• Computers and Concrete
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• v.3 no.6
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• pp.439-454
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• 2006

This paper develops an artificial neural network (ANN) model for uniformly loaded restrained reinforced concrete (RC) slabs incorporating membrane action. The development of membrane action in RC slabs restrained against lateral displacements at the edges in buildings and bridge structures significantly increases their load carrying capacity. The benefits of compressive membrane action are usually not taken into account in currently available design methods based on yield-line theory. By extending the existing knowledge of compressive membrane action, it is possible to design slabs in building and bridge decks economically with less than normal reinforcement. The processes involved in the development of ANN model such as the creation of a database of test results from previous research studies, the selection of architecture of the network from extensive trial and error procedure, and the training and performance validation of the model are presented. The ANN model was found to predict accurately the ultimate strength of fully restrained RC slabs. The model also was able to incorporate strength enhancement of RC slabs due to membrane action as confirmed from a comparative study of experimental and yield line-based predictions. Practical applications of the developed ANN model in the design process of RC slabs are also highlighted.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.337-362
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• 2015

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

• Journal of Engineering Education Research
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• v.24 no.2
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• pp.51-60
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• 2021

Although more than 15 years have passed since the introductory design courses were opened due to the introduction of engineering education certification in the civil engineering field, these courses have been operated somewhat unsatisfactorily compared to their importance as an introductory course for engineering design. This is partially because the quality of classes is affected by the instructor's individual ability due to the fact that a standard training plan for these courses has not been established so far. Therefore, in this paper, we try to present a reference model for the introductory design course by introducing a class operation case established through continuous improvement process over the last 10 years at Chungbuk National University. This case aims to cultivate students' problem solving and system design skills by carrying out projects to develop egg drop and wood bridge systems based on creative problem solving methodologies. As a result of a questionnaire survey conducted after the class, we found that students' problem solving and system design capabilities were improved significantly and there was a meaningful increase in level of interest and attention in civil engineering by taking this class.

• The Journal of Korean Physical Therapy
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• v.32 no.6
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• pp.372-377
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• 2020

Purpose: The purpose of this study was to verify the effect of applying clam exercise on improving trunk control and balance ability in stroke patients. Based on this, we tried to provide clinical information. Methods: In this study, 18 patients with chronic stroke were recruited from a rehabilitation hospital. The patients were divided into two groups: a clam exercise group (9 patients) and a control group (9 patients). After 30 minutes of neuro-development therapy, they performed clam exercise or bridge exercise for 3 weeks, 5 times a week for 30 minutes. A trunk impairment scale (TIS) and a postural assessment scale for stroke patients-trunk control (PASS-TC) were performed to evaluate the subjects' ability to control trunk before and after intervention. Balance ability was measured by Balancia before and after intervention. Results: After the training periods, area 95% COP and weight distribution of the affected side were significantly different from the clam exercise group compared to the control group (p<0.05). Conclusion: Based on the results of this study, in can be seen that the clam exercise is effective in improving the balance ability compared to the bridge exercise. Maintaining the standing posture requires muscle strength of the hip abduction and extension, which is the result of the clam exercise selectively strengthening these muscles. Therefore, if you want to provide intervention to improve the balance of stroke patients, it is recommended to perform a clam exercise.

• Earthquakes and Structures
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• v.25 no.6
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.414-419
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• 2020

An artificial intelligence-based cable tension estimation model was developed to expand the utilization of data obtained from cable accelerometers of cable-stayed bridges. The model was based on an algorithm for selecting the natural frequency in the tension estimation process based on the vibration method and an applied artificial neural network (ANN). The training data of the ANN was composed after converting the cable acceleration data into the frequency, and machine learning was carried out using the characteristics with a pattern on the natural frequency. When developing the training data, the frequencies with various amplitudes can be used to represent the frequencies of multiple shapes to improve the selection performance for natural frequencies. The performance of the model was estimated by comparing it with the control criteria of the tension estimated by an expert. As a result of the verification using 139 frequencies obtained from the cable accelerometer as the input, the natural frequency was determined to be similar to the real criteria and the estimated tension of the cable by the natural frequency was 96.4% of the criteria.

• Journal of Power Electronics
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• v.15 no.3
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• pp.621-633
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• 2015

This paper proposes the analysis and design of a DC-side symmetrical zero-current-switching (ZCS) Class-D current-source driven resonant rectifier to improve the low power-factor and high line current harmonic distortion of lighting applications. An analysis of the junction capacitance effect of Class-D ZCS rectifier diodes, which has a significant impact on line current harmonic distortion, is discussed in this paper. The design procedure is based on the principle of the symmetrical Class-D ZCS rectifier, which ensures more accurate results and provides a more systematic and feasible analysis methodology. Improvement in the power quality is achieved by using the output characteristics of the DC-side Class-D ZCS rectifier, which is inserted between the front-end bridge-rectifier and the bulk-filter capacitor. By using this symmetrical topology, the conduction angle of the bridge-rectifier diode current is increased and the low line harmonic distortion and power-factor near unity were naturally achieved. The peak and ripple values of the line current are also reduced, which allows for a reduced filter-inductor volume of the electromagnetic interference (EMI) filter. In addition, low-cost standard-recovery diodes can be employed as a bridge-rectifier. The validity of the theoretical analysis is confirmed by simulation and experimental results.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.13-23
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• 2016

In recent years, big data has usefully been deployed by organizations with the aim of getting a better prediction for the future. Moreover, knowledge management systems are being used by organizations to identify and create knowledge. Here, the output from analysis of big data and a knowledge management system are used to develop a new model with the goal of minimizing the cost of implementing new recognized processes including staff training, transferring and employment costs. Strategies are proposed from big data analysis and new processes are defined accordingly. The company requires various skills to execute the proposed processes. Organization's current experts and their skills are known through a pre-established knowledge management system. After a gap analysis, managers can make decisions about the expert arrangement, training programs and employment to bridge the gap and accomplish their goals. Finally, deduction graph is used to analyze the model.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.424-440
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• 2019

This research is about recovery experiences of rehabilitators who reside in the therapeutic community. Nine rehabilitators who reside in the therapy community have participated in this research. The research methods are made up with in-depth interview and descriptive analysis phrases of phenomenology Giorgi has proposed. The result of this research can be summarized as follows: participants have experienced courage to confront, reestablishment of responsibility, social skills training in daily life, therapeutic ally family, improvement in labour productivity, golden bridge of returning to their family and encounter self-existence. Based on the result of research, researchers have proposed the job-training program in therapeutic community, link the therapy community to the resource of local community and participation of rehabilitators' family in programs of the therapeutic community.

• Smart Structures and Systems
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• v.29 no.1
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.