• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.

• Journal of Environmental Health Sciences
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• v.47 no.6
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• pp.540-547
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• 2021

Background: Indoor air pollutants are caused by a number of factors, such as coming in from the outside or being generated by internal activities. Typical indoor air pollutants include nitrogen dioxide and carbon monoxide from household items such as heating appliances and volatile organic compounds from building materials. In addition there is carbon dioxide from human breathing and bacteria from speaking, coughing, and sneezing. Objectives: According to recent research results, most indoor air pollution is known to be greatly affected by internal factors such as burning (biomass for cooking) and various pollutants. These pollutants can have a fatal effect on the human body due to a lack of ventilation facilities. Methods: We fabricated a polydopamine (PDA) layer with Ti substrates as a coating on supported glass fiber fabric to enhance its photo-activity. The PDA layer with TiO₂ was covalently attached to glass fiber fabric using the drop-casting method. The roughness and functional groups of the surface of the Ti substrate/PDA coated glass fiber fabric were verified through infrared imaging microscopy and field emission scanning electron microscopy (FE-SEM). The obtained hybrid Ti substrate/PDA coated glass fiber fabric was investigated for photocatalytic activity by the removal of ammonia and an epidermal Staphylococcus aureus reduction test with lamp (250 nm, 405 nm wavelength) at 24℃. Results: Antibacterial properties were found to reduce epidermal staphylococcus aureus in the Ti substrate/PDA coated glass fiber fabric under 405 nm after three hours. In addition, the Ti substrate/PDA coated glass fiber fabric of VOC reduction rate for ammonia was 50% under 405 nm after 30 min. Conclusions: An electron-hole pair due to photoexcitation is generated in the PDA layer and transferred to the conduction band of TiO₂. This generates a superoxide radical that degrades ammonia and removes epidermal Staphylococcus aureus.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.469-479
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• 2021

As a material for preventing spalling of concrete, the effectiveness of PP fiber has already been confirmed. However, it is necessary to consider the maximum temperature that occurs during a fire, and to solve the mixing problem and the strength reduction problem that occur depending on the mixing amount. In this study, the fire resistance performance of tunnel segment linings according to the PP fiber content and air volume under the RABT fire scenario was investigated. As a result, no spalling or cross-sectional loss occurred in all test specimens, and when the PP fiber content was small, the maximum temperature was relatively high and the maximum temperature arrival time was also fast. On the other hand, no trend was found for the maximum temperature and arrival time according to the difference in air volume. In the internal temperature distribution results for the PP fiber mixing amount of 0.75, 1.0, 1.5, and 2.0 kg/m³, the results of 0.75 and 1.0 kg/m³ showed similar temperature distribution, and the results of 1.5 and 2.0 kg/m³ were similar. It was confirmed that the internal temperature distribution tends to decrease at the same depth when the amount of PP fiber mixed is large, and it was confirmed that a remarkable difference occurred from the results of 1.0 kg/m³ and 1.5 kg/m³ of PP fiber mixed amounts.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.217-226
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• 2022

In the view of the application of high-precision spatial information production, UAV (Umanned Aerial Vehicle)-Photogrammetry has a problem in that it lacks specific procedures and detailed regulations for quantitative quality verification methods or certification of captured images. In addition, test tools for UAV image quality assessment use only the GSD (Ground Sample Distance), not MTF (Modulation Transfer Function), which reflects image resolution and contrast at the same time. This fact makes often the quality of UAV image inferior to that of manned aerial image. We performed MTF and GSD analysis simultaneously using a siemens star to confirm the necessity of MTF analysis in UAV image quality assessment. The analyzing results of UAV images taken with different payload and sensors show that there is a big difference in σ_MTF values, representing image resolution and the degree of contrast, but slightly different in GSD. It concluded that the MTF analysis is a more objective and reliable analysis method than just the GSD analysis method, and high-quality drone images can only be obtained when the operator make images after judging the proper selection the sensor performance, image overlaps, and payload type. However, the results of this study are derived from analyzing only images acquired by limited sensors and imaging conditions. It is therefore expected that more objective and reliable results will be obtained if continuous research is conducted by accumulating various experimental data in related fields in the future.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.305-326
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• 2022

To realize the recycling utilization of waste concrete and alleviate the shortage of resources, 11 specimens of steel reinforced recycled concrete (SRRC) filled circular steel tube columns were designed and manufactured in this study, and the cyclic loading tests on the specimens of columns were also carried out respectively. The hysteretic curves, skeleton curves and performance indicators of columns were obtained and analysed in detail. Besides, the finite element model of columns was established through OpenSees software, which considered the adverse effect of recycled coarse aggregate (RA) replacement rates and the constraint effect of circular steel tube on internal RAC. The numerical calculation curves of columns are in good agreement with the experimental curves, which shows that the numerical model is relatively reasonable. On this basis, a series of nonlinear parameters analysis on the hysteretic behaviors of columns were also investigated. The results are as follows: When the replacement rates of RA increases from 0 to 100%, the peak loads of columns decreases by 7.78% and the ductility decreases slightly. With the increase of axial compression ratio, the bearing capacity of columns increases first and then decreases, but the ductility of columns decreases rapidly. Increasing the wall thickness of circular steel tube is very profitable to improve the bearing capacity and ductility of columns. When the section steel ratio increases from 5.54% to 9.99%, although the bearing capacity of columns is improved, it has no obvious contribution to improve the ductility of columns. With the decrease of shear span ratio, the bearing capacity of columns increases obviously, but the ductility decreases, and the failure mode of columns develops into brittle shear failure. Therefore, in the engineering design of columns, the situation of small shear span ratio (i.e., short columns) should be avoided as far as possible. Based on this, the calculation model on the skeleton curves of columns was established by the theoretical analysis and fitting method, so as to determine the main characteristic points in the model. The effectiveness of skeleton curve model is verified by comparing with the test skeleton curves.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.193-198
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• 2021

In this study, the rheological characteristics and of 3D printing composite materials and the compressive strength characteristics according to the lamination patterns were evaluated. As a result of rheology test, rapid material change was observed after 60 minutes of extrusion, yielding stress 1.4 times higher than immediately after mixing, and plastic viscosity was 14.94-25.62% lower. The compressive strength of the specimens manufactured in the mold and the laminated specimens were compared, and the lamination pattern of the laminated specimens were 0°, 45°, and 90° as variables. The compressive strength of the mold casting specimen and the laminated specimen from 1 to 28 days of age showed similar performance regardless of the lamination pattern. In particular, at the age of 28 days, the modulus of elasticity, maximum compressive strength, and strain at maximum stress of all specimens were almost the same. In order to analyze the interface of the laminated specimens, X-ray CT analysis of the specimen whose compressive strength were measured was performed. Through CT analysis, it was confirmed that cracks did not occur at the lamination interface, which can be judged that the interface in the laminated specimen behaved in an integrated manner.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.56-64
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• 2021

Leakage due to deterioration and damage is one of the major causes of volume change by freezing and thawing, and it leads micro-cracking and surface scaling in concrete structures. The deterioration of damaged concrete accelerates with the chloride attack. Thus, in the detailed guidelines for facility performance evaluation (2020), the quality of cover concrete and the freeze-thaw (FT) repetition cycle were newly suggested for concrete durability assessment. The quality of cover concrete should be evaluated by the rebound hammer test and the FT repetition cycle should be also considered in the deterioration environmental assessment. This study suggested the application of fast dynamic based nonlinear ultrasound method to monitor initial micro-scale damage under freezing and thawing environment. Concrete specimens were fabricated with different water-cement ratios (40%, 60%) and air contents (1.5% and 3.0%). The compressive strength, rebound number, relative dynamic modulus, and nonlinear ultrasound were measured with different FT cycles. The scanning electron microscopy was also performed to investigate the micro-scale FT damage. As a result, both the rebound number and the relative dynamic modulus had difficulty to detect early damage but the proposed method showed a potential to detect initial micro-scale damage and predict the FT resistance performance of concrete.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.43-54
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• 2022

The settlement at the railroad foundation is often the leading cause of track irregularity and potential derailment. The control of such deformation is considered necessary in track maintenance practice. Nevertheless, the monitoring process performed by in situ surveying requires an excessive amount of manpower and cost. The InSAR, a remote sensing technique by RADAR satellite, is used to overcome such a burden. The PS-InSAR technique is preferred for a long-term precise monitoring method. However, this study aims to obtain relatively brief analysis results from only two satellite images using the D-InSAR technique, while a minimum of 25 images are required for PS-InSAR. This study verifies the precision of D-InSAR within a few millimeters by inspecting railroad facilities and land settlements in Korea Railroad Research Institute's test track with images from TerraSAR-X Satellite. Multiple corner reflectors were adopted and installed on an embankment and the building roof to raise the surface reflectivity. Those reflectors were slightly adjusted periodically to verify the detecting performance. The results revealed the optimum distance between corner reflectors. Further, the deformation of railway tracks, slopes, and concrete structures was analyzed successively. In conclusion, this study indicates that the D-InSAR technique effectively monitors the short-term deformation of a broad area such as railway structures.