• Korean Chemical Engineering Research
• /
• v.60 no.3
• /
• pp.321-326
• /
• 2022

As the thickness of the polymer membrane of PEMFC(Proton Exchange Membrane Fuel Cells) is getting thinner for PEMFC performance and price reduction, research on improving durability has become more important. In the durability evaluation of membranes, the mechanical durability evaluation time is more than twice that of the chemical durability evaluation time, so it is necessary to select the durability evaluation conditions well. In this study, we tried to check how much the mechanical durability evaluation time changes when there is a difference in the inflow gas type and flow rate in the mechanical durability evaluation protocol (Wet/Dry). When nitrogen was used at a flow rate of 2,000 mL/min, the evaluation time increased by 1.25 times compared to when air was used. An increase in the degradation rate of the electrode Pt was the main factor when air was used. When the flow rate was reduced to 800 mL/min, the air and nitrogen evaluation times increased by 1.5 times and 1.2 times, respectively.

• 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.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.77-91
• /
• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Geomechanics and Engineering
• /
• v.28 no.5
• /
• pp.505-520
• /
• 2022

In recent years, geotextile-encased gravel columns (usually called stone columns) have become a popular method to increasing soil shear strength, decreasing the settlement, acceleration of the rate of consolidation, reducing the liquefaction potential and increasing the bearing capacity of foundations. The behavior of improved loose base-soil with gravel columns under shear loading and the shear stress-horizontal displacement curves got from large scale direct shear test are of great importance in understanding the performance of this method. In the present study, by performing 36 large-scale direct shear tests on sandy base-soil with different fine-content of zero to 30% in both not improved and improved with gravel columns, the effect of the presence of gravel columns in the loose soils were investigated. The results were used to predict the shear stress-horizontal displacement curve of these samples using support vector machines (SVM). Variables such as the non-plastic fine content of base-soil (FC), the area replacement ratio of the gravel column (Arr), the geotextile encasement and the normal stress on the sample were effective factors in the shear stress-horizontal displacement curve of the samples. The training and testing data of the model showed higher power of SVM compared to multilayer perceptron (MLP) neural network in predicting shear stress-horizontal displacement curve. After ensuring the accuracy of the model evaluation, by introducing different samples to the model, the effect of different variables on the maximum shear stress of the samples was investigated. The results showed that by adding a gravel column and increasing the Arr, the friction angle (ϕ) and cohesion (c) of the samples increase. This increase is less in base-soil with more FC, and in a proportion of the same Arr, with increasing FC, internal friction angle and cohesion decreases.

• Geomechanics and Engineering
• /
• v.30 no.6
• /
• pp.539-549
• /
• 2022

Accidental anchor drop can cause disturbances to seabed materials and pose significant threats to the safety and serviceability of submarine structures such as pipelines. In this study, a series of anchor drop tests was carried out to investigate the penetration mechanism of a Hall anchor in sand and clay. A special anchor drop apparatus was designed to model the inflight drop of a Hall anchor. Results indicate that Coriolis acceleration was the primary cause of large horizontal offsets in sand, and earth gravity had negligible impact on the lateral movement of dropped anchors. The indued final horizontal offset was shown to increase with the elevated drop height of an anchor, and the existence of water can slow down the landing velocity of an anchor. It is also observed that water conditions had a significant effect on the influence zone caused by anchors. The vertical influence depth was over 5 m, and the influence radius was more than 3 m if the anchor had a drop height of 25 m in dry sand. In comparison, the vertical influence depth and radius reduced to less than 3 m and 2 m, respectively, when the anchor was released from 10 m height and fell into the seabed with a water depth of 15 m. It is also found that the dynamically penetrating anchors could significantly influence the earth pressure in clay. There is a non-linear increase in the measured penetration depth with kinematic energy, and the resulted maximum earth pressure increased dramatically with an increase in kinematic energy. Results from centrifuge model tests in this study provide useful insights into the penetration mechanism of a dropped anchor, which provides valuable data for design and planning of future submarine structures.

• Journal of the Korean Geotechnical Society
• /
• v.22 no.1
• /
• pp.25-34
• /
• 2006

In this study an effective stress numerical procedure is used to assess the results of dynamic centrifuge tests under high effective stress. The centrifuge models consist of loose Nevada sand with an initial vertical effective stress of 380kPa at depth, and they are modeled as a one-dimentional soil column. Liquefaction occurred up to 37m or 22m at depth, and the onset of liquefaction triggering was opposite to the conventional liquefaction evaluation procedure. In other words, liquefaction occurs first at the top and propagates downward as shaking continues. The results observed in centrifuge tests are reasonably predicted by the effective stress model. It is noted that the degree of initial saturation and additional densification at depth arising from the application of the high acceleration field play a key role in capturing the results of dynamic centrifuge tests.

• Composites Research
• /
• v.21 no.3
• /
• pp.1-8
• /
• 2008

The main objective of this study is to investigate the effect of salt water on the mechanical properties of a carbon-epoxy composite material. Specimens were made of a carbon-epoxy composite USN125 and tested under inplane tension and shear after 0, 0.5, 1, 2, 3, 6, 9, and 12 months immersion in 3.5% salt water. Waterproof painting and acceleration technique were not applied. The tensile strengths and moduli in fiber and matrix directions did not show any remarkable degradation until 12 months immersion. In contrast to the tensile properties, shear strength and modulus started to degrade from the early stage of the immersion time and gradually decreased to 36% and 46% of dry values, respectively, after 12 months immersion.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.1A
• /
• pp.45-52
• /
• 2010

This study pointed on the dynamic impact of AGT (Automated Guide-way Transit) bridge, due to concrete rail prominence. An experiment was done with 30 m P.S.C. bridge in AGT test line in Kyungsan. An artificial prominence with 10 mm hight, was installed at the mid span of concrete rail. And computer simulation was executed for the artificial prominence. As an experiment result, in the case of with prominence, bridge acceleration responses are increased 50% at the speed range of 20 km/h-60 km/h, and bridge displacement responses increased slightly. With these results, the prominence of concrete rail can be induce excess impact and vibration. And the computer program simulated much the same as experiments. So this program can be used for AGT bridge design and formulate the standard of concrete rail management.

• Journal of Animal Environmental Science
• /
• v.18 no.1
• /
• pp.1-8
• /
• 2012

The study was conducted to investigate the effects of climate on indoor environment of a swine house with natural. This study was tested in the beef swine stall at Young-in, Kyung-ki do. The test was experimented for the effect of interior environment by the outdoor environment and the interior-pan. The results are as follows. 1. In test 1 ($T_{out}$ : $25.7^{\circ}C$, without fan), an indoor air flow pattern was showed that entered from sidewall winch-curtain to went out of a indoor by the ridge winch-curtain. And the velocity of a section of the center was measured two times as large as the velocity of the floor. It is the acceleration of the velocity by thermal buoyancy. And, the entered air was rapidly dissipated by flow energy. So that in the swain livestock with sidewall winch-curtain is effected by thermal buoyancy. And the air temperature of the indoor was distributed more higher as compared with the outdoor temperature. This result is caused by the sensible heat from swine and the ventilation is restricted. 2. In test 2 (($T_{out}$ : $25.7^{\circ}C$, with fan), the velocity of a section of the center was measured more higher as compared with the test 1. And the variance of air velocity was distributed higher as compared with the test 1. This result is showed dead region of air flow with a fan operation. And, the variance of gas density was distributed lower as compared with the test 1.

• Journal of The Korean Association For Science Education
• /
• v.25 no.7
• /
• pp.787-793
• /
• 2005

The purposes of this study were to investigate the development of probabilistic thinking in relation to the cognitive level of elementary school students and to analyze the effects of probability activities in Thinking Science(TS) program on the development of probabilistic thinking. 152 6th grade elementary school students compiled the sample group which was divided into an experimental group and a control group. Probability activities in TS program were used with the experimental group, while the normal curriculum was conducted with the control group. Both the experimental and control group were assessed with Science Reasoning Task II and a probabilistic thinking test before execution of this investigation and were post-tested with probabilistic thinking test after the project period was complete. Results of this study showed that the students in the concrete operational stage and transitional stage used subjective strategy together with quantitative strategy in probability problem-solving, and students in the early formal operational stage used quantitative strategy in probability problem-solving. It was also found that the higher the cognitive level of students, the higher the probabilistic thinking level. The probability activities of the TS program influenced the development of probabilistic thinking of elementary school students. Assessing the development of probabilistic thinking on the basis of the cognitive level found that the level of effectiveness was significantly higher for students in the early concrete operational stage and transitional stage than students in any other stage.