has three stages: rapid rise, rapid decline, and slow decline. The higher the slope angle, the higher the acceleration of the sliding mass in the direction parallel to the slope surface, the higher the speed peak value and the faster the sliding mass speed reaches the peak value. During the failure process, energy is transferred between sliding mass through collisions. Landslides can easily lead to debris flow. The maximum height of the first wave generated when the debris flow entered the water is 5.95 m, and the wave height that propagated to the opposite bank is 3.09 m. The landslide-induced waves propagated along the reservoir area for 30 km.

Research on Dam Deformation Prediction Model Based on Optimized SVM

Although constructing a dam can bring significant economic and social benefits to a region, it can be catastrophic for the population living downstream when it breaks. Given the dynamic and nonlinear characteristics of dam deformation, the traditional dam prediction model has been unable to meet the actual engineering demands. Consequently, this paper advocates for a novel method to solve this issue. The proposed method is based on the optimization of improved chicken swarm (ICSO) and support vector machine (SVM). To begin with, the mean square error is used as the objective function, and then, we apply the improved chicken swarm algorithm to iterate continuously, and finally, the optimal SVM parameters are obtained. Through the modeling and simulation experiments of a nonlinear system, the validity of the improved chicken swarm algorithm to optimize an SVM model has been verified. Based on the horizontal displacement monitoring data of FengMan Dam, this paper analyzed the influencing factors of horizontal displacement. According to the results, three prediction models have been established, respectively: the SVM prediction model optimized by the improved chicken swarm algorithm, the SVM prediction model optimized by the basic chicken swarm algorithm and the BP neural network prediction model optimized by the genetic algorithm. The obtained results from the experiment authenticate the validity and superiority of the proposed method.

The Dynamic Displacement Monitoring Method Using Unmanned Aerial Vehicles Based on Digital Close-Range Photogrammetry

How to carry out fast and reliable dynamic deformation monitoring of building structures is an important issue for engineers and technicians. Digital close-range photogrammetry technology has the advantages of noncontact, multipoint, and rapid monitoring, and the application of UAVs makes the monitoring task no longer limited by the size of the field, but the current technology can not guarantee the rapid deployment of UAVs in dynamic deformation monitoring, and these methods commonly have higher requirements for image quality, and cannot ensure the stability of accuracy. To this end, we propose a dynamic displacement monitoring method for UAVs based on digital close-range photogrammetry technology and optimize the data processing process for the problem that there is still moving when the UAV hovers. A building with an overhead corridor structure with a large flow of people was selected for the experiment. The experiment used ground digital cameras and drones to monitor separately to compare the monitoring results. The results show that the proposed method is simple and easy to use. The monitoring method can be deployed quickly, which is expected to be applied to emergency monitoring scenarios.

Effects of Weak Bedding Plane, Fault, and Extreme Rainfall on the Landslide Event of a High Cut-Slope.

High cut-slopes are widespread in engineering constructions and often converted into landslides. Some extreme circumstances facilitate the landslide process, such as the weak bedding plane, rainfall, and faults. Therefore, this paper intends to offer insights into the influence of the weak bedding plane, extreme rainfall as well as faults on the landslide process of the high cut-slope. In this paper, the Anling landslide in Anhui Province, China, is selected as an example. Geological surveys, displacement monitoring, data analysis, as well as numerical simulation are carried out. The entire excavation construction and landslide deformation process are simulated to reveal the formation mechanism of the landslide using the finite difference code, FLAC3D. The effects of the fault on the landslide and the effectiveness of adjusting protection measures by adding piles are investigated on the basis of the finite difference analysis. According to monitoring data and numerical simulation, the weak bedding plane and extreme rainfall are considered the main factors leading to the Anling landslide. Field investigation and numerical experiments indicate that the fault shall facilitate and accelerate the landslide process. The construction of piles in a suitable position for the landslide is a reasonable and economical measure to stabilize the landslide.

Week-ahead shallow landslide displacement prediction using chaotic models and robust LSTM

Time-series monitoring of landslide displacement is crucial for controlling the geo-risk associated with sudden landslide occurrence and slope failure. Accurate prediction is valuable for geohazard mitigation in advance of short-term displacement. In this research, a novel chaotic modeling framework is proposed to predict landslide displacement using a robust long short-term memory (LSTM) network. To facilitate the prediction framework, daily instant displacement is measured in three dimensions at 19 monitoring locations. Then, the chaotic characteristics are computed for data reconstruction purposes, and the reconstructed data are selected as inputs in the prediction model. Next, LSTM is applied as the prediction algorithm and is trained using reconstructed field data. A generic LSTM is often trained to minimize the mean square error (MSE) loss, which can be oversensitive to a few outliers. In this research, the pseudo-Huber loss is adopted as the loss function and is integrated with LSTM as an improvement over the MSE loss. The effectiveness and efficiency of the proposed framework have been validated by the benchmark LSTM and other machine learning algorithms. The computational results show that the proposed approach performed better than conventional LSTM and other machine learning algorithms. This framework may be valuable for engineers for practical landslide hazard estimation or rapid preliminary screening of slope stability.

Evaluating and Analyzing the Potential of the Gaofen-3 SAR Satellite for Landslide Monitoring

Gaofen-3 is the first Chinese spaceborne C-band SAR satellite with multiple polarizations. The Gaofen-3 satellite’s data has few applications for monitoring landslides at present, and its potential for use requires further investigation. Consequently, we must evaluate and analyze the landslide interference quality and displacement monitoring derived from the Gaofen-3 SAR satellite’s data, particularly in high and steep, mountainous regions. Based on the nine Gaofen-3 SAR datasets gathered in 2020–2021, this study used DInSAR technology to track landslide displacement in Mao County, Sichuan Province, utilizing data from Gaofen-3. Our findings were compared to SENTINEL-1 and ALOS-2 data for the same region. This study revealed that due to its large spatial baseline, Gaofen-3’s SAR data have a smaller interference effect and weaker coherence than the SENTINEL-1 and ALOS-2 SAR data. In addition, the displacement sensitivity of the Gaofen-3 and SENTINEL-1 data (C-band) is higher than that of the ALOS-2 data (L-band). Further, we conducted a study of observation applicability based on the geometric distortion distribution of the three forms of SAR data. Gaofen-3’s SAR data are very simple to make layover and have fewer shadow areas in hilly regions, and it theoretically has more suitable observation areas (71.3%). For its practical application in mountainous areas, we introduced the passive geometric distortion analysis method. Due to its short incidence angle (i.e., 25.8°), which is less than the other two satellites’ SAR data, only 39.6% of the Gaofen-3 SAR data in the study area is acceptable for suitable observation areas. This study evaluated and analyzed the ability of using Gaofen-3’s data to monitor landslides in mountainous regions based on the interference effect and observation applicability analysis, thereby providing a significant reference for the future use and design of Gaofen-3’s data for landslide monitoring.

Remote Sensing Precursors Analysis for Giant Landslides

Monitoring and early warning systems for landslides are urgently needed worldwide to effectively reduce the losses of life and property caused by these natural disasters. Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze–thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems.

Employing vision-based sensing for long-term monitoring

Despite the crucial role of structural health monitoring (SHM), traditional contact-based sensors are costly and lacking automation. Vision-based sensors have recently emerged as a potential substitute, due to their non-contact nature and low cost. However, investigations lack validating their long-term performance. This study assesses vision-based sensing for robust and efficient long-term displacement monitoring. Additionally, the suitability of various machine learning (ML) algorithms in automatically extracting information from the generated data are examined. A first experiment examines the capability to monitor ambiently excited structures over long periods. The parameters assessed are: a) noise and drift (e.g., related to environmental changes such as temperature, humidity, and lighting); and b) obtainable displacement resolution related to ambient excitation. Then, a second experiment examines the capability to monitor dynamically excited structures over long periods (via the employment of an artificial excitation, e.g., an eccentrically loaded mass motor). Here, the following parameters assessed are the: a) frequency bandwidth range; b) frequency resolution; c) displacement resolution related to dynamic excitation; and d) maximum number monitored points for given computational resources. Finally, for processing the generated data, the following ML algorithms are examined: a) Artificial Neural Networks (ANNs); b) support vector machines (SVMs); c) decision trees; and d) Convolutional Neural Networks (CNNs). Concerning long-term monitoring, the paper found that non-contact sensing had comparable accuracy with traditional contact-based sensors. About the ML models, the CNNs were found to be advantageous. Whilst this study was carried out on a small-scale structure, the potential of vision-based sensing for long-term monitoring of full-scale structures was highlighted.

Stability trends of Jinpingzi landslide: A numerical study

The occurrence and development of creeping landslides lead to a gradual destruction of the affected environment; therefore, research into creeping landslide stability trends based on the monitoring of landslide data is of considerable practical value. We investigated the Jinpingzi landslide area II using strength subtraction to analyze the relationship between stress and strain at important points and the stability of the landslide. The results confirm the relationship between the internal failure pattern, safety factor, and key point displacement of creeping landslides. Furthermore, the results confirm the change rules of displacement, in addition to changes in the safety factor. By combining the surface displacement monitoring data of Jinpingzi landslide area II, its stability trend was analyzed based on the established relationship model between deformation displacement and internal failure characteristics of creeping landslides. The results demonstrate that the Jinpingzi landslide area II is in a stable damage deformation. The sliding surface formed in the landslide and the results obtained using the model of the Jinpingzi landslide area II are consistent with the actual situation determined by analysis in the field and exploring the geology.

Retrofit, Restoration and Preservation of Byzantine Monuments: Assessment of Natural Degradation and Evaluation of Intervention Options

Monuments reflect the depth and grandeur of a great civilization developed in a certain region, during a specific period of history. They possess aesthetic, historical and functional values. Undoubtedly, they are seen as part of mankind’s cultural heritage. In particular, the ecclesiastical byzantine monuments in countries where the byzantine civilization flourished, between the 4th and the 15th centuries, such as in Greece and Cyprus, are of great importance, because they are considered as edifices that offer, through their aesthetic and archeological study, a mental upgrade and valuable knowledge of roman and byzantine building techniques. Thus, the need for periodic maintenance and restoration of such monuments is imperative. The objective of the present work is to give a general description of the basic steps of the dominant retrofit methodology which is nowadays used in Greece and Cyprus and leads to the preservation of byzantine monuments. This method is the same as in any other type of monument and goes through four stages: (i) in-situ visual investigation of the degree of wear and on-site experiments, (ii) laboratory testing of the original materials of the monument under study, (iii) installation of a wireless sensor network on a monument under study, aiming at the most efficient monitoring of local displacements, differential foundation settlements, temperature changes, local humidity concentration, salt formation, etc. and (iv) evaluation of several retrofit options and the implementation of the optimal solution, by applying a formulated grading methodology. An immediate finding of the present work is that these steps enable the creation of a suitable analytical model which can be used in runnig a software package, for the design of a complete retrofit/restoration procedure and the preparation of accurate architectural and structural drawings, which will finally be used for the implementation of the maintenance/preservation design. Undeniably, what is important is to select the optimal retrofit procedure for this type of historical buildings, something which is explained in detail in this article. The latter statement expresses the scope of the present work. Another major finding of this review research is that the above retrofit procedure has already been applied to many byzantine monuments, during the past twenty years in Greece and in Cyprus and seems to have already become the dominant method over existing practices, because it uses modern technologies and is more efficient and effective, since it is based on optimization procedures.

Long-term deflection monitoring of a box girder bridge with an optical-fiber, liquid-level system

In this study, a fiber Bragg grating (FBG)–differential settlement measurement (DSM) system was used to conduct 2-year monitoring of the vertical displacement of a prestressed concrete box girder bridge that had previously been damaged in an earthquake. This system is based on the principles of the hydrostatic liquid leveling of connected vessels, buoyancy, the equilibrium of forces, and the photoelasticity of FBGs. Specifically, a series of vertical displacements were measured at 42 observation points along the aforementioned bridge and used to determine its load-carrying capacity. Static vehicle loading tests were also conducted, and the displacement measurements of the FBG–DSM system were compared with those of conventional sensors and the displacement values simulated through finite-element simulation. The results reveal that the FBG–DSM liquid-level system has high potential for the inexpensive, long-term monitoring of bridges over long distances without requiring suitable environmental conditions, in situ visibility and intensive labor work.

A Data-Driven Dam Deformation Forecasting and Interpretation Method Using the Measured Prototypical Temperature Data

Dam deformation is an intuitive and reliable monitoring indicator for dam structural response. With the increase in the service life of the project, the structural response and environmental quantity data collected by the structural health monitoring (SHM) system show a geometric growth trend. The traditional hydraulic-seasonal-time (HST) model shows poor performance in dealing with massive monitoring data due to the multidimensional data collinearity problem and the inaccurate temperature field simulations. To address these problems, this study proposes a data-driven dam deformation monitoring model for dealing with massive monitoring data based on the light gradient boosting tree (LGB) and Bayesian optimization (BO) algorithm. The proposed BO–LGB method can mine the underlying relationship between temperature changes and dam deformation instead of simple harmonic functions. Moreover, LGB is used to simulate the relationship between high-dimensional environmental quantity data and dam displacement changes, and the BO algorithm is used to determine the optimal hyperparameter selection of LGB based on massive monitoring data. A concrete dam in long-term service was used as the case study, and three typical dam displacement monitoring points were used for model training and validation. The experimental results have indicated that the method can properly consider the collinearity in variables, and has a good balance in modeling accuracy and efficiency when dealing with high-dimensional large-scale dam monitoring data. Moreover, the proposed method can explain the contribution difference between different input variables to select the factors with a more significant influence on modeling.

Deformation monitoring and remote analysis of ultra-deep underground space excavation

The development and utilization of urban underground space is producing many deep foundation pits. On-site monitoring is a key issue to ensure the safety of the foundation excavation, especially the monitoring of the lateral displacement of the structure. The ultra-deep circular foundation excavation project (58.65 m deep, the deepest foundation pit in Shanghai) in the test section of the Suzhou River deep tunnel is one of the most challenging engineering around the world. The distributed optical fiber monitoring technology of Brillouin optical frequency domain analysis (BOFDA) was employed to monitor the lateral displacement of the deep foundation. The monitoring process lasted for 14 months, and a total of 19 times of monitoring was carried out following the progress of the excavation of the foundation pit. This paper introduces the process, methods and precautions of optical fiber monitoring from early installation, equipment debugging to data acquisition, data processing and data analysis. By comparing the fiber monitoring data with the manual monitoring data, the deformation of the deep foundation excavation is analysed. The analysis results not only obtain the deformation law of ultra-deep foundation excavation, but also prove the advantages of optical fiber monitoring in deep foundation excavation. Finally, with the support of the Smart Infrastructure Service System (iS3), the research combines optical fiber monitoring with an information platform. Under the framework of the iS3 platform, an integrated data processing and analysis module for fiber monitoring is developed, and the feasibility of remote real-time collection and analysis of monitoring data is discussed. With the support of the information platform, the monitoring data can be transmitted back to the computer terminal to realize remote monitoring.

Analysis of Deep Foundation Pit Pile-Anchor Supporting System Based on FLAC3D

Relying on a deep foundation pit project in Beijing, using FLAC3D three-dimensional finite-difference software simulation combined with displacement monitoring data verification method, the deep foundation pit excavation and three-pile and two-anchor rod support system in anhydrous sand pebble stratum are systematically analyzed, and summed up the variation law of formation stress, internal force of soil nail, axial force of bolt, stress and displacement of pile in the process of excavation and support of deep foundation pit. The results show that during the excavation of the foundation pit, the maximum horizontal displacement of the sidewall is not at the top of the pile and the top of the slope but at a certain distance below the top of the pile. The axial force of the anchor rod is unevenly distributed along the length direction, the axial force of the free section is equal, the axial force of the anchoring section decreases in turn, and the prestress of the anchor rod spreads to the anchoring section after tensioning and locking. The soil-nailed wall is a passive force-bearing system, the force is small during the excavation and support process, and the axial force distribution along the length direction is large in the middle and small at the two ends. By analyzing the variation law of stress and deformation, the timeliness of the data in the construction process is improved, and an effective and reasonable case reference is provided for the informatization construction of simulated relevant working conditions.

Repairing expansive soil channel slope with soilbags

Shallow slope failure often occurs in expansive soil channel slopes because of the strong swelling–shrinkage behaviour of expansive soils and well-developed fissures. In this paper, a repair method for expansive soil channel slopes using soilbags is proposed. Model tests were carried out to illustrate the effect of this repair method. The test results indicate that the assembly of soilbags arranged on the slope limits the deformation of expansive soils after water absorption and reduces infiltration and evaporation during the drying–wetting cycle process. A method for analysing the sliding stability of the repaired slope was suggested and a case study of the repair of a 65 m test expansive soil channel slope with soilbags in the South-to-North Water Transfer Project in China was presented. The monitoring of vertical and lateral displacements of the testing slope demonstrates the effectiveness of using soilbags to repair expansive soil channel slopes.

Vibration compensation for railway track displacement monitoring system using biomedical image processing concept

Vibration compensation in track displacement monitoring is explored by using rail image feature points. The key point is to compare the image of the rail to be tested with the image of the standard rail. A downstream section of Metro Line A is selected for real time analysis of image. The sampling frequency of the detection system is 0.5m/point, and the driving speed of the detection vehicle is about 40km/h, take the distance of 100m between K23+000-K22+900 of kilometer mark, total 200 points, and analyze the image data by grey scale conversion concept. In order to verify the accuracy of the vibration compensation data, one point is taken at an interval of 5m, and a total of 20 points are measured, and compare and analyze with dynamic real-time detection data. After vibration compensation, the geometric parameters of the contact rail are closer to the expected value, and the maximum error can be controlled within ±3mm. Using biomedical image-based processing concept to realize the track displacement monitoring method, which meets the requirements of non-contact measurement, and the degree of automation is high, it can be monitored and alarmed in real time, and it has a good application prospect.

Shallow Failure of Weak Slopes in Bayan Obo West Mine.

The slope stability of large open-pit mines has always been a concern and the analysis of large-scale slope landslides is a focus. However, shallow failure in soft rock slopes also has a serious impact on safe production. The northern slope of Baiyunebo West Mine has many shallow landslides in the final slope, resulting in damage of the maintenance channel of the belt transportation system, which has a serious impact on the safety of production. In order to reduce the shallow failure in weak rock slope, it is necessary to analyze the behavior and characteristics of shallow failure in weak rock. Firstly, the mechanical parameters of the intact rock were obtained by using the exploration data; secondly, through the analysis of blasting-damage range, the distribution characteristics of fractures after the failure of weak rock were obtained. Finally, through theoretical analysis, numerical simulation, surface displacement monitoring and on-site shallow-failure case analysis, the deformation and characteristics of shallow failure of weak rock slope in West Mine were obtained. It was found that the mechanical parameters of rock mass strength on the surface of weak rock slope and the original rock were quite different after mining disturbance. The mode of failure of shallow weak rock slope in the West Mine was creep-cracking; the numerical modelling analysis was carried out by using the assignment method of shallow lithology weakening and gradual change, which is more in line with the deformation characteristics of weak rock slope in West Mine. The lower deformation of the soft rock slope in West Mine is 3–5 times that of the upper deformation. The research results are helpful to understand the influence of blasting on the stability of soft rock slope. At present, West Mine has started to adjust blasting parameters according to the research results, so as to reduce the excessive damage of blasting to rock mass, so the stability of the slope is improved.

Deformation and Damage Characteristics of Deep Rock Specimens Based on 3D-DIC and FBG

Abstract Specimen-scale testing of loaded rock parameters is an important research component of rock mechanics testing. In this paper, a method combining 3D-DIC and FBG (fiber Bragg grating) technology is proposed and applied to the study of deformation and damage characteristics of deep limestone and sandstone specimens under uniaxial loading conditions in the Panxie mine area of Huainan coalfield. The selection of optical fiber, the bonding method, and the process of rock specimen fabrication are introduced in detail. Two different fiber Bragg grating sensor arrays were constructed by using both multi-inclination type and orthogonal type deployment of fiber grating to dynamically monitor the strain response of rock specimens throughout the whole process. The results show that both 3D-DIC displacement monitoring and fiber Bragg grating strain captured the rock deformation and failure process well with identical outcomes. Under a continuous load of 0.3 kN/s, the local cracks of the rock specimen and the damage to the rock specimen resulted in different degrees of sudden changes in the fiber strain value. According to the differences in the degrees of brittleness and texture densities of the rock specimens, the local cracks evolution to transfixion of the limestone registered a certain time accumulation and produced more local cracks, while the sandstone at the same stage produced fewer local cracks within a shorter duration. The effective combination of regional noncontact testing and high-precision point contact can dynamically and accurately capture the rock deformation and damage characteristics at the specimen scale. In addition, the combined monitoring method of 3D-DIC and fiber grating can provide assistance in the prediction of rock crack expansion and potential fracture surfaces.

Assessment of Real-Time PPP with Trimble RTX correction service for real-time dynamic displacement monitoring based on high-rate GNSS observations

Real-time displacement monitoring of earth crust and engineering structures with the Global Navigation Satellite System (GNSS) is highly important for detecting and preventing the damage and possible loss of life during natural phenomena. Nowadays, the Real-Time Kinematic Precise Point Positioning (RT-PPP) method using a stand-alone GNSS receiver can provide fast and accurate dynamic displacement. Trimble Real-Time Extended (RTX) technology enables real-time precise positioning with cm-level accuracy in a short convergence time. The present study investigates, for the first time, the dynamic displacement detection performance of RT-PPP based on CenterPoint RTX correction service for early warning systems and rapid risk assessment. To evaluate the performance of Trimble RTX technology, a series of shake table experiments were conducted and harmonic oscillations with different frequencies and amplitudes were generated. Trimble RTX solutions were obtained at 20 Hz sampling rate and compared with Linear Variable Differential Transformer (LVDT) data, considered as reference, in both frequency and time domains. Results show that Trimble RTX technology can accurately detect the frequency of harmonic motions, whose amplitudes are slightly different from LVDT values because of GNSS inherent noise. The average of Root Mean Square Error (RMSE) values obtained from the differences between LVDT and filtered RTX-derived time series for all events is 3.6 mm. These results indicated that the CenterPoint RTX technology could be employed for real-time and near-real-time applications to monitor structural and earthquake-induced motions accurately.