Real-time Displacement Research Articles

Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

Model predictive control with real-time variable weight for civil aircraft towing taxi-out control systems

Unlike the traditional method of taxying, where civil aircraft reach the runway by relying on their engines, the new mode of towing taxi-out may become preferred because of its lower energy consumption, lower emissions, and higher efficiency. To improve the tracking accuracy and lateral stability of the towing system, this study used a two-level time-varying weight control method (RMPC) based on model predictive control (MPC) and fuzzy control. The tractor speed and front wheel angle were set as the control variables in the MPC controller, and the real-time lateral displacement error and yaw angle error of the tractor were set as the fuzzy control inputs. The influence of the weight matrix on the accuracy and stability of path tracking was coordinated by online optimization of the MPC objective function weight. The simulations of multiple working conditions were performed using TruckSim software in Simulink, which showed that the proposed control method can limit the lateral displacement error of the tractor and aircraft within 0.4 m, which can be reduced by 70.83% and 77.4% compared with the single MPC, respectively. Additionally, the maximum yaw angle errors of the tractor and aircraft are reduced by 76.11% and 51.31% compared with the single MPC, respectively. Furthermore, the yaw angle error of tractors can be limited to 4° and that of aircraft can be limited to 6.5°, which is an improvement of the lateral stability and driving safety for the civil aircraft towing system. The new controller may provide technical insights and support for the practical development and safe application of the towing taxi-out mode.

LAVOLUTION: Tunable structured light for bridge displacement measurement

Assessing bridge conditions is critical, but field measurements often face challenges like sensor installation and accuracy requirements. This paper introduces a novel approach using structured light (SL) for non-target-based measurements, eliminating the need for physical markers. A tunable SL system, featuring four laser pointers aligned with a commercial camera, was developed to estimate the relative angle between the camera and the target structure, and to accurately calculate the scale factor. The proposed method involves three main processes: (1) laser alignment, (2) angle estimation, and (3) real-time displacement measurement. By precisely aligning the SL system and minimizing discrepancies between projected SL points and an ideal numerical model, the method achieves accurate results. Validation through numerical simulations, lab-scale tests, and full-scale bridge tests demonstrated a maximum error of 2.69% in scale and 2.53% in displacement, confirming the method’s effectiveness and applicability.

The Real-Time Detection of Vertical Displacements by Low-Cost GNSS Receivers Using Precise Point Positioning.

Vertical displacements are traditionally measured with precise levelling, which is inherently time consuming. Rapid or even real-time height determination can be achieved by the Global Navigation Satellite System (GNSS). Nevertheless, the accuracy of real-time GNSS positioning is limited, and the deployment of a network of continuously operating GNSS receivers is not cost effective unless low-cost GNSS receivers are considered. In this study, we examined the use of geodetic-grade and low-cost GNSS receivers for static and real-time GNSS levelling, respectively. The results of static GNSS levelling were processed in four different software programs or services. The largest differences for ellipsoidal/normal heights reached 0.054 m/0.055 m, 0.046 m/0.047 m, and 0.058 m/0.058 m for points WRO1, BM_ROOF, and BM_CP, respectively. In addition, the values depended on the software used and the location of the point. However, the multistage experiment was designed to analyze various strategies for GNSS data processing and to define a method for detecting vertical displacement in a time series of receiver coordinates. The developed method combined time differentiation of coordinates estimated for a single GNSS receiver using the Precise Point Positioning (PPP) technique and Butterworth filtering. It demonstrated the capability of real-time detection of six out of eight displacements in the range between 20 and 55 mm at the three-sigma level. The study showed the potential of low-cost GNSS receivers for real-time displacement detection, thereby suggesting their applicability to structural health monitoring, positioning, or early warning systems.

Investigation on the bending performance of sandwich beams with re-entrant diamond auxetic core

ABSTRACT Auxetic metamaterials, characterised by a negative Poisson’s ratio (NPR), exhibit unique mechanical properties where they expand under tension and contract under compression. These structures hold significant potential with applications spanning various industries such as biomedical, construction, sports, aerospace and defence. The re-entrant diamond auxetic metamaterial represents a modified unit cell structure designed to mitigate the bending-dominated failure observed in conventional re-entrant structures. Notably, this modification demonstrates advantages, particularly in uni-axial compression scenarios. However, its performance concerning energy absorption under bending loads has not been explored until now. This study examines the bending behaviour of a re-entrant diamond auxetic metamaterial, both as a core and in sandwich configurations. The samples are fabricated using additive manufacturing (3D printing) and compression moulded glass-epoxy composite skins are added to these cores. Experimental assessments are conducted to evaluate bending performance, facilitated by advanced digital image correlation (DIC) techniques for real-time displacement measurement on the sample surface. The results reveal remarkable 14, 11 and 4-times improvement in energy absorption, specific energy absorption and failure displacement in the sandwich construction compared to the core configuration. Additionally, the study investigates the impact of eccentric loading on the flexural performance of the re-entrant diamond auxetic metamaterial.

Ultrasonic Welding of Acrylonitrile-Butadiene-Styrene Thermoplastics without Energy Directors.

Ultrasonic welding (USW) of thermoplastics plays a significant role in the automobile industry. In this study, the effect of the welding time on the joint strength of ultrasonically welded acrylonitrile-butadiene-styrene (ABS) and the weld formation mechanism were investigated. The results showed that the peak load firstly increased to a maximum value of 3.4 kN and then dropped with further extension of the welding time, whereas the weld area increased continuously until reaching a plateau. The optimal welding variables for the USW of ABS were a welding time of 1.3 s with a welding pressure of 0.13 MPa. Interfacial failure and workpiece breakage were the main failure modes of the joints. The application of real-time horn displacement into a finite element model could improve the simulation accuracy of weld formation. The simulated results were close to the experimental results, and the welding process of the USW of ABS made with a 1.7 s welding time can be divided into five phases based on the amplitude and horn displacement change: weld initiation (Phase I), horn retraction (Phase II), melt-and-flow equilibrium (Phase III), horn indentation and squeeze out (Phase IV) and weld solidification (Phase V). Obvious pores emerged during Phase IV, owing to the thermal decomposition of the ABS. This study yielded a fundamental understanding of the USW of ABS and provides a theoretical basis and technological support for further application and promotion of other ultrasonically welded thermoplastic composites.

Real-time FPGA Implementation of an Optic Flow Algorithm Suitable for Background Oriented Schlieren Technique

Abstract To meet the demands of real-time displacement detection in embedded systems, a real-time optical flow calculation method suitable for background oriented schlieren (BOS) technology was transplanted and implemented on an Field Programmable Gate Array(FPGA) system. Firstly, through the analysis of different optical flow operation characteristics and the comparison of processing results in MATLAB with actual experimental data, an optical flow algorithm suitable for real-time processing of background shadow images was selected. Subsequently, by using hardware pipelining and multimodule parallel processing, the single-layer L&K optical flow algorithm was transplanted onto the FPGA and algorithm optimization was performed to achieve real-time optical flow calculation based on adjacent frame images. The key optimization techniques in this paper mainly include cleverly designing Double-Data-Rate Three Synchronous Dynamic Random Access Memory (DDR3) memory partitioning read and write logic to avoid image read and write conflicts; ensuring the timing alignment and data synchronization of consecutive frames by using burst read arbitration modules and two independent First In First Out (FIFO) buffers; and in optical flow calculation, constructing a 3×3 window using a shifting FIFO method to effectively save cache resources and improve computational speed. Experimental results show that the system can achieve real-time processing speeds of not less than 60fps for 1024×768 resolution images, meeting the requirements of practical applications.

Structural displacement measurement using deep optical flow and uncertainty analysis

Displacement serves as a crucial indicator in the field of structure health monitoring for assessing the condition of civil engineering structures. Computer vision is commonly employed for this purpose due to its cost-effectiveness and convenience. Traditional non-contact computer vision methods, such as optical flow, have been applied to obtain the displacement of bridges. However, real-time monitoring remains challenging due to the computational complexity involved. Therefore, optical flow based on deep learning (referred to as deep optical flow) has gained significant attention. Its main advantage lies in its ability to achieve real-time displacement monitoring, which is of great significance. Furthermore, the accuracy of deep optical flow is comparable to that of traditional optical flow methods for displacement measurement. In this paper, the deep optical flow, RAFT-GOCor, is proposed and applied to extract the displacement of structures, then Bayesian RAFT-GOCor based on Monte Carlo dropout is also presented and applied to analyze the uncertainty of the displacement. The algorithms are verified by laboratory simulated experiments and field bridge loading test. The results indicate that both deep optical flow and uncertainty analysis are feasible methods for measuring structural displacement.

Development and clinical evaluation of a real-time multiple cross displacement amplification assay for rapid and sensitive detection of Mycobacterium tuberculosis

Molecular techniques of nucleic acid testing recommended by the World Health Organization (WHO) for the Mycobacterium tuberculosis (MTB) detection were considered to have the potential access to the accurate tuberculosis (TB) notifications. In this study, a new method, which coupled real-time (rt) fluorescence technique with multiple cross displacement amplification (MCDA), was developed for the rapid, sensitive and specific detection of MTB (termed MTB-rt-MCDA). According to the principle of the rt-MCDA test, a set of ten primers were designed for the MCDA reaction, of which one was engineered with a restrictive endonuclease recognition site, a fluorophore and a quencher for achieving the real-time fluorescence detection. MTB-rt-MCDA test was conducted under the optimized conditions (67 °C, 40 min) on the real-time fluorescence platform. The MTB-rt-MCDA assay accurately identified the MTB strains with no cross reaction with other bacteria. The lowest detectable genomic DNA concentration of the MTB-rt-MCDA assay was 25 fg/μl. We employed the genomic DNA templates extracted from sputum of clinical cases for validating the practical applicability of this assay, and the detection power of the MTB-rt-MCDA assay was comparable to that of the Xpert method and MCDA-based biosensor detection and superior to smear microscope method. The complete process of the MTB-rt-MCDA assay, including rapid extraction of DNA and rt-MCDA test, takes less than 1 h. In conclusion, the presented MTB-rt-MCDA assay provided an effective and simple option for the rapid screening of MTB infection.

Design and Validation of a Stratified Shear Model Box for Seismic Response of a Sand-Blowing Reclamation Site

The global increase in building collapses and damage on soft-soil sites due to distant significant earthquakes poses similar challenges for sand-blowing reclamation (SBR) sites on soft-soil layers. This study was initiated to capture the vibration characteristics of the SBR sites and to provide fresh insights into their seismic responses. Initially, considering the heterogeneity and layered structure of soil at SBR sites, we developed a novel stratified shearing model box. This model box enables the simulation of the complex characteristics of soil layers at SBR sites under laboratory conditions, representing a significant innovation in this field. Subsequently, an innovative jack loading system was developed to apply active vertical pressure on the soil layer model, accelerating soil consolidation. Furthermore, a new data collection and analysis system was devised to monitor and record acceleration, pore water pressure, and displacement in real time during the experiments. To verify the model box’s accuracy and innovation, and to examine the seismic response of SBR sites under varying consolidation pressures, four vibration tests were conducted across different pressure gradients to analyze the model’s predominant period evolution due to consolidation pressures. The experimental results demonstrate that the model box accurately simulates the propagation of one-dimensional shear waves in soil layers under various consolidation pressures, with notable repeatability and reliability. Our experiments demonstrated that increasing consolidation pressure results in higher shear wave speeds in both sand and soft-soil layers, and shifts the site’s predominant period towards shorter durations. Concurrently, we established the relationship between the site’s predominant period and the input waves. This study opens new paths for further research into the dynamic response properties of SBR sites under diverse conditions through shaking-table tests.

Examination of load-deformation characteristics of long-span bridges in harsh natural environments based on real-time updating artificial neural network

Long-span bridges, often exposed to challenging harsh natural environments with severe weather conditions, necessitate real-time examination of load-deformation characteristics to ensure structural integrity and safety. Previous studies have primarily focused on investigating the causes of deformation in bridge structures under different single-load conditions during severe natural disasters, utilizing physics-based, mechanics-based, and data-driven methods. However, these methods cannot achieve fully achieve effective analysis of the real-time effects of multi-factor loads on bridge deformation, particularly in the presence of dynamic and simultaneous loads such as wind or temperature variations. A novel data-driven method is proposed based on a state-of-the-art real-time updating artificial neural networks (ANNs) algorithm to investigate the real-time coupling relationship between multi-loads and bridge deformation, enabling real-time prediction of bridge deformations. Additionally, the real-time characteristics between structural deformation and multi-loads are explained by incorporating SHapley Additive exPlanation (SHAP) in harsh natural environments. The proposed method has been validated on the 1,006-meter Forth Bridge in Scotland, showing high accuracy in real-time displacement prediction. The 9-day testing dataset demonstrated the R2 values for Y and Z direction deformations were found to be 0.98 and 0.87, respectively. The performance metrics for each day indicated that the majority of Y and Z direction deformations had R2 values exceeding 0.8, with RMSE and MAE values below 30 mm. The SHAP analysis revealed that an increase in wind speed leads to intensified Y direction deformation (larger SHAP values), while temperature has a significant impact on Z direction deformation (smaller SHAP values). Moreover, the weight influences of each load on the deformation are not fixed. The study's findings demonstrate that the proposed method enables accurate long-term prediction and assessment, allowing precise monitoring and prevention of abnormal risks in bridges under harsh environmental conditions.

A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images.

To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration. Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset. The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively. A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion. The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.

Real-time displacement monitoring using camera video records with camera motion correction

Structure from Motion (SfM) method can reconstruct the story drift ratio, but the camera motion produced by earthquakes reduces the measuring accuracy. This paper improves the applicability of the traditional method by correcting camera motion according to the identification of translation and rotation of camera. Experiments at different levels are designed to prove the proposed method. First, one set of experiments proves the motion correction method by the Single-degree-of-freedom (SDOF) system.The error of maximum response is 4.5% for the case with less rotation. As for the camera motion with larger rotation, the average error increases to 7.9%, which still meets the practical utilization. After that, the accuracy of using SfM method is confirmed by the Multi-degree-of-freedom (MDOF) system with the average error of 4.9%. This paper is expected to extend approaches for the application of the SfM method in the case of huge earthquakes.

Study of electromagnetic damping of an oscillating bar magnet and its energy budget

The oscillation of a bar magnet along the axis of a closed loop solenoid has been studied quantitatively using an ultrasonic sensor and Arduino microcontroller-based platform. Real-time displacement of the oscillatory magnet with and without the solenoid shows an additional damping effect due to electromagnetic induction in the presence of the closed-loop solenoid. Variation of the induced current in the solenoid circuit has been analyzed over several cycles by simultaneous recording of the voltage developed across two resistors using ‘analogread’ library function. Energy drained from the oscillator due to electromagnetic damping has been compared with the energy lost in Joule heating of the solenoid circuit. A phase plot for the underdamped oscillator was generated using the fitting parameter values of the curve fitted with the position vs. time data points on the plot. This portable and inexpensive microcontroller-based design is well suited for STEM education of high school and for sophomores in an undergraduate physics course.

Feasibility Study for Monitoring an Ultrasonic System Using Structurally Integrated Piezoceramics

This paper presents a new approach to monitoring ultrasonic systems using structurally integrated piezoceramics. These are integrated into the sonotrode at different points and with different orientations. The procedure for integrating the piezoceramics into the sonotrode and their performance is experimentally investigated. We examine whether the measured signal can be used to determine the optimal operating frequency of the ultrasonic system, if integrating several piezoceramics enables discernment of the current vibration shape, and if the piezoceramics can withstand the high strains caused by the vibrations in a frequency range of approximately 20–25 kHz. The signals from the piezoceramic sensors are compared to the real-time displacement at different points of the sonotrode using a 3D laser scanning vibrometer. To evaluate the performance of the sensors, different kinds of excitation of the ultrasonic system are chosen.

Four-dimensional analysis of aortic root motion in normal population using retrospective multiphase computed tomography.

Aortic root motion is suspected to contribute to proximal aortic dissection. While motion of the aorta in four dimensions can be traced with real-time imaging, displacement and rotation in quantitative terms remain unknown. The hypothesis was to show feasibility of quantification of three-dimensional aortic root motion from dynamic CT imaging. Dynamic CT images of 40 patients for coronary assessment were acquired using a dynamic protocol. Scans were ECG-triggered and segmented in 10 time-stepped phases (0-90%) per cardiac cycle. With identification of the sinotubular junction (STJ), a patient-specific co-ordinate system was created with the z-axis (out-of-plane) parallel to longitudinal direction. The left and right coronary ostia were traced at each time-step to quantify downward motion in reference to the STJ plane, motion within the STJ plane (in-plane), and the degree of rotation. Enrolled individuals had an age of 65 ± 12, and 14 were male (35%). The out-of-plane motion was recorded with the largest displacement of 10.26 ± 2.20 and 8.67 ± 1.69 mm referenced by left and right coronary ostia, respectively. The mean downward movement of aortic root was 9.13 ± 1.86 mm. The largest in-plane motion was recorded at 9.17 ± 2.33 mm and 6.51 ± 1.75 mm referenced by left and right coronary ostia, respectively. The largest STJ in-plane motion was 7.37 ± 1.96 mm, and rotation of the aortic root was 11.8 ± 4.60°. In vivo spatial and temporal displacement of the aortic root can be identified and quantified from multiphase ECG-gated contrast-enhanced CT images. Knowledge of normal 4D motion of the aortic root may help understand its biomechanical impact in patients with aortopathy and pre- and post-surgical or transcatheter aortic valve replacement.

Closed-Loop Optogenetic Perturbation of Macaque Oculomotor Cerebellum: Evidence for an Internal Saccade Model.

Internal models are essential for the production of accurate movements. The accuracy of saccadic eye movements is thought to be mediated by an internal model of oculomotor mechanics encoded in the cerebellum. The cerebellum may also be part of a feedback loop that predicts the displacement of the eyes and compares it to the desired displacement in real time to ensure that saccades land on target. To investigate the role of the cerebellum in these two aspects of saccade production, we delivered saccade-triggered light pulses to channelrhodopsin-2-expressing Purkinje cells in the oculomotor vermis (OMV) of two male macaque monkeys. Light pulses delivered during the acceleration phase of ipsiversive saccades slowed the deceleration phase. The long latency of these effects and their scaling with light pulse duration are consistent with an integration of neural signals at or downstream of the stimulation site. In contrast, light pulses delivered during contraversive saccades reduced saccade velocity at short latency and were followed by a compensatory reacceleration which caused gaze to land on or near the target. We conclude that the contribution of the OMV to saccade production depends on saccade direction; the ipsilateral OMV is part of a forward model that predicts eye displacement, whereas the contralateral OMV is part of an inverse model that creates the force required to move the eyes with optimal peak velocity for the intended displacement.

Smart Trash Bin to Prevent Animal Disturbance Using Raspberry Pi and Deep Learning

A trash can is an indispensable component of human daily life. It is ubiquitous and extremely useful for keeping the city clean. However, littering is a frequent and widespread problem. Not only do humans litter, but so do animals, particularly monkeys. Animals are constantly searching for food, and when we discard our food waste, they are attracted and leave their natural habitats. Following that, the user may forget to lock the trash can, which may end up in the hands of the animal. With the aid of a deep learning system that detects and identifies human subjects, a smart trash bin with an autonomous locking mechanism will be used in this project to reduce animal disturbance. In this paper, Raspberry-Pi 4 module is being used. Moreover, this article describes a human subject identification method and its real-time displacement using the OpenCV library of programming roles, which is primarily aimed at Raspberry Pi with camera module. The smart trash bin system helps to reduce animal disturbance and minimise littering.

Subregional analysis of joint stiffness facilitates insight into ligamentous laxity after ACL injury.

Purpose: Increased incidence of anterior cruciate ligament injuries has amplified the need for quantitative research in clinical and academic settings. We used a novel digital arthrometer to measure knee laxity in healthy people and patients with anterior cruciate ligament injuries. Changes in stiffness were also assessed to develop new indicators for detecting anterior cruciate ligament injury. The purpose of this study was to use arthrometer to measure the quantitative indicator of knee laxity, bringing clinicians a new perspective on how to identify injury to the ACL. Methods: In this cross-sectional study, anterior tibial displacement under continuous loading was measured using a novel digital arthrometer in 30 patients with unilateral anterior cruciate ligament injury and 30 healthy controls. Load-displacement curves were plotted, using real-time load and displacement changes. Stiffness was defined by the slope of the applied load to tibial displacement. Anterior tibial displacement and instantaneous stiffness values under different loads were compared. The restricting contribution of the anterior cruciate ligament transformed the displacement-stiffness curve from a sharp decrease to a stable increase, resulting in a minimum stiffness value. Using the minimum stiffness as the turning point, the load-displacement curve was divided into regions 1 and 2. The two regions' stiffness changes were compared. Based on the findings, receiver operating characteristic curves were plotted and the area under the curve was calculated to estimate the diagnostic accuracy. Results: Anterior tibial displacement was significantly greater in the anterior cruciate ligament injury group than in the controls under each 10-N increase load (p < 0.05). In the anterior cruciate ligament injury group, instantaneous stiffness was significantly lower on the injured side than on the healthy side (p < 0.05). In the two regions of the load-displacement curve, stiffness was significantly lower in the anterior cruciate ligament injury group than in the control group (all, p < 0.05). Receiver operating characteristic curves were plotted, using changes in stiffness under the two regions in both groups. Stiffness in region 2 had the largest area under the curve (0.94; 95% CI, 0.88-0.99). Using the cut-off value of 9.62N/mm to detect ACL injury, the sensitivity and specificity were 93% and 82%, respectively. Conclusion: Our investigation of ligament stiffness provides novel insights into the properties of knee laxity. Stiffness in the later stages of increased loading <9.62N/mm could be a valid indicator for identifying knee laxity.

Correction of Misalignment Errors in the Integrated GNSS and Accelerometer System for Structural Displacement Monitoring

Structural health monitoring (SHM) systems are widely deployed to monitor the dynamic behaviors of large civil infrastructures such as bridges and tall buildings. Global Navigation Satellite System- (GNSS-) based technologies are often a key component in such an SHM system considering the unique capability of GNSS in determining real-time displacements. GNSS often integrates with an accelerometer to achieve complementary advantages. However, due to the various error sources in GNSS measurements and accelerometer, accuracies of GNSS and accelerometer fusion results often cannot meet the requirements of SHM. We propose to integrate a multi-antenna GNSS and an accelerometer with an unscented multi-rate Kalman filter (UMRKF-MA) to correct the system misalignment errors between the sensors, aiming to produce a much more accurate real-time displacement measurement technology for monitoring large civil infrastructures. Extensive experiments with datasets gathered using a shaking table have indicated that the proposed method was able to improve the accuracy of real-time displacement measurements by up to about 40–65% compared to some existing approaches, and that a 1 mm level of real-time monitoring of displacements could be achieved with the method. The method has also been applied to process a dataset from a real-world long-span bridge when heavy vehicles passed through the bridge in a loading test and significantly improved results were obtained.