• Smart Structures and Systems
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• v.30 no.5
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• pp.521-535
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• 2022

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-mean-square 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.

• Smart Structures and Systems
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• v.15 no.2
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• pp.335-353
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• 2015

Previous long-term measurements of the Uldolmok tidal current power plant showed that the structure's natural frequencies fluctuate with a constant cycle-i.e., twice a day with changes in tidal height and tidal current velocity. This study aims to improve structural health monitoring (SHM) techniques for offshore structures under a harsh tidal environment like the Uldolmok Strait. In this study, lab-scale experiments on a simplified offshore structure as a lab-scale test structure were conducted in a circulating water channel to thoroughly investigate the causes of fluctuation of the natural frequencies and to validate the displacement estimation method using multimetric data fusion. To this end, the numerical study was additionally carried out on the simplified offshore structure with damage scenarios, and the corresponding change in the natural frequency was analyzed to support the experimental results. In conclusion, (1) the damage that occurred at the foundation resulted in a more significant change in natural frequencies compared with the effect of added mass; moreover, the structural system became nonlinear when the damage was severe; (2) the proposed damage index was able to indicate an approximate level of damage and the nonlinearity of the lab-scale test structure; (3) displacement estimation using data fusion was valid compared with the reference displacement using the vision-based method.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.4
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• pp.374-381
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• 2011

It has been doing to research on novel techniques for structural health monitoring by applying various sensor techniques to measure the deflection in mechanical and civil structures. Several electric-based displacement sensors have many difficulties for using them because of EMI (Electro-Magnetic Interference) noise of many lead-wires when they are installed to many points in the structures. In this paper, it is proposed an affordable intensity-based fiber optic sensor to measure small displacement solving the problems of conventional sensors. In detail, the sensor head was designed on the basis of the principle of bending loss and a basic experiment was performed to obtain the sensitivity, the linearity and the stroke of the sensor. Moreover, a prototype was designed and manufactured to be easily installed to a structure and a real-time control software was also successfully developed to drive the fiber optic sensor system.

• Smart Structures and Systems
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• v.16 no.2
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• pp.295-328
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• 2015

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the "drift effect" in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.

• The Journal of Engineering Geology
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• v.18 no.4
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• pp.545-553
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• 2008

Face mapping and displacement monitoring during tunnel construction are the most influential information for the stability assessment of ground and around structures. Especially, the result of face mapping and displacement analysis is essential to the excavation and support design in NATM which is based on the drilling and blasting. However, there have not been so many studies to put those useful information into practice for decision-making process during construction. The study reviewed the tunnel behaviour based on the RMR rating and displacement monitoring when the geological condition of rock mass varies inevitably. The study analysed the crown settlement using convergence equation in order to compensate the disparity induced by the location and time of measurement and found a distinct relation between the geological condition and the line of influence. As a result of analysing the various parameters related to the tunnel convergence according to the geological condition, the study suggested the basic knowledge about the relation between face mapping and displacement behaviour of tunnel.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.199-210
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• 2008

Acoustic emission(AE)/Microseimsic(MS) activities are low-energy seismic events associated with a sudden inelastic deformation such as the sudden movement of existing fractures, the generation of new fractures or the propagation of fractures. These events rapidly increase before major failure and happen within a given rock volume and radiate detectable seismic waves. The main difference between AE and MS signals is that the seismic motion frequencies of AE signals are higher than those of MS signals. As the failure of geotechnical structures usually happens as a high velocity and small displacement, it is not easy to determine the precursor and initiation stress level of failure in displacement detection method. To overcome this problem, AE/MS techniques for detection of structure failure and damage have recently adopt in civil engineering. In this study, AE/MS monitoring system, which consist of sensor, data acquisition and operation program, is constructed with domestic technology. To verify and optimize the developed system, we are now carrying out the field application at an underground research laboratory and the developed AE/MS monitoring will be used in detecting of seismic events with various scales.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.5
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• pp.31-39
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• 2017

Research in the machine tool industry has focused on ICT-based smart machines rather than hardware technologies related to machine tools. Real-time tool-status monitoring is representative of this type of technology and has become important for measuring sensors during cutting processes. In this paper, we studied several research areas and used a round bar to conduct fundamental research into the axial displacement of the main spindle of a tool when it was subjected to a machining load. We were able to use the gap sensor to detect the axial displacement indirectly by using grooves with various shapes on the round bar and sensing the gaps between the grooves. We then determined the optimal groove shape for monitoring the tool state.

• The Journal of the Korea Contents Association
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• v.16 no.1
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• pp.362-369
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• 2016

Recently, Last to check the security status of various medium and large bridge structures using various kinds of measurement equipment, but most of the methods are used to measure and check the displacement behavior of the bridge by a certain period. In this study, receive GPS satellite signals that can be observed in real time the whole region, a bridge to automatically measure the displacement and behavior characteristics of the structure in real-time in mm over the 24 hours, the measurement information and transmits the data to the wireless network, by making use, it was applied to the real-time monitoring system in connection with a bridge to be able to automatically notify GNSS fine displacement behavior. In fact, analysis and receives the measurement data to GNSS provided in the upper bridge of the middle and large-sized aging for this purpose, measuring USN and at the same time is converted into a three-dimensional position information of a test study was conducted to monitor the bridge displacement in real time. As a result, a vertical displacement of about 0.027~0.037m at the measurement time of day of the measurement point is that the repeated and confirmed.

• Smart Structures and Systems
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• v.22 no.5
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• pp.631-641
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• 2018

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.