• 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 Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Explosives and Blasting
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• v.40 no.3
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• pp.19-32
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• 2022

Hydrogen is a fuel having the highest energy compared with other common fuels. This means hydrogen is a clean energy source for the future. However, using hydrogen as a fuel has implication regarding carrier and storage issues, as hydrogen is highly inflammable and unstable gas susceptible to explosion. Explosions resulting from hydrogen-air mixtures have already been encountered and well documented in research experiments. However, there are still large gaps in this research field as the use of numerical tools and field experiments are required to fully understand the safety measures necessary to prevent hydrogen explosions. The purpose of this present study is to develop and simulate 3D numerical modelling of an existing hydrogen gas station in Jeonju by using handheld LiDAR and Ansys AUTODYN, as well as the processing of point cloud scans and use of cloud dataset to develop FEM 3D meshed model for the numerical simulation to predict peak-over pressures. The results show that the Lidar scanning technique combined with the ANSYS AUTODYN can help to determine the safety distance and as well as construct, simulate and predict the peak over-pressures for hydrogen refueling station explosions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5C
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• pp.293-301
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• 2008

Soil profiling is one of the most important work among geotehnical engineering practice. Generally, soil profile is estimated from the observation of soil samples during subsurface exploration but such estimation also includes some experiencing aspects such as flushed water from the borehole, slime colour, boring speed and so on. In addition, since the capacity of hydraulic drill rig is significantly increased, thin layers might be easily missed. So, continuous soil profile is almost impossible over all depth to be bored from conventional subsurface exploration. While CPT or CPTu can serve continuous soil profile information over all depth generally in 5cm interval. Many charts or methods for soil profile from CPT result have been proposed during last several decades over the world. However they have not been verified in local ground condition in Korea. In this research, CPT results and soil classification results based on USCS were compiled from 17 sites over the Korea. Soil classification results by using 7 CPT soil classification charts were compared with those of USCS for the compiled database. Most proper CPT soil classification chart for Korean soil characteristics was evaluated and effective parameters for the soil classification from CPT were discussed. Finally interrelationship between CPT soil classification chart and USCS soil classification was evaluated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.741-747
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• 2008

Nowadays, the accuracy of the geoid model has been improved through development of the combination model which was composed of traditional gravimetric geoid and geometric geoid by the GPS/leveling data in USA and Japan. It is a state of the art method in geoid modeling field that what so called hybrid geoid. In this paper, as a basic study to develop Korean hybrid geoid model, we studied gravimetric geoid solutions using three gravity reduction methods (Helmert's condensation method, RTM method and Airy-isostatic method) and evaluated the usefulness of each method in context of precise geoid. The gravimetric geoid model were determined by restoring the gravity anomalies (included TC) and the indirect effects were made from various reduction methods on the EIGEN-CG03C reference field. The results are compared with respect to the geometric geoid undulation determined from 498 GPS/leveling after LSC fitting. The results showed that hybrid geoid with RTM (Residual terrain model) reduction method was most accurate method and the value of the difference compared to geometric geoid was $0.001{\pm}0.053m$.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.435-445
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• 2023

A delta is a depositional landform that is formed when sediment transported by a river is deposited in a relatively low-energy environment, such as a lake, sea, or a main channel. Among these, a delta formed at the confluence of rivers has a great importance in river management and research because it has a significant impact on the hydraulic and sedimentological characteristics of the river. Recently, the equilibrium state of the confluence area has been disrupted by large-scale dredging and construction of levees in the Nakdong River. However, due to the natural recovery of the river, the confluence area is returning to its pre-dredging natural state through ongoing sedimentation. The time-series data show that the confluence delta has been steadily growing since the dredging, but once it reaches a certain size, it repeats growth and retreat, and the overall size does not change significantly. In this study, we developed a model to explain the sedimentation-erosion processes in the confluence area based on the assumption that the confluence delta reaches a dynamic equilibrium. The model is based on two fundamental principles: sedimentation due to supply from the tributary and erosion due to the main channel. The erosion coefficient that represents the Nakdong River confluence areas, was obtained using data from the tributaries of the Nakdong River. Sensitivity analyses were conducted using the developed model to understand how the confluence delta responds to changes in the sediment and water discharges of the tributary and the main channel, respectively. We then used annual average discharge of the Nakdong River's tributaries to predict the dynamic equilibrium positions of the confluence deltas. Finally, we conducted a simulation experiment on the development of the Gamcheon-Nakdong River delta using recorded daily discharge. The results showed that even though it is a simple model, it accurately predicted the dynamic equilibrium positions of the confluence deltas in the Nakdong River, including the areas where the delta had not formed, and those where the delta had already formed and predicted the trend of the response of the Gamcheon-Nakdong River delta. However, the actual retreat in the Gamcheon-Nakdong River delta was not captured fully due to errors and limitations in the simplification process. The insights through this study provide basic information on the sediment supply of the Nakdong River through the confluence areas, which can be implemented as a basic model for river maintenance and management.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.113-129
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• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.09a
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• pp.21-36
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• 2005

Although the main geology of Korea consists of granite and gneiss, it Is not uncommon to encounter anisotropy Phenomena in crosshole radar tomography even when the basement is crystalline rock. To solve the anisotropy Problem, we have developed and continuously upgraded an anisotropic inversion algorithm assuming a heterogeneous elliptic anisotropy to reconstruct three kinds of tomograms: tomograms of maximum and minimum velocities, and of the direction of the symmetry axis. In this paper, we discuss the developed algorithm and introduce some case histories on the application of anisotropic radar tomography in Korea. The first two case histories were conducted for the construction of infrastructure, and their main objective was to locate cavities in limestone. The last two were performed In a granite and gneiss area. The anisotropy in the granite area was caused by fine fissures aligned in the same direction, while that in the gneiss and limestone area by the alignment of the constituent minerals. Through these case histories we showed that the anisotropic characteristic itself gives us additional important information for understanding the internal status of basement rock. In particular, the anisotropy ratio defined by the normalized difference between maximum and minimum velocities as well as the direction of maximum velocity are helpful to interpret the borehole radar tomogram.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.