Real-time Deformation Monitoring Research Articles

Research on an Intelligent Perception Data Collection Method

Abstract This article proposes a sensor anomaly event intelligent perception monitoring technology to address the problem of insufficient intelligent perception of landslides. This method achieves real-time dynamic monitoring of abnormal deformation in landslide disasters from deformation, accelerated deformation to instability and failure stages. This article proposes an algorithm that triggers acquisition by sensing abnormal deformation and adjusts the acquisition frequency autonomously. By embedding the algorithm into the monitoring system, real-time monitoring of abnormal event data of disaster bodies can be achieved. The system achieves low-power sleep monitoring mode by adjusting the working mode. This method provides a good real-time understanding of the dynamic evolution laws of landslide bodies and improves the effectiveness of disaster prevention and reduction.

Digital-Twin virtual model real-time construction via spatio-temporal cascade reconstruction for full-field plastic deformation monitoring in metal tube bending manufacturing

Digital Twin (DT) technology, which integrates multi-source information, is extensively applied for comprehensive monitoring, predicting, and optimizing manufacturing processes. The core of this technology is the Digital Twin Virtual Model (DTVM), which acts as a virtual mirror reflecting the real-world physical processes within a digital environment. In processes like tube bending, constructing a real-time DTVM capable of capturing full-field plastic deformation is essential for monitoring and analyzing plastic behavior. However, existing DTVMs often simplify spatial resolution and suffer from temporal delays, impeding the accurate real-time depiction of the complete state of the real physical processes. To address this issue, a real-time DTVM construction method based on spatio-temporal cascade reconstruction was proposed for full-field plastic deformation monitoring in metal tube bending. Initially, a joint-section driven predefined bending tube coordinate representation method was introduced to comprehensively capture the entire plastic deformation area in bending tubes. Subsequently, through a physics-derived model integrating limited real-time data and plastic forming theory, a low-fidelity model with complete but low accuracy was obtained. This model was subsequently refined into a high-fidelity model with both completeness and high accuracy using the proposed FPDR-Net. To eliminate temporal lags, the concept of compensation for time-delay through prediction was introduced. The newly developed TSCR-Net was applied to leverage past data to predict the present state, thereby achieving real-time synchronization mapping between the physical process and the DTVM. Finally, the proposed real-time reconstruction method for monitoring was validated through a case study on the bending of a 6061-T6 tube. The accuracy of full-field plastic deformation reconstruction was compared to traditional algorithms and finite element methods. The experimental results demonstrated that the proposed approach is highly efficient for real-time and full-field plastic deformation monitoring.

Evaluation of sensor-enabled piezoelectric geoelectric cable in cyclic shear tests of subgrade soil under vertical cyclic loads

Of late, deformation of subgrade soil has led to an increasing number of road subsidence diseases. Real-time monitoring of subgrade deformation is critical to ensure the safety of subgrade operations. In this paper, a sensor-enabled piezoelectric geoelectric cable (SPGC) with impedance strain effect and piezoelectric effect is tested. The SPGC impedance and voltage signals obtained by cyclic shear test under vertical static load and cyclic shear test under vertical cyclic load are used to evaluate the monitoring effect. The results showed that normal stress had the greatest effect on the shear strength of the soil, whereas the normal stress and horizontal shear displacement amplitude significantly influenced the strain in the soil. Varying the normal and horizontal shear frequencies had little effect on the shear strength and strain of the soil. The normalized impedance and voltage of the SPGC, respectively, decreased and increased rapidly during the initial stage of the shear cycle; these changes were relatively small during the middle and late stages of the shear cycle. The SPGC voltage waveform revealed the changes in the shear stress and vertical displacement under different normal and horizontal shear frequencies, from which the stability of the subgrade soil under the aforementioned conditions could be evaluated. The variations in the SPGC impedance and effective voltage from the cyclic shear tests under both vertical static and vertical cyclic loads remained essentially consistent with the number of cycles. However, there was a difference in that the trough of the SPGC impedance under the vertical cyclic load was larger than that under the vertical static load; likewise, the effective SPGC voltage under the cyclic load was larger than that under the static load. Through an analysis of the SPGC impedance and voltage signals in the subgrade soil, the consistency of the SPGC-normalized impedance and effective voltage with shear stress was clarified; this helped us evaluate the health of the subgrade and monitor the characteristics of the precursor signals before a slide were to occur, thereby affording us an opportunity to issue timely warnings.

Novel sensorized additive manufacturing-based enlighted tooling concepts for aeronautical parts

This paper presents lightweight tooling concepts based on additive manufacturing, with the aim of developing advanced tooling systems as well as installing sensors for real-time monitoring and control during the anchoring and manufacturing of aeronautical parts. Leveraging additive manufacturing techniques in the production of tooling yields benefits in manufacturing flexibility and material usage. These concepts transform traditional tooling systems into active, intelligent tools, improving the manufacturing process and part quality. Integrated sensors measure variables such as displacement, humidity and temperature allowing data analysis and correlation with process quality variables such as accuracy errors, tolerances achieved and surface finish. In addition to sensor integration, additive manufacturing by directed energy arc and wire deposition (DED-arc) has been selected for part manufacturing. The research includes the mechanical characterisation of the material and the microstructure of the material once manufactured by DED-arc. Design for additive manufacturing" principles guide the design process to effectively exploit the capabilities of DED-arc. These turrets, equipped with sensors, allow real-time monitoring and control of turret deformation during clamping and manufacturing of aeronautical parts. As a first step, deformation monitoring is carried out within the defined tolerance of ± 0.15, which allows a control point to be established in the turret. Future analysis of the sensor data will allow correlations with process quality variables to be established. Remarkably, the optimised version of the turret after applying DED technology weighed only 2.2 kg, significantly lighter than the original 6 kg version. Additive manufacturing and the use of lightweight structures for fixture fabrication, followed by the addition of sensors, provide valuable information and control, improving process efficiency and part quality. This research contributes to the development of intelligent and efficient tool systems for aeronautical applications.

Real-time shape sensing of large-scale honeycomb antennas with a displacement-gradient-based variable-size inverse finite element method

Real-time thermal deformation monitoring plays a crucial role in calibrating phase signals and maintaining satellite performance for large spaceborne antennas. The inverse finite element method (iFEM) represents a promising shape-sensing methodology applicable to monitor the three-dimensional displacement of structures through surface-measured strain. However, the high-accuracy reconstruction of large-scale structures involves a large number of inverse elements, which leads to the low level of computational efficiency. To address this issue, a displacement-gradient-based variable-size iFEM is proposed in this paper. According to the characteristics of deformation, this method optimizes the size of each inverse element to reduce the number of inverse elements required, which improves real-time performance and maintains the high accuracy of reconstruction. The real-time performance of the proposed method is verified by conducting a simulation test on a large-scale honeycomb antenna. According to the simulation results, the variable-size iFEM is applicable to achieve the same accuracy of reconstruction using fewer inverse elements than conventional iFEM. An experimental test is performed and the optimal variable-size discretization that balances accuracy and efficiency is determined. The results show that the maximum error of reconstructed displacement is less than 7.3 % and the reconstruction time is in no excess of 0.1 s.

Automated deformation detection and interpretation using InSAR data and a multi-task ViT model

Many geological hazards are associated with ground deformations. Prompt and accurate detection and interpretation of ground deformation is therefore vital to geohazard mitigation. Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) is an effective geodetic technique for monitoring ground deformation. However, accurate computation and interpretation of deformation using InSAR are often hindered by various errors and a lack of expert knowledge. We present a new advanced deep learning model based on a multi-task vision transformer (MT-ViT) to automatically detect, locate, and interpret deformation using single interferograms. To address the issue of limited training data in InSAR applications, the proposed model utilizes pre-trained weights from optical images and transfers them to a simulated InSAR dataset. Then real interferograms are used to fine-tune the weights in the network. An overall loss function is designed, which considers the classification and localization losses in the model. The effectiveness of the proposed model is demonstrated using both simulated and real InSAR datasets that contain either coseismic or volcanic deformation. The experimental results from the model are also compared with the state-of-the-art convolutional neural network (CNN) based techniques. The results show significant improvement in both the accuracy of the results and the computational efficiency over the CNN-based approaches. The MT-ViT model achieved 99.4 % classification accuracy, 54.1 % mean intersection over union (IOU), and 0.9 km localization accuracy. A comprehensive evaluation of the hyperparameters in training the MT-ViT model was carried out, which will inform future research in this direction. The research results highlight the promising capabilities of MT-ViT in near real-time deformation monitoring and automated deformation interpretation.

IoT-Based SHM Using Digital Twins for Interoperable and Scalable Decentralized Smart Sensing Systems

Advancing digitalization is reaching the realm of lightweight construction and structural–mechanical components. Through the synergistic combination of distributed sensors and intelligent evaluation algorithms, traditional structures evolve into smart sensing systems. In this context, Structural Health Monitoring (SHM) plays a key role in managing potential risks to human safety and environmental integrity due to structural failures by providing analysis, localization, and records of the structure’s loading and damaging conditions. The establishment of networks between sensors and data-processing units via Internet of Things (IoT) technologies is an elementary prerequisite for the integration of SHM into smart sensing systems. However, this integrating of SHM faces significant restrictions due to scalability challenges of smart sensing systems and IoT-specific issues, including communication security and interoperability. To address the issue, this paper presents a comprehensive methodological framework aimed at facilitating the scalable integration of objects ranging from components via systems to clusters into SHM systems. Furthermore, we detail a prototypical implementation of the conceptually developed framework, demonstrating a structural component and its corresponding Digital Twin. Here, real-time capable deformation and strain-based monitoring of the structure are achieved, showcasing the practical applicability of the proposed framework.

Real-time deformation monitoring of large diameter shield tunnel based on multi-sensor data fusion technique

The measurement of the shield tunnel deformation is an important tool to ensure structural integrity. The structural “deformation” of the tunnel is not merely a simple geometric deformation, which includes the elastic deformation of the segments and the rigid deformation caused by the opening and misalignment of the joints between the segments. Therefore, it is hard to monitor the deformation only relying on a single device. In this study, a data-fusion computing algorithm based on a fusion of rotation and strain measurements is proposed to improve the accuracy of tunnel deformation monitoring without the need of reference frame. SmartRock sensor was utilized to record rotation and the strain change of tunnel segments. Kalman filter was used as the fusion technique. The algorithm was successfully applied in the project of Jinan Yellow River tunnel, China. The result was compared with the result obtained by laser scanner and shows good consistency.

Monitoring Technology of Surrounding Rock Deformation Based on IWFBG Sensing Principle and Its Application

Abstract To address the challenge of achieving precise real-time monitoring of significant deformation in deep roadway surrounding rock, a quasi-distributed strain-sensing cable (SSC), which has a spatial resolution of 1 m, was developed based on the principle of Identical Weak Fiber Bragg Gratings. The performance of SSC has been evaluated through a series of calibration tests, revealing a range of 0%–3%, an accuracy level of 0.5%, a strain sensitivity measuring at 1.23 pm/με, and a temperature sensitivity recorded as 10.78 pm/℃. Furthermore, a real-time deformation monitoring system has been established to monitor rock deformation, consisting of SSC, supporting installation equipment, demodulation equipment, and monitoring software. Moreover, the proposed methodology was applied in the deep roadway of Guqiao coal mine. The results showed that the maximum surface displacement of the roadway is 103.47 mm, while the lateral contrast error stands at 5%. The maximum strain value of the surrounding rock measures 27,095 με. The depth of rock rupture zone is about 3 m, while the boundary of rock damage zone extends up to 6 m. This information serves as the foundation for determining the parameters of the roadway reinforcement support design.

In Situ Nanomechanics: Opportunities Based on Superplastic Nanomolding.

In situ nanomechanics, referring to the real-time monitoring of nanomechanical deformation during quantitative mechanical testing, is a key technology for understanding the physical and mechanical properties of nanoscale materials. This perspective reviews the progress of in situ nanomechanics from the aspects of preparation and testing of nanosamples, with a major focus on one-dimensional (1D) nanostructures and discussions of their challenges. We highlight the opportunities provided by in situ nanomechanics combined with the superplastic nanomolding technique, especially in the aspects of regulating physical and chemical properties which are highly exploitable for mechanoelectronics, mechanoluminescence, piezoelectronics, piezomagnetism, piezothermography, and mechanochemistry.

Ground Deformation Monitoring for Subway Structure Safety Based on GNSS

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.

A real-time spatial deformation estimation method based on spatial curvature decomposition and interpolation

Structural deformation entails safety risks, and at times, direct measurement can be challenging, especially when reference points are unavailable. Therefore, the development of a practical and precise deformation estimation method is crucial. This paper introduces a strain-based method with spatial curvature decomposition and interpolation for estimating structural deformation. The proposed method was tested through numerical simulations of various static and dynamic deformation cases for different structures, including a railway track, a rigid hanger of a bridge under low-frequency dynamic loads, and a cantilever beam under high-frequency dynamic loads. The feasibility of the method was verified, and it was further validated by a field test on a railway track. Compared to existing strain-based methods, it offers more flexible sensor placement, requires fewer parameters, and can estimate dynamic, static, planar, and spatial deformations. This strain-based method shows promise for real-time structural deformation monitoring, enhancing safety assessments.

Research on mechanical damage-energy evolution characteristics of coal based on digital image measurement technology

This study investigates the mechanical damage-energy evolution characteristics of coal using digital image measurement technology. Traditional measurement methods, such as strain gauges and extensometers, often suffer from limitations and inaccuracies in measuring coal deformation and damage. In contrast, digital image measurement technology offers a non-contact and non-interfering approach, providing high precision and real-time monitoring of coal deformation and strain changes. The study utilizes a tri-axial fluid–solid coupling device and non-contact digital image processing technology to measure the deformation of coal samples under different confining pressures. By analyzing the total input energy, dissipative energy, and releasable strain energy of coal, the research sheds light on the energy distribution and transformation during coal failure. The advantages of digital image measurement technology over traditional methods are highlighted, including its high precision, real-time monitoring capabilities, non-disturbing measurement approach, and cost-effectiveness. These advantages enhance the accuracy and efficiency of testing and monitoring coal samples, leading to improved coal mining practices and energy production processes. Overall, this study contributes to a better understanding of the mechanical damage-energy evolution characteristics of coal. The insights gained from digital image measurement technology provide valuable information for the prediction and prevention of geological disasters, the analysis of stability in coal mining operations, and the development of improved safety protocols. Further research is encouraged to explore additional factors and mechanisms that may influence coal damage and energy evolution, ensuring a comprehensive understanding of coal behavior.

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring.

The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of "zero mean" and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5-7%.

Physical Model Experiments on Failure Mechanism on Slopes of Weathered Basalt Soils during Heavy Rainfall Events.

Basalt platforms are widely distributed in many areas of China, where landslides occur frequently. It is well recognized that landslide hazards seriously threaten engineering constructions and property safety. It is, therefore, of great significance to understand deformation and failure behaviors and their mechanisms in basalt slopes to reduce the loss caused by landslides. In this work, the Pengshan Landslide in Zhejiang Province is taken as a prototype and slope model tests are carried out. During the tests, real-time monitoring of pore pressure, earth pressure and slope deformation is conducted. Based on the experimental data, the influence of rainfall intensity and the thickness of a weak interlayer on the slope stability are obtained. It is demonstrated that the rainfall and weak interlayer are the most important factors causing the slope instability of a basalt platform. Furthermore, damage from a basalt platform slope usually starts from local failure, and the slope foot is the most likely sliding part. Moreover, when the rainfall intensity is doubled, the initial deformation time of the slope is reduced by about half and the final failure time is advanced by one-third. In addition, when the thickness of the weak interlayer is doubled, the initial deformation time of slope is shortened by about half and the final failure time is advanced by one-quarter.

LZER0: A Cost-Effective Multi-Purpose GNSS Platform.

Recent advances in Global Navigation Satellite System (GNSS) technology have made low-cost sensors available to the mass market, opening up new opportunities for real-time ground deformation and structure monitoring. In this paper, we present a new product developed in this framework by the National Institute of Oceanography and Applied Geophysics-OGS in collaboration with a private company (SoluTOP SAS): a cost-effective, multi-purpose GNSS platform called LZER0, suitable not only for surveying measurements, but also for monitoring tasks. The LZER0 platform is a complete system that includes the GNSS equipment (M8T single-frequency model produced by u-blox) and the web portal where the results are displayed. The GNSS data are processed using the RTKLIB software package, and the processed results are made available to the end user. The relative positioning mode was adopted both with real-time and post-processing RTKLIB engines. We present three applications of LZER0-cadastral, monitoring, and automotive-which demonstrate that it is a flexible, multi-purpose platform that is easy to use in terms of both hardware and software, and can be easily deployed to perform various tasks in the research, educational, or professional sectors.

A Novel Low-Cost GNSS Solution for the Real-Time Deformation Monitoring of Cable Saddle Pushing: A Case Study of Guojiatuo Suspension Bridge

Extreme loadings, a hostile environment and dangerous operation lead to the unsafe state of bridges under construction, especially large-span bridges. Global Navigation Satellite Systems (GNSS) tend to be the best choice for real-time deformation monitoring due to the significant advantage of automation, continuation, all-weather operation and high precision. Unfortunately, the traditional geodetic GNSS instrument with its high price and large volume is limited in its applications. Hence, we design and develop low-cost GNSS equipment by simplifying the monitoring module. The performance of the proposed solution is evaluated through an experimental dynamic scenario, proving its ability to track abrupt deformation down to 3–5 mm. We take Chongqing Guojiatuo Suspension Bridge in China as a case study. We build a real-time low-cost GNSS monitoring cloud platform. The low-cost bridge GNSS monitoring stations are located at the top of the south and north towers, midspan upstream and downstream respectively and the reference station is located in the stable zone 400 m away from the bridge management buildings. We conducted a detailed experimental assessment of low-cost GNSS on 5 April and a real-time deformation detection experiment of the towers and main cables during the dynamic cable saddle pushing process on 26 February 2022. In the static experiment, the standard deviation of the residual using the multi-GNSS solution is 2 mm in the horizontal direction and 5 mm in the vertical direction. The multi-GNSS solution significantly outperforms the BDS/GPS single system. The dynamic experiment shows that, compared with the movement measured by the robotic total station, the horizontal error of the south tower and north tower measured by low-cost GNSS is below 0.005 m and 0.008 m respectively. This study highlights the potential of low-cost GNSS solutions for Structural Health Monitoring (SHM) applications.

Deformation Monitoring and Shape Reconstruction of Flexible Planer Structures Based on FBG

To reduce the dependence of real-time deformation monitoring and shape reconstruction of flexible planar structures on experience, mathematical models, specific structural curvature (shape) sensors, etc., we propose a reconstruction approach based on FBG and a data-driven model; with the aid of ANSYS finite element software, a simulation model was built, and training samples were collected. After the machine learning training, the mapping relationship was established, which is between the strain and the deformation variables (in three directions of the x-, y-, z-axis) of each point of the surface of the flexible planar structure. Four data-driven models were constructed (linear regression, regression tree, integrated tree, and BP neural network) and comprehensively evaluated; the predictive value of the BP neural network was closer to the true value (R2 = 0.9091/0.9979/0.9964). Finally, the replication experiment on the flexible planar structure specimen showed that the maximum predictive error in the x-, y-, and z-axis coordinates were 2.93%, 35.59%, and 16.21%, respectively. The predictive results are highly consistent with the expected results of flexible planar structure deformation monitoring and shape reconstruction in the existing test environment. The method provides a new high-precision method for the real-time monitoring and shape reconstruction of flexible planar structures.

Accelerating parallel digital image correlation computation with feature mesh interpolation

Parallel digital image correlation (DIC) computation has been increasingly used in DIC community due to the growing requirement on real-time deformation monitoring. In existing parallel DIC speeded up with feature-based initialization method, high parallelism requires more valid initialized points, which means more feature points are needed. However, more feature points significantly increase the time of feature extracting and matching, thus reducing the whole parallelism. To solve this problem, we propose to construct a mesh of matched feature points and assign initial values by mesh interpolation. The proposed method depends less on the feature distribution and provides more and better valid initialized points with much less feature points. Along with the well-designed parallel process, the proposed method is parallel-friendly, scalable and can realize high parallelism. The effectiveness, parallel performance, and the ability of real-time measurement of the proposed method are demonstrated by processing real experimental images.

Non-Measuring Camera Monitoring of Comprehensive Displacement of Simulated Slope Mass Based on Edge Extraction of Subpixel Ring Mark

Slope hazards threaten the safety of buildings and people’s lives and property. Real-time and dynamic monitoring of slope deformation by digital image monitoring technology is an effective method to prevent slope hazards. In this study, the Zhang Zhengyou calibration method is used to calibrate a non-metric digital camera, which is used to monitor the simulated slope with ring marks. The sub-pixel algorithm is used to identify the center coordinates of the landmarks. The proportional coefficient is obtained from the relationship between the landmarks and the actual distance. The change in displacement of the position of the digital camera is calculated in combination with the specific displacement value of the slope, yielding the rapid and accurate displacement trend of the slope. The outdoor experimental results show that the monitoring accuracy of this method can reach millimeter level, which can meet the demand of slope monitoring.