Publications — Junhwa Lee, RACE Lab (Robotics & AI for Civil Engineering Lab)

Junhwa Lee, Department of Civil Engineering, Pukyong National University. Innovating infrastructure construction and maintenance through robotics, AI, digital twin, and smart sensing technologies.

Journal papers (29)

1. Probabilistic 3D position and displacement estimation of measurement points on civil structures based on stereo vision

   Gamin Lee and Junhwa Lee. Journal of the Korea Institute for Structural Maintenance and Inspection, 30(4), pp.26-37, 2026. doi:10.11112/jksmi.2026.30.4.26

   This study proposes a probabilistic stereo-vision framework that propagates matching uncertainty to 3D position covariance and displacement covariance for civil-structure monitoring points. Per-point pixel covariance is assigned from the curvature of the zero-mean normalized cross-correlation (ZNCC) peak, and the Jacobian of closest-ray midpoint reconstruction is used for first-order error propagation. The reconstructed point and the displacement are expressed as random variables and visualized as covariance ellipsoids. Simulations confirm agreement between the analytical and Monte Carlo covariances. A field experiment on a pedestrian suspension footbridge visualizes the tracked cable-intersection points, their covariance ellipsoids, and representative displacement traces, showing how the resolvability differs between near and mid-range points. The framework therefore reports not only displacement estimates but also point-wise measurement reliability.

2. Long-term displacement measurement of bridge structures using a total station

   Kyungwon Kim, Sung-Han Sim, Young-Joo Lee, Junhwa Lee, and Seungjun Lee. Journal of the Korea Institute for Structural Maintenance and Inspection, 30(3), pp.87-98, 2026. doi:10.11112/jksmi.2026.30.3.87

   Accurately measuring the long-term displacement trend of bridges during and after construction is essential for structural safety assessment. Conventional total-station-based displacement measurement requires the instrument to remain fixed for long periods, and self-motion compensation using a single fixed-point reflector has inherent limitations. This study proposes a method that places four reflectors at the outer corners and one at the center of the bridge underside, defines a base plane from the four outer points, and computes the reference-plane-based camber of the deck as the signed perpendicular distance from the center reflector to the plane. The derived camber is mathematically invariant to rotation and translation of the total station coordinate frame; thus, repeated installation and removal of the instrument does not break the temporal continuity of the camber time series, making the method suitable for intermittent monitoring over long durations. A 16-day field validation on a vehicle/pedestrian bridge showed that the proposed method repeatedly observed daily camber variations of 2-4 mm above an outer-plane residual RMS noise floor of about 0.77 mm. The weather comparison used ambient temperature and shortwave radiation from a public weather archive. The zero-lag correlation with ambient temperature was weak (Pearson r=+0.23), while a 5 h lag comparison increased the correlation to r=+0.61, indicating a delayed thermal response tendency rather than direct accuracy validation.

3. Caching-based incremental stiffness matrix update for rapid computation of truss structure assembly sequences

   Siripong Petpradit and Junhwa Lee. Journal of the Korean Society for Railway, 28(8), pp.802-811, 2025. doi:10.7782/JKSR.2025.28.8.802

   Truss structures, characterized by linear members connected by hinges, efficiently distribute loads; hence, they are widely adopted in modern engineering. The hinged connections ensure that each member carries only tension or compression, enabling effective load transfer throughout the structure. During construction, the assembly sequence can critically affect substructure stiffness, especially in vulnerable modes like the primary bending mode. Improper assembly sequencing or temporary support placement may lead to reduced stiffness with an increased risk of collapse. In this study, we propose a caching-based incremental stiffness matrix update technique that reduces computation time by updating cached substructure stiffness matrices with the incremental contributions of newly assembled members. Validation on various truss assembly scenarios demonstrates that the proposed method significantly improves computational efficiency compared to traditional approaches.

4. Reference-free ego-motion compensation for vision-based measurement of structural deformations of miter gates

   Shuo Wang, Travis Fillmore, Junhwa Lee, Brian A. Eick, and Billie F. Spencer, Jr. Structural Health Monitoring, 24(4), pp.2193-2208, 2025. doi:10.1177/14759217241297248

   Monitoring the structural condition of miter gates in locks and dams on inland waterways is vital to prevent unforeseen failures. Considering that the application of hydrostatic loads on miter gates is a quasistatic process resulting in two primary structural states, monitoring displacements induced by these loads is an effective method for indicating common degradations and examining miter gate safety. Noncontact sensors such as dual-camera systems are preferred for ensuring continuous operation. A dual-camera system records pre- and postload three-dimensional positions of a miter gate, allowing for the calculation of load-induced structural displacements. However, movement of the dual-camera system itself during the measurement process, referred to as “ego-motion,” introduces measurement errors, as if the structure undergoes additional rigid body motion. Existing studies utilize stationary reference points to correct false displacements; however, the reference points are often challenging to identify in the field. Instead of displacement, this article introduces an algorithmic approach for vision-based extraction of structural deformation while mitigating the impact of rigid body motion from both the structure and the dual-camera system. The algorithm is formulated on the premise that the measurement of deformation, which indicates changes in the relative positions of points on a structure, remains unaffected by rigid body motions originating from any sources. Besides the measurement robustness under ego-motion, deformation is often more informative than displacement in detecting degradations in structures by providing a more direct link to structural strain. The proposed approach is validated through numerical simulations focusing on a field measurement scenario at a miter gate located at the Dalles, Oregon. The procedure of deformation measurement utilizing a dual-camera system is also demonstrated in the synthetic environment created by a graphics-based digital twin of the gate. The proposed approach will facilitate inexpensive deformation measurements that enable condition assessment of miter gates.

5. Multi-vision system for high-resolution strain measurement of continuously welded rail

   Junhwa Lee, Changgil Lee, Inho Yeo, and Seunghoo Jeong. Structural Control and Health Monitoring, 2025, 2447466, 2025. doi:10.1155/stc/2447466

   A continuous welded rail (CWR) is a critical component of modern rail systems, providing increased stability, improved passenger comfort, and reduced maintenance compared with jointed rails. However, the unique mechanical properties of CWR, particularly in the immovable zone where friction restricts longitudinal deformation, require accurate and continuous monitoring to prevent rail buckling or fractures. Despite the availability of various strain‐monitoring technologies, including fiber Bragg grating sensors, strain gauges, and vision‐based systems, these approaches have significant limitations in full‐scale CWR applications. Challenges such as insufficient resolution for detecting minute strains and sensor‐adhesion durability reduce the effectiveness of current strain‐monitoring solutions. To address these limitations, we propose a high‐resolution, vision‐based biaxial strain measurement system specifically designed for CWRs. This system utilizes three microscopic cameras strategically positioned to capture detailed displacement data, allowing for accurate computation of biaxial strain through advanced image processing techniques. The proposed system was validated through both laboratory‐scale and full‐scale experiments and exhibited a minimum detectable strain of 1.5 µε under controlled loading conditions.

6. Phase shift-based resonance assessment for in-service high-speed railway bridges

   Seunghoo Jeong, Hyunmin Kim, Sung Il Kim, Kyoung Chan Lee, and Junhwa Lee. Structural Health Monitoring, 24(1), pp.534-547, 2025. doi:10.1177/14759217241239988

   Resonance in high-speed railway bridges can deteriorate the structural integrity and running safety of a train; thus, the resonant speed needs to be identified. Previous studies have proposed resonant conditions analytically, but their applications to in-service bridges are limited. Free vibration after the passage of a train was utilized to assess resonance, but it could not capture the natural frequency of the coupled system because the train-bridge interaction was neglected. This study proposed a practical framework for quantifying the resonance in a high-speed railway bridge using a phase shift. The static displacement of the railway bridge was numerically reconstructed using the train loading history. Phase angles were extracted by comparing the static and dynamic displacements, which were directly utilized to develop a novel resonance indicator in this study. Numerical simulations and field demonstrations validated the applicability of the proposed method for understanding the resonance behavior of full-scale railway bridges.

7. Robust localization of shear connectors in accelerated bridge construction with neural radiance field

   Gyumin Lee, Ali Turab Asad, Khurram Shabbir, Sung-Han Sim, and Junhwa Lee. Automation in Construction, 168, 105843, 2024. doi:10.1016/j.autcon.2024.105843

   Accelerated bridge construction (ABC) demands precise alignment of prefabricated members to prevent assembly failure. Conventional methods struggle to localize shear connectors from point cloud data (PCD) generated by structure-from-motion due to its sparsity. This paper introduces a robust method for shear connector localization using PCD generated by a neural radiance field and a three-step narrowing-down algorithm. The PCD exhibits densely populated points for small connectors, allowing the algorithm to pinpoint their locations accurately. The method successfully identified all 72 shear connectors in a mock-up prefabricated girder, with an average error of 10 mm, demonstrating its potential for assessing constructability in ABC projects. Future research may integrate deep learning-based segmentation techniques to enhance efficiency and adaptability in complex geometries and non-standard bridge designs.

8. Long-term shape sensing of bridge girders using automated ROI extraction of LiDAR point clouds

   Ganesh Kolappan Geetha, Sahyeon Lee, Junhwa Lee, and Sung-Han Sim. Smart Structures and Systems, 33(6), pp.399-414, 2024. doi:10.12989/sss.2024.33.6.399

   This study discusses the long-term deformation monitoring and shape sensing of bridge girder surfaces with an automated extraction scheme for point clouds in the Region Of Interest (ROI), invariant to the position of a Light Detection And Ranging system (LiDAR). Advanced smart construction necessitates continuous monitoring of the deformation and shape of bridge girders during the construction phase. An automated scheme is proposed for reconstructing geometric model of ROI in the presence of noisy non-stationary background. The proposed scheme involves (i) denoising irrelevant background point clouds using dimensions from the design model, (ii) extracting the outer boundaries of the bridge girder by transforming and processing the point cloud data in a two-dimensional image space, (iii) extracting topology of pre-defined targets using the modified Otsu method, (iv) registering the point clouds to a common reference frame or design coordinate using extracted pre-defined targets placed outside ROI, and (v) defining the bounding box in the point clouds using corresponding dimensional information of the bridge girder and abutments from the design model. The surface-fitted reconstructed geometric model in the ROI is superposed consistently over a long period to monitor bridge shape and derive deflection during the construction phase, which is highly correlated. The proposed scheme of combining 2D-3D with the design model overcomes the sensitivity of 3D point cloud registration to initial match, which often leads to a local extremum.

9. Full-scale structural displacement measurement with camera ego-motion compensation using RGB and LiDAR cameras

   Hyunjun Kim, Sung-Han Sim, Jinyoung Yoon, and Junhwa Lee. Measurement, 237, 115194, 2024. doi:10.1016/j.measurement.2024.115194

   Displacement measurements based on computer vision techniques have shown significant potential in structural health monitoring. However, as the inevitable camera ego-motion results in a drift in the measured displacement, compensation for such errors must be performed carefully. Although an additional RGB camera is considered in conventional methods for error compensation, the need for the sub-target limits its application. This study presents a dual-camera system, consisting of RGB and light detection and ranging (LiDAR) cameras, that enables accurate camera ego-motion compensation for long-term structural displacement measurements. In particular, the LiDAR camera of the proposed system can measure the six-degrees-of-freedom (6-DOF) ego-motion of the camera body regardless of the sub-target and its elaborate design. The accuracy of the proposed method is evaluated by comparing it with that of a conventional RGB camera. The resulting displacement can be employed to monitor the long-term behavior of full-scale bridges.

10. Long-term displacement measurement system for bridge bearing capable of camera reposition

    Jonghwa Hong, Sung-Han Sim, Jeongmin Lee, Hyunjun Kim, Soojin Cho, and Junhwa Lee. Measurement, 235, 114921, 2024. doi:10.1016/j.measurement.2024.114921

    Bridge bearings are critical components that counteract stresses from thermal expansion and traffic loads. Their malfunction due to significant deviations from the intended positions can compromise structural safety. Thus, monitoring their displacement is essential. Previous studies utilized computer vision methods for measuring bearing displacement. However, field-installed cameras often experience movement, resulting in measurement inaccuracies. This paper introduces a computer vision-based method for measuring the six degrees of freedom (6-DOF) displacement of a bridge bearing while correcting for camera ego-motion. A pair of three-dimensional (3D) markers is affixed to both the top and bottom of the bearing. The 3D positions of these markers in relation to the camera are determined using the perspective-n-point method. Employing a formula introduced in this study, we calculate the relative pose between the markers, representing the 6-DOF bearing displacement with camera ego-motion correction. This method’s effectiveness is confirmed through both laboratory-scale and field experiments.

11. Estimation of water stagnation in asphalt-overlaid bridges using ground-penetrating radar

    Junhwa Lee, Jinwoong Choi, Yooseong Shin, and Sung-Han Sim. Structural Control and Health Monitoring, 2023, 7280555, 2023. doi:10.1155/2023/7280555

    Stagnant water in asphalt-overlaid bridge decks is a primary cause of deterioration. Rainwater seeping through the asphalt layer stagnates on waterproofing membranes of the bridge deck, consequently degrading the asphalt pavement and the underlying concrete deck. Thus, identifying ponding regions under pavements potentially containing water can facilitate the prognostic maintenance of bridge decks. This study proposes a framework to estimate the subsurface ponding zone in bridge decks using ground-penetrating radar (GPR). The depth distribution of the nonpermeable layer in the subsurface of the bridge is extracted (depth map) from the GPR C-scan using a conventional thickness evaluation method and used to build a bathymetric dendrogram to model subsurface water flows. The subsurface ponding zone can be identified by considering drainage on the bathymetric dendrogram. The proposed framework is demonstrated using an in-service bridge in Korea. The estimated subsurface ponding zone is compared with damage locations of concrete observed after hydrodemolition.

12. Image-based high-precision strain measurement system for continuous welded rail

    Seunghoo Jeong, Changgil Lee, Gwanghee Kim, Inho Yeo, and Junhwa Lee. Journal of the Korean Society for Railway, 26(9), pp.664-671, 2023. doi:10.7782/JKSR.2023.26.9.664

    고속철도 운영기관은 선로 온도를 바탕으로 레일좌굴을 예방하기 위해 열차의 운행속도를 조절하고 있다. 기후변화로 인한 온도상승은 향후 운행속도 제한 빈도를 증가시킬 것으로 예상된다. 하지만 운행속도 제한 온도를 초과했지만 좌굴이 발생하지 않을 수도 있으며 역으로 좌굴이 발생하지 않을 온도조건이지만 다른 외부조건으로 인해 좌굴이 발생할 수도 있다. 만약 선로온도에 따른 축응력 변화를 모니터링할 수 있다면 응력을 기준으로 좌굴을 예방할 수 있는 방안을 과학적 근거를 통해 마련할 수 있을 것이다. 이에 본 연구는 장대레일의 응력변화를 계측하기 위하여 탈부착이 가능한 영상기반 초정밀 변형계측 시스템을 개발하였다. 해당 시스템은 3대의 디지털현미경을 사용하여 레일 복부를 대상으로 2축 변형률(축방향, 연직방향)을 측정할 수 있도록 설계되어 부동구간에서도 레일 축응력 변화를 계측할 수 있다. 이번 연구에서는 1축 하중재하 실험을 통해 1축 변형률 및 응력변화를 계측할 수 있는지 여부를 실내실험을 통해 확인하였다. 실험결과, 0.25℃ 온도 팽창에 해당하는 최대 3με 수준의 변형률을 계측할 수 있음을 확인하였고, 변형률계(strain gauge)로 계측된 결과와 비교하여 계측시스템의 성능을 검증하였다.

13. Seasonal variation of dynamic properties of Honam high-speed railway PSC bridge

    Seunghoo Jeong, Junhwa Lee, and Kyoung Chan Lee. Journal of the Korean Society for Railway, 26(4), pp.260-267, 2023. doi:10.7782/JKSR.2023.26.4.260

    호남고속철도 40m PSC 박스 거더 교량을 대상으로 장기계측한 결과를 바탕으로 계절 변화에 따른 구조 동특성 변화를 살펴보았다. 대상 교량은 일반적으로 4.5 Hz의 고유진동수를 보였지만 동절기 영하의 온도에서 고유진동수가 최대 5.3 Hz까지 증가하는 것을 계측을 통해 확인하였다. 고유진동수 증가는 동절기에 교량의 강성이 최대 39% 증가한 것으로 해석된다. 동절기에 교량의 강성이 증가하는 원인으로 콘크리트 궤도와 교량이 동결로 인하여 고착되어 구조적으로 일체화되어 나타나는 것으로 추정된다. 콘크리트 궤도의 교량보호콘크리트층(PCL) 및 궤도슬래브(TCL)가 교량과 일체화되면 교량의 휨 강성이 30~40% 증가하며, 이는 고유진동수 증가에 따른 휨강성 변화와 그 값의 범위가 유사하다. 다만, 콘크리트 궤도와 교량 사이의 동결에 의한 고착 현상이 실제로 어느 정도 영향을 주는지에 대해서는 향후 추가적인 실험적 검증이 필요하다.

14. Computer vision-based remote displacement monitoring system for in-situ bridge bearings robust to large displacement induced by temperature change

    Byunghyun Kim, Junhwa Lee, Sung-Han Sim, Soojin Cho, and Byeong Hwa Park. Smart Structures and Systems, 30(5), pp.521-535, 2022. doi:10.12989/sss.2022.30.5.521

    Efficient management of deteriorating civil infrastructure is one of the most important research topics in many developed countries. In particular, the remote displacement measurement of bridges using linear variable differential transformers, global positioning systems, laser Doppler vibrometers, and computer vision technologies has been attempted extensively. This paper proposes a remote displacement measurement system using closed-circuit televisions (CCTVs) and a computer-vision-based method for in-situ bridge bearings having relatively large displacement due to temperature change in long term. The hardware of the system is composed of a reference target for displacement measurement, a CCTV to capture target images, a gateway to transmit images via a mobile network, and a central server to store and process transmitted images. The usage of CCTV capable of night vision capture and wireless data communication enable long-term 24-hour monitoring on wide range of bridge area. The computer vision algorithm to estimate displacement from the images involves image preprocessing for enhancing the circular features of the target, circular Hough transformation for detecting circles on the target in the whole field-of-view (FOV), and homography transformation for converting the movement of the target in the images into an actual expansion displacement. The simple target design and robust circle detection algorithm help to measure displacement using target images where the targets are far apart from each other. The proposed system is installed at the Tancheon Overpass located in Seoul, and field experiments are performed to evaluate the accuracy of circle detection and displacement measurements. The circle detection accuracy is evaluated using 28,542 images captured from 71 CCTVs installed at the testbed, and only 48 images (0.168%) fail to detect the circles on the target because of subpar imaging conditions. The accuracy of displacement measurement is evaluated using images captured for 17 days from three CCTVs; the average and root-meansquare errors are 0.10 and 0.131 mm, respectively, compared with a similar displacement measurement. The long-term operation of the system, as evaluated using 8-month data, shows high accuracy and stability of the proposed system.

15. Comparative study of long-term displacement measurement methods — Focusing on a pre-stressed concrete bridge under construction

    Junhwa Lee, Seunghoo Jeong, Hyunjun Kim, Kyoung Chan Lee, and Sung-Han Sim. Measurement, 201, 111691, 2022. doi:10.1016/j.measurement.2022.111691

    Bridges continuously deflect over their entire lifetime, mostly due to fatigue, deterioration, time-varying material properties, or changes in boundary conditions. Long-term displacement can be employed in both the construction and operation stages for quality control and prognostic maintenance measures. Despite their versatile advantages, conventional sensors are limitedly employed for long-term displacement measurements owing to practical limitations such as clearance occupation, financial feasibility, long-term drift error, or insufficient displacement resolution. This study aims to demonstrate the long-term application of displacement measurement methods on a full-scale railway bridge using a dual-camera system, a light detection and ranging (LiDAR) system, a strain gauge, a linear variable differential transformer (LVDT), and a ring gauge for long-term monitoring for 548 days. The resulting displacements are comparatively analyzed based on dead loads and weather conditions, elucidating the practical issues arising in the long-term displacement monitoring of full-scale bridge.

16. Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

    Jaebeom Lee, Seunghoo Jeong, Junhwa Lee, Sung-Han Sim, Kyoung Chan Lee, and Young-Joo Lee. Structural Health Monitoring, 21(6), pp.2518-2530, 2022. doi:10.1177/14759217211063424

    Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.

17. Nontarget-based measurement of 6-DOF displacement using combined RGB color and depth information

    Sahyeon Lee, Junhwa Lee, Jong-Woong Park, and Sung-Han Sim. IEEE/ASME Transactions on Mechatronics, 26(3), pp.1358-1368, 2021. doi:10.1109/TMECH.2020.3019288

    Structural displacement is an important physical quantity that provides essential information regarding the structural conditions and the safety of a structure. To date, a wide variety of displacement measurement methods have been developed. As a noncontact type of measurement, the computer-vision-based approach receives significant attention for its cost effectiveness, measurement convenience, and great performance. However, most existing methods are limited to one- or two-degrees-of-freedom (DOF) displacement measurement and require known dimensions of structural members or predefined target markers attached to the target structure, which limits real-world application. This article proposes a nontarget-based 6-DOF displacement measurement method using the combined RGB and depth information. The proposed method utilizes three-dimensional information of measurement points on the target structure obtained using the RGB-D camera, and suggests a coordinate transform scheme to determine the 6-DOF displacement in the physical domain. The proposed method is validated in two laboratory experiments using a shear building model and a pan-tilt with a measurement plate, in which both translational and rotational displacements are measured.

18. Automated wireless monitoring system for cable tension forces using deep learning

    Seunghoo Jeong, Hyunjun Kim, Junhwa Lee, and Sung-Han Sim. Structural Health Monitoring, 20(4), pp.1805-1821, 2021. doi:10.1177/1475921720935837

    As demand for long-span bridges is increasing worldwide, effective maintenance has become a critical issue to maintain their structural integrity and prolong their lifetime. Given that a stay-cable is the principal load-carrying component in cable-stayed bridges, monitoring tension forces in stay-cables provides critical data regarding the structural condition of bridges. Indeed, various methodologies have been proposed to measure cable tension forces, including the magneto-elastic effect-based sensor technique, direct measurement using load cells, and indirect tension estimation based on cable vibration. In particular, vibration-based tension estimation has been widely applied to systems for tension monitoring and is known as a cost-effective approach. However, full automation under different cable tension forces has not been reported in the literature thus far. This study proposes an automated cable tension monitoring system using deep learning and wireless smart sensors that enables tension forces to be estimated. A fully automated peak-picking algorithm tailored to cable vibration is developed using a region-based convolution neural network to apply the vibration-based tension estimation method to automated cable tension monitoring. The developed system features embedded processing on wireless smart sensors, which includes data acquisition, power spectral density calculation, peak-picking, post-processing for peak-selection, and tension estimation. A series of laboratory and field tests are conducted on a cable to validate the performance of the proposed automated monitoring system.

19. Long-term displacement measurement of full-scale bridges using camera ego-motion compensation

    Junhwa Lee, Kyoung Chan Lee, Seunghoo Jeong, Young-Joo Lee, and Sung-Han Sim. Mechanical Systems and Signal Processing, 140, 106651, 2020. doi:10.1016/j.ymssp.2020.106651

    Civil infrastructures experience long-term deflection as a result of persistent and transitory loadings, including self-weight, pre-stress, traffic, temperature variation, and stress redistributions due to damage. Thus, long-term displacement is an important safety indicator that can be widely employed in structural health monitoring. However, its measurement is challenging because of the errors induced by the ego-motion of sensors. Researchers have attempted to compensate for the motion-induced error in computer vision-based displacement measurement methods for full-scale civil structures by using fixed objects in the background. Because remotely located objects cannot fully provide six degrees-of-freedom (6-DOF) camera motions, further developments are necessary for complete error compensation. This paper proposes a long-term displacement measurement strategy that uses computer vision-based ego-motion compensation. The proposed system consists of main and sub-cameras attached to each other. While the main camera employs the conventional computer vision-based method for displacement measurement, the sub-camera measures the ego-motions of the dual-camera system from which the motion-induced errors are estimated and compensated for. The proposed long-term displacement measurement algorithm was numerically validated, and the sub-camera was found to provide a noticeable error compensation. A laboratory-scale test showed that the motion-induced error was reduced from 44.1 mm to 1.1 mm. A field application conducted upon a newly constructed railway bridge provided continuous long-term displacement measurement data, which were consistent with LiDAR-based displacement measurements and numerical predictions.

20. Stress estimation using digital image correlation with compensation of camera motion-induced error

    Junhwa Lee, Seunghoo Jeong, Young-Joo Lee, and Sung-Han Sim. Sensors, 19(24), 5503, 2019. doi:10.3390/s19245503

    Measurement of stress levels from an in-service structure can provide important and useful information regarding the current state of a structure. The stress relaxation method (SRM) is the most conventional and practical method, which has been widely accepted for measuring residual stresses in metallic materials. The SRM showed strong potential for stress estimation of civil engineering structures, when combined with digital image correlation (DIC). However, the SRM/DIC methods studied thus far have practical issues regarding camera vibration during hole drilling. To minimize the error induced by the camera motion, the imaging system is installed at a distance from the specimen resulting in the low pixel density and the large extent of the inflicted damage. This study proposes an SRM/DIC-based stress estimation method that allows the camera to be removed during hole drilling and relocated to take the after-drilling image. Since the imaging system can be placed as close to the specimen as possible, a high pixel density can be achieved such that subtle displacement perturbation introduced by a small damage can be acquired by DIC. This study provides a detailed mathematical formulation for removing the camera relocation-induced false displacement field in the DIC result. The proposed method is validated numerically and experimentally.

21. Bayesian prediction of prestressed concrete bridge deflection using finite element analysis

    Jaebeom Lee, Kyoung Chan Lee, Sung-Han Sim, Junhwa Lee, and Young-Joo Lee. Sensors, 19(22), 4956, 2019. doi:10.3390/s19224956

    Vertical deflection has been emphasized as an important safety indicator in the management of railway bridges. Therefore, various standards and studies have suggested physics-based models for predicting the time-dependent deflection of railway bridges. However, these approaches may be limited by model errors caused by uncertainties in various factors, such as material properties, creep coefficient, and temperature. This study proposes a new Bayesian method that employs both a finite element model and actual measurement data. To overcome the limitations of an imperfect finite element model and a shortage of data, Gaussian process regression is introduced and modified to consider both, the finite element analysis results and actual measurement data. In addition, the probabilistic prediction model can be updated whenever additional measurement data is available. In this manner, a probabilistic prediction model, that is customized to a target bridge, can be obtained. The proposed method is applied to a pre-stressed concrete railway bridge in the construction stage in the Republic of Korea, as an example of a bridge for which accurate time-dependent deflection is difficult to predict, and measurement data are insufficient. Probabilistic prediction models are successfully derived by applying the proposed method, and the corresponding prediction results agree with the actual measurements, even though the bridge experienced large downward deflections during the construction stage. In addition, the practical uses of the prediction models are discussed.

22. Long-term displacement measurement of bridges using a LiDAR system

    Junhwa Lee, Kyoung Chan Lee, Sahyeon Lee, Young-Joo Lee, and Sung-Han Sim. Structural Control and Health Monitoring, 26(10), e2428, 2019. doi:10.1002/stc.2428

    Long-term displacement measurements provide important information about the structural safety of civil engineering structures and the associated maintenance necessary. Most approaches developed for measuring displacement, such as direct measurements using the linear variable differential transformer and computer vision-based sensing, relate to campaign-type sensor deployment. However, these methods have critical limitations when used to accurately measure long-term displacement, because it is difficult in practice to appropriately handle unavoidable sensor movement. This study therefore proposes a long-term displacement measurement method using light detection and ranging (LiDAR) that is designed for use with short- and medium-span bridge structures. Reflectors that have higher reflectance than common construction materials (such as concrete and steel) are used to enhance measurement accuracy and reduce scanning time. Strategically deployed reflectors provide a reference point, which is independent of the LiDAR position. Thus, LiDAR can be temporarily installed in the field only when measurements are necessary. Expensive LiDAR systems can be employed to measure the long-term displacement of multiple bridges cost-effectively because permanent installation is not required. The proposed method is validated within the laboratory by focusing on LiDAR position independence and is then used during the early erection stages of a railroad bridge to measure long-term displacement.

23. Integrated cable vibration control system using Arduino

    Seunghoo Jeong, Junhwa Lee, Soojin Cho, and Sung-Han Sim. Smart Structures and Systems, 23(6), pp.695-702, 2019. doi:10.12989/sss.2019.23.6.695

    The number of cable-stayed bridges has been increasing worldwide, causing issues in maintaining the structural safety and integrity of bridges. The stay cable, one of the most critical members in cable-stayed bridges, is vulnerable to wind-induced vibrations owing to its inherent low damping capacity. Thus, vibration mitigation of stay cables has been an important issue both in academia and practice. While a semi-active control scheme shows effective vibration reduction compared to a passive control scheme, real-world applications are quite limited because it requires complicated equipment, including for data acquisition, and power supply. This study aims to develop an Arduino-based integrated cable vibration control system implementing a semi-active control algorithm. The integrated control system is built on the low-cost, low-power Arduino platform, embedding a semi-active control algorithm. A MEMS accelerometer is installed in the platform to conduct a state feedback for the semi-active control. The Linear Quadratic Gaussian control is applied to estimate a cable state and obtain a control gain, and the clipped optimal algorithm is implemented to control the damping device. This study selects the magneto-rheological damper as a semi-active damping device, controlled by the proposed control system. The developed integrated system is applied to a laboratory size cable with a series of experimental studies for identifying the effect of the system on cable vibration reduction. The semi-active control embedded in the integrated system is compared with free and passive mode cases and is shown to reduce the vibration of stay-cables effectively.

24. Uniaxial static stress estimation for concrete structures using digital image correlation

    Junhwa Lee, Eun Jin Kim, Seongwoo Gwon, Soojin Cho, and Sung-Han Sim. Sensors, 19(2), 319, 2019. doi:10.3390/s19020319

    This paper proposes a static stress estimation method for concrete structures, using the stress relaxation method (SRM) in conjunction with digital image correlation (DIC). The proposed method initially requires a small hole to be drilled in the concrete surface to induce stress relaxation around the hole and, consequently, a displacement field. DIC measures this displacement field by comparing digital images taken before and after the hole-drilling. The stress level in the concrete structure is then determined by solving an optimization problem, designed to minimize the difference between the displacement fields from DIC and the one from a numerical model. Compared to the pointwise measurements by strain gauges, the full-field displacement obtained by DIC provides a larger amount of data, leading to a more accurate estimation. Our theoretical results were experimentally validated using concrete specimens, demonstrating the efficacy of the proposed method.

25. Comparative study on displacement measurement sensors for high-speed railroad bridge

    Soojin Cho, Junhwa Lee, and Sung-Han Sim. Smart Structures and Systems, 21(5), pp.637-652, 2018. doi:10.12989/sss.2018.21.5.637

    This paper presents a comparative study of displacement measurement using four sensors that are being used in the field: they are a ring gauge, a laser Doppler vibrometer (LDV), a vision-based displacement measurement system (VDMS), and an optoelectronic displacement meter (ODM). The comparative study was carried out on a brand-new high-speed railroad bridge designed to produce displacements within a couple of millimeters under the loading of a high-speed train. The tests were carried out on a single-span steel plate girder bridge two times with different train loadings: KTX and HEMU. The measured displacement is compared as raw and further discussion was made on the measurement noise, peak displacement, and frequency response of four sensors. The comparisonsare summarized to show the pros and cons of the used sensors in measuring displacement at a typical high-speed railroad bridge.

26. Computer vision-based structural displacement measurement robust to light-induced image degradation for in-service bridges

    Junhwa Lee, Kyoung Chan Lee, Soojin Cho, and Sung-Han Sim. Sensors, 17(10), 2317, 2017. doi:10.3390/s17102317

    The displacement responses of a civil engineering structure can provide important information regarding structural behaviors that help in assessing safety and serviceability. A displacement measurement using conventional devices, such as the linear variable differential transformer (LVDT), is challenging owing to issues related to inconvenient sensor installation that often requires additional temporary structures. A promising alternative is offered by computer vision, which typically provides a low-cost and non-contact displacement measurement that converts the movement of an object, mostly an attached marker, in the captured images into structural displacement. However, there is limited research on addressing light-induced measurement error caused by the inevitable sunlight in field-testing conditions. This study presents a computer vision-based displacement measurement approach tailored to a field-testing environment with enhanced robustness to strong sunlight. An image-processing algorithm with an adaptive region-of-interest (ROI) is proposed to reliably determine a marker’s location even when the marker is indistinct due to unfavorable light. The performance of the proposed system is experimentally validated in both laboratory-scale and field experiments.

27. Concrete crack identification using a UAV incorporating hybrid image processing

    Hyunjun Kim, Junhwa Lee, Eunjong Ahn, Soojin Cho, Myoungsu Shin, and Sung-Han Sim. Sensors, 17(9), 2052, 2017. doi:10.3390/s17092052

    Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%.

28. Extension of indirect displacement estimation method using acceleration and strain to various types of beam structures

    Soojin Cho, Sung-Han Sim, Jong-Woong Park, and Junhwa Lee. Smart Structures and Systems, 14(4), pp.699-718, 2014. doi:10.12989/sss.2014.14.4.699

    The indirect displacement estimation using acceleration and strain (IDEAS) method is extended to various types of beam structures beyond the previous validation on the prismatic or near-prismatic beams. By fusing different types of responses, the IDEAS method is able to estimate displacements containing pseudo-static components with high frequency noise to be significantly reduced. However, the concerns to the IDEAS method come from possible disagreement of the assumed sinusoidal mode shapes to the actual mode shapes, which allows the IDEAS method to be valid only for simply-supported prismatic beams and limits its applicability to real world problems. In this paper, the extension of the IDEAS method to the general types of beams is investigated by the mathematical formulation of the modal mapping matrix only for the monitored substructure, so-called monitoring span. The formulation particularly considers continuous and wide beams to extend the IDEAS method to general beam structures that reflect many real bridges. Numerical simulations using four types of beams with various irregularities are presented to show the effectiveness and accuracy of the IDEAS method in estimating displacements.

29. Vision-based displacement measurement system operable at arbitrary positions

    Junhwa Lee, Soojin Cho, and Sung-Han Sim. Journal of the Korea Institute for Structural Maintenance and Inspection, 18(6), pp.123-130, 2014. doi:10.11112/jksmi.2015.19.1.001

    In this study, a vision-based displacement measurement system is developed to accurately measure the displacement of a structure with locating the camera at arbitrary position. The previous vision-based system brings error when the optical axis of a camera has an angle with the measured structure, which limits the applicability at large structures. The developed system measures displacement by processing the images of a target plate that is attached on the measured position of a structure. To measure displacement regardless of the angle between the optical axis of the camera and the target plate, planar homography is employed to match two planes in image and world coordinate systems. To validate the performance of the present system, a laboratory test is carried out using a small 2-story shear building model. The result shows that the present system measures accurate displacement of the structure even with a camera significantly angled with the target plate.

Under review (4)

1. Damage identification of truss structures using nonlinear inverse mapping

   Kyungwon Kim and Junhwa Lee. Under review, 2026.

2. Orthorectification of UAV images through optimal positioning of lasers

   Hyechan Ye, Sung-Han Sim, and Junhwa Lee. Under review, 2026.

3. Computer vision-based scan-to-BIM from single image for prefabricated concrete slab using UAVs

   Ganesh Kolappan Geetha, Junhwa Lee, and Sung-Han Sim. Under review, 2026.

4. Geometry-decoupled vision-based displacement measurement with Poisson-constrained tilt correction for removable rail strain monitoring

   Seunghoo Jeong and Junhwa Lee. Under review, 2026.