• Architectural research
• /
• v.20 no.4
• /
• pp.137-145
• /
• 2018

The nondestructive test is widely used in the field of diagnosis and maintenance to evaluate the degree of damaging of structures caused by aging, and the demand for this test method is expected to continue increasing. However, there is a lack of standards related to the nondestructive test, and South Korea is relying heavily on developed nations for original technologies related to diagnosis. It is an urgent task to establish a nondestructive test method appropriate for the circumstance of South Korea. The purpose of this study is to compare and analyze estimated error of compressive strength in single-story structures comprised of vertical and horizontal reinforced concrete members using the impact testing method and rebound hardness method, which are nondestructive test methods, and to review on-site applicability of these methods. Based on compressive strength of the structures estimated, overall mean error was 21.2% for the impact testing method and 15.6% for the rebound hardness method. The necessity of a reliable diagnostic method based on compound nondestructive test methods to increase accuracy of estimation was confirmed.

• Smart Structures and Systems
• /
• v.31 no.5
• /
• pp.501-516
• /
• 2023

Dynamic irregularity and acceleration of bridges subjected to high-speed trains provide crucial information for comprehensive evaluation of the health state of under-track structures. This paper proposes a novel approach for real-time estimation of vertical track dynamic irregularity and bridge acceleration using deep generative adversarial network (GAN) and vibration data from in-service train. The vehicle-body and bogie acceleration responses are correlated with the two target variables by modeling train-bridge interaction (TBI) through least squares generative adversarial network (LSGAN). To realize supervised learning required in the present task, the conventional LSGAN is modified by implementing new loss function and linear activation function. The proposed approach can offer pointwise and accurate estimates of track dynamic irregularity and bridge acceleration, allowing frequent inspection of high-speed railway (HSR) bridges in an economical way. Thanks to its applicability in scenarios of high noise level and critical resonance condition, the proposed approach has a promising prospect in engineering applications.

• Smart Structures and Systems
• /
• v.14 no.5
• /
• pp.831-845
• /
• 2014

This paper proposed a discrete wavelet transform based method for time-varying physical parameter identification of shear type structures. The time-varying physical parameters are dispersed and expanded at multi-scale as profile and detail signal using discrete wavelet basis. To reduce the number of unknown quantity, the wavelet coefficients that reflect the detail signal are ignored by setting as zero value. Consequently, the time-varying parameter can be approximately estimated only using the scale coefficients that reflect the profile signal, and the identification task is transformed to an equivalent time-invariant scale coefficient estimation. The time-invariant scale coefficients can be simply estimated using regular least-squares methods, and then the original time-varying physical parameters can be reconstructed by using the identified time-invariant scale coefficients. To reduce the influence of the ill-posed problem of equation resolving caused by noise, the Tikhonov regularization method instead of regular least-squares method is used in the paper to estimate the scale coefficients. A two-story shear type frame structure with time-varying stiffness and damping are simulated to validate the effectiveness and accuracy of the proposed method. It is demonstrated that the identified time-varying stiffness is with a good accuracy, while the identified damping is sensitive to noise.

• Smart Structures and Systems
• /
• v.1 no.4
• /
• pp.325-338
• /
• 2005

Utilizing robotic based reconfigurable nodal structural health monitoring systems has many advantages over static or human positioned sensor systems. However, creating a robot capable of traversing a variety of civil infrastructures is a difficult task, as these structures each have unique features and characteristics posing a variety of challenges to the robot design. This paper outlines the design and implementation of a novel robotic platform for deployment on ferromagnetic structures as an enabling structural health monitoring technology. The key feature of this design is the utilization of an attachment device which is an advancement of the common magnetic base found in the machine tool industry. By mechanizing this switchable magnetic circuit and redesigning it for light weight and compactness, it becomes an extremely efficient and robust means of attachment for use in various robotic and structural health monitoring applications. The ability to engage and disengage the magnet as needed, the very low power required to do so, the variety of applicable geometric configurations, and the ability to hold indefinitely once engaged make this device ideally suited for numerous robotic and distributed sensor network applications. Presented here are examples of the mechanized variable force magnets, as well as a prototype robot which has been successfully deployed on a large construction site. Also presented are other applications and future directions of this technology.

• Journal of Korean Association for Spatial Structures
• /
• v.21 no.3
• /
• pp.43-50
• /
• 2021

A tilted tall building is actively constructed as landmark structures around world to date. Because lateral displacement responses of a tilted tall building occurs even by its self-weight, reduction of seismic responses is very important to ensure structural safety. In this study, a smart tuned mass damper (STMD) was applied to the example tilted tall building and its seismic response control performance was investigated. The STMD was composed of magnetorheological (MR) damper and it was installed on the top floor of the example building. Control performance of the STMD mainly depends on the control algorithn. Fuzzy logic controller (FLC) was selected as a control algorithm for the STMD. Because composing fuzzy rules and tuning membership functions of FLC are difficult task, evolutionary optimization algorithm (EOA) was used to develop the FLC. After numerical simulations, it has been seen that the STMD controlled by the EOA-optimized FLC can effectively reduce seismic responses fo the tilted tall building.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.181-193
• /
• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Korean Journal of Biological Psychiatry
• /
• v.24 no.2
• /
• pp.68-74
• /
• 2017

Objectives It is well known that problem drinking is associated with alterations of brain structures and functions. Brain functions related to alcohol consumption can be determined by the resting state functional connectivity in various resting state networks (RSNs). This study aims to ascertain the alcohol effect on the structures forming predetermined RSNs by assessing their cortical thickness. Methods Twenty-six abstinent male patients with alcohol dependence and the same number of age-matched healthy control were recruited from an inpatient mental hospital and community. All participants underwent a 3T MRI scan. Averaged cortical thickness of areas constituting 7 RSNs were determined by using FreeSurfer with Yeo atlas derived from cortical parcellation estimated by intrinsic functional connectivity. Results There were significant group differences of mean cortical thicknesses (Cohen's d, corrected p) in ventral attention (1.01, < 0.01), dorsal attention (0.93, 0.01), somatomotor (0.90, 0.01), and visual (0.88, 0.02) networks. We could not find significant group differences in the default mode network. There were also significant group differences of gray matter volumes corrected by head size across the all networks. However, there were no group differences of surface area in each network. Conclusions There are differences in degree and pattern of structural recovery after abstinence across areas forming RSNs. Considering the previous observation that group differences of functional connectivity were significant only in networks related to task-positive networks such as dorsal attention and cognitive control networks, we can explain recovery pattern of cognition and emotion related to the default mode network and the mechanisms for craving and relapse associated with task-positive networks.

• Smart Structures and Systems
• /
• v.6 no.3
• /
• pp.263-275
• /
• 2010

In the last decade, wireless sensor networks have emerged as a promising technology that could accelerate progress in the field of structural monitoring. The main advantages of wireless sensor networks compared to conventional monitoring technologies are fast deployment, small interference with the surroundings, self-organization, flexibility and scalability. These features could enable mass application of monitoring systems, even on smaller structures. However, since wireless sensor network nodes are battery powered and data communication is the most energy consuming task, transferring all the acquired raw data through the network would dramatically limit system lifetime. Hence, data reduction has to be achieved at the node level in order to meet the system lifetime requirements of real life applications. The objective of this paper is to discuss some general aspects of data processing and management in monitoring systems based on wireless sensor networks, to present a prototype monitoring system for civil engineering structures, and to illustrate long-term field test results.

• Investigative Magnetic Resonance Imaging
• /
• v.13 no.1
• /
• pp.47-53
• /
• 2009

Purpose : Although it's been known for half a century that unique structures have evolved in the cerebellum and they then became greatly enlarged in the human brain, the function of these structures still remains unknown. The purpose of this study was to assess cerebellar activation during motor, sensory, word generation, listening comprehension, and working memory tasks with using functional magnetic resonance imaging (fMRI). Materials and Methods : Eleven healthy right-handed subjects (Male: female, 6:5, mean age: 27.4years) were imaged on a Siemens 1.5T scanner. Whole brain functional maps were acquired using BOLD EPI sequences in the axial plane. Each paradigm consisted of five epochs of activation vs. the control condition. The activation tasks consisted of left finger complex movement, sensory stimulation of the left hand, word generation, listening comprehension, and working memory tasks. The reference function was a boxcar waveform. The activation maps were thresholded at p = 0.001. SPM 5 evaluated the activated areas and responses within the cerebellum. Results : Cerebellar activation was observed on motor task, word generation task, and working memory task. There were 949 activated areas and the mean fitted and adjusted response was 0.68 during the motor task. There were 319 activated areas and the mean fitted and adjusted response was 0.15 during the word generation task. There were 330 activated areas and the mean fitted and adjusted response was 0.26 during the working memory task. Conclusion : Our results suggest that the cerebellum is involved in a variety of functional tasks, including motor, word generation, and working memory tasks. However, during the motor task, the cerebellum showed a large activated area and a high response. Cerebellar function can be evaluated by fMRI.

• The Korean Journal of Health Service Management
• /
• v.11 no.1
• /
• pp.27-42
• /
• 2017

Objectives : This study analyzes the characteristics of hospital organization structures, insurance claims reviews and assessment tasks and their effects on hospitals in Pusan. Methods : The data for this study were collected through interview and self-administered surveys in 109 hospitals. The study included only - hospitals with a minimum of 50beds and excluded those providing only dental, psychiatric, or long-term care. Results : The findings of this study state that the number of beds has an influence on the organizitional structure. Conclusions : Hospital managements should seek human resources management(the insurance claims reviewer and evaluator) schemes that take into account the characteristics of the medical institution. In addition, insurance claims review and assessment tasks in hospitals require considerable knowledge and experience, and hospitals should be equipped with staff that have the relevant expertise. Therefore, to further deepen knowledge, comprehensive training should be continuously carried out in order to produce specialists in claims review and assessment.