Displacement Monitoring Research Articles

GNSS Real-Time Warning Technology for Expansive Soil Landslide—A Case in Ningming Demonstration Area

Efficient monitoring and early warning are the preconditions of realizing expansive soil landslide hazard prevention and control. Previous early warning of expansive soil landslides was evaluated through soil sampling experiments to analyze the stability coefficient. However, the existing methods lack timeliness and ignore the inconsistent deformation characteristics of different parts of the landslide mass. There are still difficulties in the dynamic numerical early warning of landslides at multiple points. Considering that the degradation of expansive soil landslides’ strength is directly reflected by surface displacement, for the Ningming expansive soil demonstration area and based on the GNSS shallow real-time displacement monitoring sequence, a landslide early-warning method based on the GNSS displacement rate combined with the GNSS displacement tangent angle model was proposed, and we thus designed early-warning thresholds for different warning levels. Combined with multi-source data such as soil moisture, soil pressure, and rainfall, the feasibility of accurate early warning of expansive soil landslides based on GNSS real-time surface displacement was verified. The proposed method does not require numerical calculation of internal stress and achieved two successful early warnings of landslides in the test area, which has a certain promotional value.

InterpolatiON of InSAR Time series for the dEtection of ground deforMatiOn eVEnts (ONtheMOVE): application to slow-moving landslides

The aim of this work is to develop an innovative methodology to analyse the time series (TS) of interferometric satellite data. TS are important tools for the ground displacement monitoring, mostly in areas in which in situ instruments are scarce. The proposed methodology allows to classify the trend of TS in three classes (uncorrelated, linear, non-linear) and to obtain the parameters of non-linear time series to characterise the magnitude and timing of changes of ground instabilities. These parameters are the beginning and end of the non-linear deformation break(s), the length of the event(s) in days, and the quantification of the cumulative displacement in mm. The methodology was tested on two Sentinel-1 datasets (2014–2020) covering the Alpine and Apennine sectors of the Piemonte region, an area prone to slow-moving slope instabilities. The results were validated at the basin scale (Pellice-Chisone and Piota basin) and at a local scale (Brenvetto, Champlas du Col and Casaleggio Boiro landslides) comparing with in situ monitoring system measurements, possible triggering factors (rainfall, snow) and already-collected events of the territory. The good correlation of the results has proven that the methodology can be a useful tool to local and regional authorities for risk planning and management of the area, also in terms of near real-time monitoring of the territory both at local and regional scale.

Bayesian incremental learning paradigm for online monitoring of dam behavior considering global uncertainty

Advanced data-driven methods for real-time monitoring of dam structural behavior, especially displacement, are obtaining increasing popularity. Most existing data-driven models are offline and static, however, which cannot be dynamically updated with the increase of monitoring data. Additionally, the capability of monitoring models to identify anomalies, which is the ultimate goal of dam behavior modeling, has received little attention. This study proposes a novel Extreme Learning Machine (ELM)-based Bayesian incremental learning methodology to address these challenges. The proposed model is termed Incremental Global Bayesian ELM (I-GBELM), which couples ELM with global Bayesian and incremental learning for online monitoring of dam displacements. Firstly, a global Bayesian approach to the ELM is presented to account for both input and target uncertainties and to infer the network parameters analytically. Secondly, an incremental learning paradigm is theoretically derived, which aims to directly learn new knowledge from additional data to update model parameters without retraining from scratch. The I-GBELM model integrates the strengths of both methods so that it allows accurately predicting displacements, identifying anomalies, and efficiently updating parameters. For online deployment purposes, finally, a non-iterative pruning algorithm is developed to adaptively discard the redundant hidden nodes, yielding a more compact model. The performance of the proposed model is fully validated through comprehensive verification cases using benchmark and real-world monitoring datasets. The empirical results indicate that the proposed I-GBELM outperforms all other models in most cases, and hence, is a competitive modeling tool for online structural health monitoring.

Real-time high-precision landslide displacement monitoring based on a GNSS CORS network

GNSS technology is an effective all-weather, continuous and real-time method of detecting three-dimensional landslide displacement. However, current positioning methods such as the network real-time kinematic (RTK) technique based on virtual reference station (VRS) technology have some limitations, particularly a sensitivity to atmospheric modeling error. In this study, taking advantage of existing GNSS continuously operating reference stations (CORS), we propose a network RTK algorithm (virtual atmosphere constraint (VAC)) for high-precision landslide monitoring. Network modeling atmosphere delays were used as virtual observations for the terminal position solution, by which their accuracy information can be considered. Additionally, a noise reduction method that combines complementary ensemble empirical mode decomposition and median filtering was proposed to reduce GNSS signal residual errors in the original RTK coordinate time series. To validate the effectiveness of the algorithm, we processed one week of data from two monitoring receivers on the Heifangtai landslide and four surrounding CORS using the VAC and VRS methods. Results show that the original displacement time series processed with VRS was prone to jitter, whereas the VAC method exhibited better accuracy, stability, and ambiguity fixing rates. Horizontal and vertical RMS errors at a stable monitoring point were 1.7 cm and 3.9 cm for VRS-RTK and 0.9 cm and 2.3 cm for VAC-RTK; thus, VAC reduced the error by 40.1% and 20.5%, respectively. After noise reduction, the RMS values of the VAC method at the stable and the other collapsed monitoring point were both reduced to an mm-level accuracy.

Formation and Failure Characteristics of a Landslide Induced by Excavation in Western Henan, China

Engineering construction in mountainous areas is a key factor inducing slope failure, which poses severe threats to life and property safety during construction and operation. Thus, clear understanding of slope structure and potential failure mechanisms is of great importance for slope reinforcement. The Shangge landslide, located in a mountainous area of Western Henan, China, was triggered by excavation along a toll station. The slope was still unstable after a row of anti-sliding piles were constructed. To clarify the deformation characteristics and failure mechanism of this landslide and provide reinforcement guidance for slopes with similar geological conditions, a combined method of field investigation, displacement monitoring, and numerical simulation was employed. The results indicate that the natural slope comprises a dual structure of soil and rock, and that the soil, with poor permeability and high water moisture, was prone to form a potential sliding surface. Secondly, slope excavation was the direct triggering factor. Lastly, but most importantly, the preliminary investigation work failed to accurately expose the potential position of the sliding surface, and the existing anti-slide piles failed to cross the potential sliding surface, which made them unable to effectively improve slope stability. The numerical simulation verified that the recommended piles installed on the platform of level 2 could significantly improve slope stability. Therefore, when carrying out engineering slope design in similar geological environments, the slope structure and material composition should be fully identified, and the influence of groundwater on the slope stability state should be emphasized. These results can provide a reference for similar slope reinforcement designs.

Spatial association-considered real-time risk rate assessment of high arch dams using observed displacement and combination prediction model

Structural health monitoring is an important approach to diagnose dam safety. The main purpose of this paper is to improve the accuracy of displacement prediction and real-time risk rate assessment of high arch dams by utilizing the deformation spatial association of multiple displacement monitoring points. Two strategies are integrated for the former. First, in addition to the traditional objective of minimizing the fitting error, deformation spatial association is also determined as an optimization objective for machine learning models. Second, five frequently used models are adopted as sub-models, and the overfitting degree is then used as an evaluation index to select optimal sub-models that included in the combination prediction model. On this basis, probability density analysis is conducted for displacement residuals, and a quantification function is defined to assess the real-time risk rate of a single monitoring point. The real-time risk rate of a high arch dam is then calculated according to the risk rates of multiple monitoring points, during which the spatial association are taken into account by the Copula function. Research results of the Jinping-I arch dam indicate that the deformation spatial association is very beneficial for improving the accuracy of dam safety diagnosis, where the prediction mean square error of four machine learning models decreases with an average rate of 66.92%, 45.37%, 35.45% and 73.12%, respectively, and the combination prediction method can further improve the displacement prediction accuracy by 23.79%. The observation data-based high-risk period of arch dams mainly occurs in operation scenarios that the rising or dropping rate of reservoir water level changes sharply. The joint risk rate can effectively avoid the accidental influence of a single monitoring point, and it exhibits an exponential decrease with the increasing number of used monitoring points. For the Jinping-I arch dam, a minimum number of 10 monitoring points is required.

Vision and Vibration Data Fusion-Based Structural Dynamic Displacement Measurement with Test Validation.

The dynamic measurement and identification of structural deformation are essential for structural health monitoring. Traditional contact-type displacement monitoring inevitably requires the arrangement of measurement points on physical structures and the setting of stable reference systems, which limits the application of dynamic displacement measurement of structures in practice. Computer vision-based structural displacement monitoring has the characteristics of non-contact measurement, simple installation, and relatively low cost. However, the existing displacement identification methods are still influenced by lighting conditions, image resolution, and shooting-rate, which limits engineering applications. This paper presents a data fusion method for contact acceleration monitoring and non-contact displacement recognition, utilizing the high dynamic sampling rate of traditional contact acceleration sensors. It establishes and validates an accurate estimation method for dynamic deformation states. The structural displacement is obtained by combining an improved KLT algorithm and asynchronous multi-rate Kalman filtering. The results show that the presented method can help improve the displacement sampling rate and collect high-frequency vibration information compared with only the vision measurement technique. The normalized root mean square error is less than 2% for the proposed method.

MPG-NET: A low-cost, multi-purpose GNSS co-location station network for environmental monitoring

Global Navigation Satellite Systems (GNSS) are very versatile sensors, which can be used for a variety of commercial and scientific applications. This holds especially true for different fields of remote sensing, such as atmospheric sounding or soil moisture monitoring. With the advent of low-cost dual-frequency GNSS equipment, certain applications are no longer restricted to the use of geodetic-grade instrumentation and can fully take leverage of the measurements in a second frequency band. In view of these emerging benefits, this study introduces the development and deployment of a multi-purpose GNSS station network in the Swiss Alps, called MPG-NET. We discuss the technical details of the station setup, in terms of GNSS hardware and technical design, as well as the quality of derived GNSS remote sensing products. In particular, our analyses focus on the quality of derived time series of zenith total delays (ZTD) and volumetric soil moisture content. Products are validated against benchmark data obtained from numerical weather models and in-situ sensors. For a prototype station, the results show a good agreement with the baseline, with errors of few millimeters for ZTD, and a remarkably high correlation for soil moisture content. Beside the documented value of low-cost GNSS for displacement monitoring (such as landslides or strong earthquakes), these findings are another step towards the establishment of a dense high-precision, multi-purpose GNSS network that comes at a very affordable price.

Continuous bridge displacement estimation using millimeter-wave radar, strain gauge and accelerometer

Monitoring of bridge displacement is essential for providing critical information regarding the health of bridge structures. However, continuous and accurate monitoring of structural displacement remains challenging. This study proposes a bridge displacement estimation technique that combines an accelerometer, a strain gauge, and a millimeter-wave radar considering intermittent radar target occlusion common in long-term displacement monitoring. The technique primarily estimates displacement using radar and accelerometer measurements but switches to using strain and acceleration measurements when radar targets are occluded. A radar target occlusion detection algorithm is developed to automatically achieve this switching. Automated initial calibration is performed to select suitable targets from all radar-detected targets and estimate the conversion factor for converting radar-based displacement from the line-of-sight direction to the actual movement direction. An artificial neural network model is automatically trained for strain–displacement transformation without the need for any pre-knowledge of a target structure. The proposed technique was validated via a laboratory test on a 10-m-long beam-type structure and a field test on a pedestrian steel box-girder bridge. The proposed technique correctly detected the intermittent radar target occlusion, and continuously estimated displacements for up to 75 min. The intermittent radar target occlusion leads to errors of a few millimeters in the displacements estimated using radar and accelerometers. However, the proposed method accurately estimates displacements with errors of less than 0.1 mm.

AB-452654-1 ESOPHAGEAL PROTECTION STRATEGIES FOR ABLATION OF ATRIAL FIBRILLATION: COMPARATIVE RESULTS BASED ON CONSECUTIVE ENDOSCOPIC EVALUATION

Catheter ablation is an effective treatment for restoring sinus rhythm in AF and has become a worldwide procedure. Although rare, atrioesophageal fistula (AEF) is among the most common lethal complications of this procedure. Several strategies have been employed to prevent esophageal lesions, including single and multi-sensor esophageal probes, different energy strategies, esophageal displacement and regular esophageal monitoring using esophagogastroduodenoscopy (EGD) to identify asymptomatic esophageal lesions that could evolve to AEF. As the occurrence of AEF is rare, one of the approaches to evaluate which strategy is more effective in preventing esophageal lesions is studying the incidence of asymptomatic on routine endoscopy. The objective of this study is to present the five-year experience of a systematic endoscopic surveillance of esophageal injury after AF catheter ablation and analyse if there is an appropriate strategy related to a lower incidence of lesions. This is a retrospective single center registry of all AF ablation procedures performed from 2016 to 2021, in which an endoscopy was performed after ablation in all cases. A total of 660 AF ablation procedures in 626 different patients were performed in that period. A control EGD data could be recorded in 609 ablation procedures (92,3%). Most of the patients were male with paroxysmal AF. Esophageal temperature monitoring with single Sensor Probe was performed in 48.4% and with multi–sensor probe in 24.4% of the cases. Most of the patients presented no esophageal lesions (71.8%). Mild lesion was found on 90 patients (14.8%) and severe lesion was found on 56 (9.2%) of them. The use of multisensor esophageal probe (P<0.001), male gender (P=0.007) and the use of PVAC (P=0.018) were correlated with less frequent thermal lesions on multivariate analysis. Most of the lesions were healed on control endoscopy. The incidence of esophageal lesions on routine EGD after AF catheter ablation is high, but most of the cases the lesion is spontaneously healed. The use of multisensor probe, PVAC and male gender were correlated to fewer thermal lesions.

Factors affecting the lateral contribution of a raft in a piled raft system

Contribution of the raft to lateral loads in a piled raft foundation system can be influenced by various factors including vertical contact pressure, number of piles, pile length, and spacing to diameter s/d ratios. Design of the piled raft system is highly dependent upon the contribution of the raft which should be thoroughly investigated. In this article, an experimental and numerical investigation was conducted in order to assess the effects of various parameters on contribution of raft to lateral loads. In experimental approach, small-scale models of the aluminium raft and galvanized iron (GI) hollow piles were used for testing under vertical and lateral loads. Vertical and lateral load cells on piles were installed for real-time load monitoring and LVDTs were used for displacement monitoring. In numerical approach, finite element software PLAXIS 3D was used. A raft was defined as a plate element, while piles were modelled as embedded beams. Based on experimental and numerical investigations, it was found that vertical contact of raft with soil has a direct relationship lateral raft contribution. Because, due to vertical load, the firm contact between the raft and the soil develops, resulting in a significant lateral contribution. Depending on the raft’s vertical contribution, the lateral contribution of the raft was found to be ranges from 14% to 55%. The numerical results were found fairly close to the experimental results. As a result, PLAXIS 3D was used to conduct a parametric analysis and It was determined that vertical pressure was directly related while the other three parameters were inversely related to the lateral raft resistance.

Displacement Monitoring of a Bridge Based on BDS Measurement by CEEMDAN-Adaptive Threshold Wavelet Method.

From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data processing method of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), combined with adaptive threshold wavelet de-noising is proposed. The adaptive threshold wavelet filtering method composed of the mean and variance of wavelet coefficients of each layer is used to de-noise the BDS displacement monitoring data. CEEMDAN was used to decompose the displacement response data of the bridge to obtain the intrinsic mode function (IMF). Correlation coefficients were used to distinguish the noisy component from the effective component, and the adaptive threshold wavelet de-noising occurred on the noisy component. Finally, all IMF were restructured. The simulation experiment and the BDS displacement monitoring data of Nanmao Bridge were verified. The results demonstrated that the proposed method could effectively suppress random noise and multipath noise, and effectively obtain the real response of bridge displacement.

Application of vibration compensation based on image processing in track displacement monitoring

Abstract The track state detection is of great significance to timely understand the operation state of track and find track defects and prevent operation accidents. This article initially analyzes the key technologies of track detection system and then proposes an image detection technology and image processing method for analyzing track detection at home and abroad, thus putting forward the scheme of track detection using image processing. The characteristics of onsite track images are analyzed, and a track state detection system based on track image preprocessing, image position correction, image defect comparison, and track section size measurement is designed in this article. Further in this article, a study of image linear transformation, noise filtering, defect recognition, and edge detection in track image processing is applied. Furthermore, a robust piecewise linear transformation is designed using the combination of image threshold transformation and image gray transformation. It reduces the loss of detailed information in the process of image processing. The center point of track bright band is determined by the image region segmentation method, which effectively reduces the error of image track measurement and improves the measurement accuracy.

Time Series Prediction Model of Landslide Displacement Using Mean-Based Low-Rank Autoregressive Tensor Completion

Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the superimposed economic development level exacerbates disaster losses, emphasizing the importance of landslide prediction. The collection of landslide monitoring data is the foundation of landslide displacement prediction, but the lack of various data severely limits the effectiveness of the landslide monitoring system. To address the issue of missing data during the landslide monitoring process, this paper proposes a time series prediction model of landslide displacement using mean-based low-rank autoregressive tensor completion (MLATC). Firstly, the reasons for the missing data of landslide displacement are analyzed, and the corresponding dataset of missing data is designed. Then, according to the characteristics and internal correlation of landslide displacement monitoring data, the establishment process of mean-based low-rank tensor completion prediction model is introduced. Finally, the proposed method is used to complete and predict the missing data for the random missing and non-random missing landslide displacement. The results show that the data completion and prediction results of the model are essentially consistent with the original displacement monitoring data of the landslide, and the accuracy and precision are relatively high. It shows that the model has good landslide displacement completion and prediction effects, which can provide a certain reference value for the missing data processing and landslide displacement prediction.

Numerical Simulation of Flood Control Levee Deformation by Shield Tunnel Excavation through the Liuyang River: A Case Study

The interval section of the Liuyang River flood control levee project of the Changsha Metro Line 6 is used as the engineering background of this study. A three-dimensional finite element numerical model of a tunnel shield containing complex interfaces is established by using the multifield coupling software COMSOL. The paper studied the deformation of flood control levees under shield tunnel excavation working conditions. The results show that when the left line shield machine is excavated below the Liuyang River flood control levee, the deformation of the flood control levee and the surrounding rock of the tunnel is biased towards the built right line tunnel. And it has an impact on the bridge pile near the tunnel. When the shield of the left line crosses the flood control levee, it can easily cause a large deformation and displacement of the levee above the area between the two shields. To ensure the controlled deformation of the levee, the construction should ensure the spacing between the two tunnel palm faces as far as possible and should be far away from other structures on the levee. When the shield crosses the levee, the deformation that occurs at the base of the levee is significantly higher than the deformations that occur at other locations of the levee. Displacement monitoring and secondary reinforcement at the base of the flood control levee appear to be necessary. The numerical simulation results validate the feasibility of using the multifield coupling software COMSOL to study the construction modeling of shield tunnels through rivers and its advantages. This method provides a practical framework for similar tunnel performance engineering or displacement monitoring in future projects.

Vibration and Displacement Monitoring of Surface and Rock Strata in Geological Hazards Based on Mixed Technologies

With the rapid development through the advancement of technology, China geological hazard monitoring system has made great progress in engineering application. Advanced geological technology on hazardous geological monitoring is emerging, Internet of Things (IOTs), Geographic Information System (GIS), and Remote Sensing (RS), for example. However, there are still many problems existing subjected to monitoring system, such as sensor power storage, stability, compatibility, and weak analysis ability, which hinders the further development and popularization of the automatic monitoring system on geological hazards. In this paper, a new buried rock sensor and a fixed inclinometer are adopted, combined with the IOTs and the Beidou navigation system, to build an automatic monitoring system for geological disasters; it not only improves the accuracy and timeliness of monitoring data of surface and rock strata vibration and displacement of geological disasters but also provides guidance for future system design and development, especially in remote areas with steep terrain.

InSARTrac Field Tests—Combining Computer Vision and Terrestrial InSAR for 3D Displacement Monitoring

InSARTrac is an innovative method for 3D displacement monitoring that combines terrestrial interferometric synthetic aperture radar (InSAR) and computer vision-based feature tracking. The 3D measurements obtained are considered far superior to 1D or 2D data and facilitate evaluations concerning the mechanisms controlling kinematics. This study presents the results of InSARTrac measurements at the Mölltal Glacier in Carinthia, Austria. The duration of glacier monitoring was four weeks and involved two instrument setup positions to obtain comparative measurements of supraglacial rock debris from different angles without utilizing retroreflectors. The mean displacement rate of the resultant vector is 22 mm/day and includes ~11 mm/day in the downgradient ice surface direction and 6 to 18 mm/day vertically downward. Additionally, the entire glacier surface was measured three times using a LIDAR-equipped UAV, revealing mean vertical displacements of 16 mm/day. The measurements indicate an InSARTrac accuracy of 4.2 ppm, which is 27% lower than in the initial controlled tests utilizing retroreflectors. The field test demonstrates the capability of InSARTrac to provide meaningful 3D displacement measurements of supraglacial rock debris. The material monitored has texture and reflectivity similar to certain classes of landslides, rock glaciers, and other alpine processes, indicating that InSARTrac has promising applications for monitoring a variety of geologic phenomena.

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation.

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.

Landslide surface horizontal displacement monitoring based on image recognition technology and computer vision

The deformation and displacement of landslides has always been the priority of landslide monitoring and early warning. With the development of image processing technology in recent years, the analysis of landslide surface deformation through image data has become a popular method and different methods of image analysis are rapidly increasing. However, the current method of analyzing landslide deformation and displacement using a single image processing method tends to analyze the displacement of the local deformation area of the landslide, which lacks overall monitoring of the slope and is difficult to use for pre-disaster monitoring of landslide hazards, easily leading to missed detection of landslide potential hazard points. Therefore, this paper combines image recognition technology and computer vision (IR-CV) to propose a method for monitoring and identifying the deformation displacement of landslide surfaces, which has been validated by indoor landslide model tests and unmanned aerial vehicle field simulation displacement recognition tests. In the model tests, the IR-CV method successfully identified the displacements generated during the deformation and damage of the landslide model while using the model deformation time series image data recorded by the camera to analyze the overall deformation of the surface of the landslide model. Further, in this paper, a field simulation displacement identification test was conducted using an unmanned aerial vehicle (UAV), and the orthophoto data obtained by the UAV verified that the IR-CV method can be used for overall deformation displacement identification of landslides in the field. The results of indoor model tests and field UAV simulation tests show that the monitoring method of IR-CV can effectively identify the deformation and displacement of landslide surfaces, which has good application prospects in pre-disaster monitoring and displacement identification of landslides, thus providing a cost-effective and feasible solution for monitoring the deformation and displacement of landslide surfaces.

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

Computer vision is becoming one of the most popular remote-sensing techniques and has been used widely in displacement monitoring and damage identification of in-service bridges. Nevertheless, several obstacles, including limited sampling rate, insufficient resolution for remote measurement, and error induced by camera tilting, restrict the application of vision-based approaches in structural health monitoring (SHM). The combination of a traditional SHM system and a modern remote-sensing technique can significantly improve the accuracy and reliability of the monitoring system. To make full use of data collected in the traditional SHM system and computer vision technique and overcome their shortcomings, we presented an improved bridge displacement estimation approach for SHM purposes by fusing camera-based and acceleration measurements. First, we estimated the scaling factor, which transfers pixel displacement to real displacement under tilt photogrammetry, by the acceleration reconstructed and camera-based displacements in the same frequency band without the actual size of the structure or the measurement parameters. Then, we extracted the low-frequency displacement from the vision-based measurement, and we fused the high-frequency displacement that was reconstructed from the acceleration measurement to achieve high-accuracy displacement estimation. The efficiency of this method was validated through dynamic load tests on a suspension model bridge in the laboratory and field tests on a highway and subway cable-stayed bridge.