Displacement Monitoring Research Articles

Quaternion-Based Iterative Extended Kalman Filter for Sensor Fusion of Vision Sensor and IMU in 6-DOF Displacement Monitoring

As civil structures are exposed to various external loads, their periodic evaluation is paramount to ensure their safety. By estimating the 6-degree-of-freedom (DOF) displacement of structures, structural behavior can be monitored directly. Therefore, this study aims to develop a translational and rotational displacement estimation method by fusing a vision sensor and inertial measurement unit (IMU) using a quaternion-based iterative extended Kalman filter (QIEKF). The QIEKF algorithm was applied to reduce the nonlinear influence on the measurement model. The 6-DOF displacement is predicted using the integral of the gyroscope output and updated via a combination of an accelerometer and a magnetometer through a vector matching process in the Kalman filter framework. Subsequently, the 6-DOF displacement estimation result is updated through a vision sensor using a 2-D planar marker and homography transformation in the Kalman filter framework. The performance of the proposed sensor fusion method was verified with experiments using a motorized motion stage, and the results show that the displacements can be estimated with high accuracy regardless of measurement noise and slowly varying signal drift.

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

This paper reports the first investigation of the inverse finite element method (iFEM) coupled with the genetic algorithm (GA) to optimize sensor placement models of plate/shell structures for their real-time and full-field deformation reconstruction. The primary goal was to reduce the number of sensors in the iFEM models while maintaining the high accuracy of the displacement results. Here, GA was combined with the four-node quadrilateral inverse-shell elements (iQS4) as the genes inherited through generations to define the optimum positions of a specified number of sensors. Initially, displacement monitoring of various plates with different boundary conditions under concentrated and distributed static/dynamic loads was conducted to investigate the performance of the coupled iFEM-GA method. One of these case studies was repeated for different initial populations and densities of sensors to evaluate their influence on the accuracy of the results. The results of the iFEM-GA algorithm indicate that an adequate number of individuals is essential to be assigned as the initial population during the optimization process to ensure diversity for the reproduction of the optimized sensor placement models and prevent the local optimum. In addition, practical optimization constraints were applied for each plate case study to demonstrate the realistic applicability of the implemented method by placing the available sensors at feasible sites. The iFEM-GA method's capability in structural dynamics was also investigated by shape sensing the plate subjected to different dynamic loadings. Furthermore, a clamped stiffened plate and a curved shell were also considered to assess the applicability of the proposed method for the shape sensing of complex structures. Remarkably, the outcomes of the iFEM-GA approach with the reduced number of sensors agreed well with those of the full-sensor counterpart for all of the plate/shell case studies. Hence, this study reveals the superior performance of the iFEM-GA method as a viable sensor placement strategy for the accurate shape sensing of engineering structures with only a few sensors.

Selection and Optimization Mechanism of the Lower Return Roadway Layout in the near Residual Coal Pillar Area

Background: To optimize the layout position of the residual coal pillar return roadway when mining a close coal seam group and to clarify the optimization mechanism, a roadway optimization layout analysis was conducted on the Tashan coal mine. Methods: Surface displacement monitoring was conducted using field tests, and the main stress magnitude, plastic zone morphology, deformation variables, and connectivity between the plastic zone of the roadway and the plastic zone of the residual coal pillar were analyzed at different locations with the help of FLAC3D numerical simulation software. Results: It was found that, in the process of close coal seam group mining, the residual coal pillar of the overlying coal seam seriously affects the stress state and plastic zone distribution of the lower coal seam roadway. The roadway is arranged in a position that is relatively far away from the residual coal pillar, which could reduce the stress influence of the residual coal pillar on the roadway and guarantee the stability of the roadway. Conclusion: Since the Tashan Mine uses the top release method for mining, the stability of the roadway can be better ensured by placing the roadway in the middle and lower regions of the coal seam and using the layout method to retain small coal pillars.

Investigation on progressive failure process of tunnel lining induced by creep effect of surrounding rock: A case study

Creep of weak rock is an important and challenging consideration during operation of tunnels, since it may lead to failure of tunnel lining with the increase of pressure and the additional deformation. In this paper, the progressive failure process of tunnel lining induced by surrounding rock creep effect was studied through time-dependent numerical simulation. In this numerical analysis, the Nishihara model considering D-P yield criterion compiled by UMAT was used to characterize the creep characteristics of surrounding rock, and the creep mechanical parameters of surrounding rock in the section with serious tunnel lining diseases were obtained by triaxial creep tests. The concrete damaged plasticity (CDP) model was used to represent the damage characteristics of lining. Numerical simulation was verified by comparing with the tunnel displacement monitoring results. It is shown that long-term stability and progressive failure mode of tunnel lining are affected by the creep effect of weak rock. The stress combination dominated by thrust and bending moment in the first 30 years makes the lining concrete unable to maintain stability. The failure modes of lining were divided into early compression damage of arch crown and late tension damage of arch shoulder.

Dynamic Characteristic Monitoring of Wind Turbine Structure Using Smartphone and Optical Flow Method

The dynamic characteristics of existing wind turbine structures are usually monitored using contact sensors, which is not only expensive but also time-consuming and laborious to install. Recently, computer vision technology has developed rapidly, and monitoring methods based on cameras and UAVs (unmanned aerial vehicles) have been widely used. However, the high cost of UAVs and cameras make it difficult to widely use them. To address this problem, a target-free dynamic characteristic monitoring method for wind turbine structures using portable smartphone and optical flow method is proposed by combining optical flow method with robust corner feature extraction in ROI (region of interest). Firstly, the ROI region clipping technology is introduced after the structural vibration video shooting, and the threshold value is set in the ROI to obtain robust corner features. The sub-pixel displacement monitoring is realized by combining the optical flow method. Secondly, through three common smartphone shooting state to monitor the structural displacement, the method of high pass filtering combined with adaptive scaling factor is used to effectively eliminate the displacement drift caused by the two shooting states of standing and slightly walking, which can meet the requirements of structural dynamic characteristics monitoring. After that, the structural displacement is monitored by assembling the telephoto lens on the smartphone. The accuracy of displacement monitored by assembling the telephoto lens on the smartphone is investigated. Finally, the proposed monitoring method is verified by the shaking table test of the wind turbine structure. The results show that the optical flow method, combined with smartphones, can accurately identify the dynamic characteristics of the wind turbine structure, and the smartphone equipped with a telephoto lens is more conducive to achieving low-cost wind turbine structure dynamic characteristics monitoring. This research can provide a reference for evaluating the condition of wind turbine structures.

Multi-Instrumental Approach to Slope Failure Monitoring in a Landslide Susceptible Newly Built-Up Area: Topo-Geodetic Survey, UAV 3D Modelling and Ground-Penetrating Radar

Slope failures and landslides cause economic damage and deaths worldwide. These losses can be minimized by integrating different methodologies, instruments, and data monitoring to predict future landslides. In the constantly growing metropolitan area of Cluj-Napoca, Romania, changes in land cover, land use, and build-up areas are an issue. The unprecedented urban sprawl pushed the city limits from the Somes River to hilly terrain prone to landslides and erosion. This study focuses on a landslide-prone area where a previous slope failure caused significant economic losses. It combines topo-geodetic measurements, UAV monitoring of surface displacement, GIS spatial analysis, ground-penetrating radar investigations, and geotechnical assessment. Two years of data show that the slope is undergoing surface erosion, with soil displacements of a few centimeters. Geodetic monitoring of the retaining wall’s control points indicates a small rotation. Coupled with georadar profile imaging showing changes in soil and rock layers with an uplift trend, it was deduced that the area suffers from a global instability. The findings provide valuable information about the dynamics of landslides and erosion for forecasting future movements and developing preventative strategies based on a new methodology that combines affordable and prevalent instrumentation and techniques.

Application of ultra-weak FBG technology in real-time monitoring of landslide shear displacement

Application of ultra-weak FBG technology in real-time monitoring of landslide shear displacement

Typical advances of artificial intelligence in civil engineering

Artificial intelligence (AI) provides advanced mathematical frameworks and algorithms for further innovation and vitality of classical civil engineering (CE). Plenty of complex, time-consuming, and laborious workloads of design, construction, and inspection can be enhanced and upgraded by emerging AI techniques. In addition, many unsolved issues and unknown laws in the field of CE can be addressed and discovered by physical machine learning via merging the data paradigm with physical laws. Intelligent science and technology in CE profoundly promote the current level of informatization, digitalization, autonomation, and intellectualization. To this end, this paper provides a systematic review and summarizes the state-of-the-art progress of AI in CE for the entire life cycle of civil structures and infrastructure, including intelligent architectural design, intelligent structural health diagnosis, intelligent disaster prevention and reduction. A series of examples for intelligent architectural art shape design, structural topology optimization, computer-vision-based structural damage recognition, correlation-pattern-based structural condition assessment, machine-learning-enhanced reliability analysis, vision-based earthquake disaster evaluation, and dense displacement monitoring of structures under wind and earthquake, are given. Finally, the prospects of intelligent science and technology in future CE are discussed.

Range Extension in Coupling-Based Wireless Passive Displacement Sensors for Remote Structural Health Monitoring

Wireless monitoring of displacement between two points is demonstrated with an interrogation range ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${D}_{\boldsymbol {m}}$ </tex-math></inline-formula> ) which is dramatically larger compared with previous configurations based on near-field coupling. The sensing system comprises a transmitting antenna, a nested split-ring resonator (NSRR) sensor functioning as a tag, and a probe, called a “sniffer coil.” In classical configuration, the NSRR and the antenna constitute an electromagnetically coupled system which exhibits high sensitivity and resolution but is only functional within the near-field of the antenna, which in practice is much smaller than one wavelength, which limits the capabilities of the sensor. In this work, the system is separated into the TX and RX channels, which enables the transmitter antenna to be placed at a location which is several wavelengths away from the sensor. To remove the effect of clutter that disrupted the sensing signal at higher <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${D}_{\boldsymbol {m}}$ </tex-math></inline-formula> in previous works, the variation with frequency of the power level coupled to the sniffer coil is tracked instead of the variation in the scattering parameters with frequency. While extending <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${D}_{\boldsymbol {m}}$ </tex-math></inline-formula> , the high sensitivity and resolution are retained by the coupling between the NSRR and the sniffer coil. Wireless sensing of displacement is essential for several application areas, most specifically, structural health monitoring (SHM). The fact that the sensing system can work in far-field and that the NSRR is passive allows for remote and nondestructive testing (NDT) of damaged structures and buildings.

Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection

Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set.

Deformable organic semiconductor sensors based on metal free phthalocyanine for pressure and compressive displacement monitoring

Deformable organic semiconductor sensors based on metal free phthalocyanine for pressure and compressive displacement monitoring

Application of Fiber Optic Sensing System for Predicting Structural Displacement of a Joined-Wing Aircraft

This work aims to achieve real-time monitoring of strains and structural displacements for the target Joined-Wing aircraft. To this end, a Fiber Optic Sensing System (FOSS) is designed and deployed in the aircraft. The classical modal method, which is used for Strain-to-Displacement Transformation (SDT), is improved to adapt to different boundary conditions by introducing extra constraint equations. The method is first verified by numerical studies on a cantilever beam model and the high-fidelity finite element model of the Joined-Wing aircraft. Ground static tests are then carried out to further demonstrate the capability of the developed FOSS and SDT algorithm in practical application. The results have shown that the improved modal method is able to predict structural deformation under different boundary conditions by using only free–free modes. In addition, the errors between the predicted displacement and the reference in the ground test are within 10%, which proves the FOSS has reasonable accuracy and the potential for future flight tests.

Fiber-Optic Gyroscope Thermal Calibration through Two-Dimensional N-Order Polynomial for Landslide Displacement Monitoring

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring.

Investigation of structural responses and dynamic characteristics of a supertall building during Typhoon Kompasu

In this paper, comprehensive field measurements were conducted on a 600-m-high skyscraper in Shenzhen during Typhoon Kompasu based on structural health monitoring (SHM) system and a remotely non-contact interferometric radar system called IBIS-FS. The SHM system recorded wind field and structural responses, including accelerations at 10 building heights by accelerometers and horizontal displacements atop the building by global positioning system (GPS). Meanwhile, the IBIS-FS simultaneously measured the horizontal displacements of the building at multiple heights. This is the first time that the interferometric radar system, i.e., IBIS-FS, is applied to the displacement monitoring of a supertall building under typhoon conditions. The effectiveness of the IBIS-FS is verified through comparing the building responses measured by the IBIS-FS with those measured by the SHM system. Then, based on the field-measured responses, the mean, background, and resonant responses of the monitored supertall building are extracted and analyzed; and the amplitude dependencies and time-varying characteristics of the structural modal parameters (i.e., natural frequencies and damping ratios) are presented and discussed. The results show that the IBIS-FS can provide convenient and accurate displacement measurements of the skyscraper under severe weather conditions. This paper aims to provide useful information for SHM and wind-resistant design of supertall buildings in tropical cyclone-prone regions.

A Stepwise Discrimination Method of Multi-Index in Landslide Stability Monitoring

The deep displacement monitoring can directly reflect the deformation information inside the slope and can provide an important evidence for the identification of landslide stability. Based on the monitoring data on deep displacement of borehole, there are many monitoring indicators that can reflect the slope state. However, these indicators have their own applicability, and they are independent of each other, which cannot fully reflect the true movement state of the landslide. Therefore, it is necessary to establish a scientific method to give full play to the strengths of each monitoring index and improve the accuracy of identification for landslide stability. Here, the near-surface accumulative displacement, displacement rate, kinetic energy, and the rate of change of kinetic energy are selected as main monitoring indicators to establish a multi-index stepwise discrimination method for landslide stability in this study. This method analyzes the total deformation characteristics and movement trend of landslide from three aspects: surface displacement, internal displacement rate, and slope energy. Relied on the monitoring data on deep displacement of borehole of a landslide in Wushan County, Chongqing, China, this study finds that a single index is easily disturbed by external factors, causing the abnormal mutation in curve which affects analysis of landslide deformation. The variation characteristics of curve among multiple indexes can be mutually corroborated, effectively identify the abnormal fluctuation of the curve, and avoid the identification errors of landslide movement state. In addition, we also found that when there is an obvious sliding surface on the slope, the displacement rate curve will cluster at the sliding surface along the depth direction in this study. And this feature can be used as an important basis for the identification of sliding surface. The method proposed in this study can provide reference and suggestions for the actual treatment of landslide and monitoring data on deep displacement mining.

Cumulative displacement-based detection of damper malfunction in bridges using data-driven isolation forest algorithm

Long-span cable-supported bridges are flexible and prone to significant deformations under temperature, wind, and vehicle loads. Such large deformations induce frequent longitudinal movement at the end of the bridge girder, which can cause malfunction or failure in the components such as expansion joints, bearings, or longitudinal viscous dampers. This paper develops a condition assessment approach using the girder end displacement measurements, which primarily includes four steps, namely, data preprocessing, feature characterization, datasets preparation and splitting, and anomaly detection. The data preprocessing step considers the physics-based correlation between the temperature and displacement, which is used to pre-check noisy data issues in the displacement monitoring data. Subsequently, hourly cumulative displacement is calculated as the feature to feed into the machine learning model. The third step prepares the datasets and splits them into training and testing datasets. Then the isolation forest algorithm is implemented for anomaly detection with well-tuned parameters of the number of decision trees and the number of samples in each decision tree. Structural health monitoring data of a suspension bridge are collected to verify the proposed approach, and the detection targets are specified as viscous damper malfunction and a specific holiday period. The proposed approach demonstrates its ability to detect both events successfully, which can shed light on the predictive and preventive maintenance of the bridge.

GNSS-Based Dam Monitoring: The Application of a Statistical Approach for Time Series Analysis to a Case Study

Dams are one of the most important engineering works of the current human society, and it is crucial to monitor and obtain analytical data to log their conditions, predict their behavior and, eventually, receive early warnings for planning interventions and maintenance activities. In this context, GNSS-based point displacement monitoring is nowadays a consolidated technique that is able to provide daily millimeter level accuracy, even with less sophisticated and less expensive single-frequency equipment. If properly designed, daily records of such monitoring systems produce time series that, when long enough, allow for an accurate reconstruction of the geometrical deformation of the structure, thus guiding semi-automatic early warning systems. This paper focuses on the procedure for the GNSS time series processing with a statistical approach. In particular, real-world times series collected from a dam monitoring test case are processed as an example of data filtering. A remove–restore technique based on a collocation approach is applied here. Basically, it consists of an initial deterministic modeling by polynomials and periodical components through least squares adjustment and Fourier transform, respectively, followed by a stochastic modeling based on empirical covariance estimation and a collocation approach. Filtered time series are interpreted by autoregressive models based on environmental factors such as air or water temperature and reservoir water level. Spatial analysis is finally performed by computing correlations between displacements of the monitored points, as well as by visualizing the overall structure deformation in time. Results positively validate the proposed data processing workflow, providing useful hints for the implementation of automatic early warning systems in the framework of structural monitoring based on continuous displacement measurements.

Accelerated Movements of Xiaomojiu Landslide Observed with SBAS-InSAR and Three-Dimensional Measurements, Upper Jinsha River, Eastern Tibet

Many ancient landslides in the upper reaches of the Jinsha River seriously threaten the safety of residents on both sides of the river. The river erosion and groundwater infiltration have greatly reduced the stability of the ancient landslides along the Jinsha River and revived many large landslides. Studying their deformation characteristics and mechanisms and predicting possible failure processes are significant to the safety of residents and hydropower projects. We used SBAS-InSAR and three-dimensional decomposition techniques in our study. Our results showed that the trailing edge and middle part of the landslide have rapidly deformed. The maximum vertical annual displacement rate was 12 cm/a period from July 2017 to July 2019. Correlation analysis showed that creep deformation is closely related to the river damming of the Baige landslide events and that the rising river level was an important factor in the resurrection and accelerated destruction of the Xiaomojiu landslide. As a result, we predicted the possible failure process of the Xiaomojiu landslide, which might have lasted 80 s and eventually formed a landslide deposit with a height of about 150 m, a length of approximately 1500 m, and an average width of 450 m. Our results provide data references for displacement monitoring and instability risk simulation of large landslides along the Jinsha River.

Displacement Monitoring Requirements and Laser Displacement Monitoring Technology of Bridges with Short and Medium Spans

Short and medium-span bridges account for more than 90% of the bridges in China, and it is not suitable to use conventional health monitoring technology to monitor them. Based on an analysis of the proportion of live load, displacement under load, the action time of live load and other factors affecting short and medium-span bridges, this paper determines a feasible technical solution using mid-span deflection to monitor bridges with requirements including measuring range, resolution and accuracy. A set of wireless laser displacement monitoring technologies and equipment is studied by using the phase laser ranging method and the principle of pulsed laser ranging, and the reliability of the data is verified by experimental tests. Using both practical application cases and economic analysis, this paper proves that the technology has significant application advantages for short and medium-span bridges.

Study on Design and Deformation Law of Pile-Anchor Support System in Deep Foundation Pit

In this study, a deep foundation pit project of Nanlishi Road in the Xicheng District of Beijing was taken as the engineering background. Based on the monitoring method of that project and referring to its design scheme principle, this study applied advanced monitoring technology methods such as anchor axial force and deep horizontal displacement monitoring. The mechanism of pile–soil interaction, the stress change and deformation law of the three-pile and two-anchor support systems of deep foundation pits, and the stability of deep foundation pit support in an anhydrous sandy pebble stratum, were studied in depth. Results show: The axial force of the anchor rod had great loss in the early stage of prestressed tension locking; with the deepening of foundation pit excavation, the lateral pressure of stratum increased gradually, and the prestress of the anchor increased until the end of excavation, where it tended to be stable; the maximum horizontal displacement of the pile was smaller than the design value, and the maximum horizontal displacement was not at the top of the pile; the axial force of the prestressed anchor varied with the formation pressure and surrounding load; the tension of the lower anchor had a certain influence on the axial force of the upper anchor. Except for the east side of the foundation pit, the anchors of the first layer were all stabilized at about 140 kN, and the anchors of the second layer were stabilized at about 150 kN. The third row of anchors on the north side was stable at around 170 kN. By analyzing the variation law of stress and deformation of the supporting structure of the foundation pit, the timeliness of the data during the construction process was improved, and a reference is provided for the informatization construction of related working conditions.