• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• The Journal of Society for e-Business Studies
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• v.14 no.2
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• pp.23-39
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• 2009

Increasing number of business and technology expansion has sparked a growing interest in IT service business evaluation. However, it is not an easy task to come up with a fair and objective evaluation of IT service business due to the difficulties involved in the definition performance assets (marketing, human resources etc.) and knowledge assets with respect to its industry. Several public organizations in Korea are developing a "standardized evaluation protocol" based on qualitative method. But the standard evaluation protocol does not provide suitable guidelines on how to construct and evaluate the key index of IT service business. The main objective of this study is in the development of a systematic approach for the evaluation of IT service business competitiveness by emphasizing the qualitative and quantitative index to be evaluated in the framework. Application of the developed framework and guideline format showed that the used of the framework in this study provided relatively more efficient evaluation results in IT service industry.

• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.16-27
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• 2022

This study analyzed both the legal system regarding fair trade and the types of unfair trade in the construction industry. Then, it derived the factors with which to evaluate the level of fairness. These factors were classified by each type of participant in the construction industry, such as general contractors and subcontractors, and each construction stage, such as bidding, contracting, and construction. The perceived fairness level of factors was analyzed using a survey of 238 employees of general contractors and subcontractors. Next, the study developed a fairness index for the construction industry. The index showed that subcontractors perceived the level of fairness more negatively than general contractors, but both perceived the construction stage of the industry as having the lowest level of fairness. General contractors perceived the bidding and contracting stages as having the highest fairness levels, and subcontractors perceived the contracting stage as having the highest level of fairness. The developed fairness index identified the evaluation factors that need improvement and the fairness level perceived by each type of participant at each stage of construction. The results of this study can contribute to establishing measures that improve the level of fairness in the construction industry.

• Atmosphere
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• v.33 no.5
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• pp.519-530
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• 2023

Deep convection can make adverse effects on safe and efficient aviation operations by causing various weather hazards such as convectively-induced turbulence, icing, lightning, and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distribution of deep convective area near the airport and airspace. This study developed a new index, the Aviation Convective Index (ACI), for deep convection, using the operational global Unified Model of the Korea Meteorological Administration. The ACI was computed from combination of three different variables: 3-hour maximum of Convective Available Potential Energy, averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logic algorithm. In this algorithm, the individual membership function was newly developed following the cumulative distribution function for each variable in Korean Peninsula. This index was validated and optimized by using the 1-yr period of radar mosaic data. According to the Receiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimized ACI (ACI_YrOpt) based on the optimal weighting coefficients for 1-yr period shows a better skill than the no optimized one (ACI_NoOpt) with the uniform weights. In all forecast time from 6-hour to 48-hour, the AUC and TSS value of ACI_YrOpt were higher than those of ACI_NoOpt, showing the improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.

• International Journal of Safety
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• v.4 no.2
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• pp.12-17
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• 2005

This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.87-91
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• 2011

강수는 지역에 따라 발생양상이 매우 다른 자연현상 중 하나이다. 이러한 강수를 효과적으로 분석하여 확률강수량을 산정하기위해서 수문학에서는 다양한 방법이 시도되어 왔다. 우리나라에서는 지점빈도해석을 통한 확률강수량을 주로 사용해왔으나 최근 들어 Hosking and Wallis(1997)가 제안한 지역빈도해석을 활용을 적극 도모 하고 있는 중이다. 이러한 지역빈도해석 기법은 지점빈도해석 기법에 비하여 한정된 강수자료를 활용하는 측면 등 여러 가지 장점을 가진 확률 강수량 산정방법이다. 그러나 이 기법을 적용하여 확률강수량을 산정하기 위해서는 강수의 지역구분을 먼저 수행하여야 한다. 강수지역의 구분을 위해서는 여러 가지 기법이 존재하나 최근에는 Cluster 기법 중 K-means 방법이나 Fuzzy c-means 방법 등을 주로 적용하여 지역구분을 수행하고 있다. 그러나 K-means 방법이나 Fuzzy c-means 방법 등은 산정 방법내에서 최적 군집수를 결정할 수 있는 알고리즘이 없기 때문에 임의적으로 최적 군집수를 결정하여야 한다. 본 연구에서는 이러한 단점을 극복하기 위하여 Cluster 평가지수 중 하나인 Dunn 지수를 이용하여 최적 군집수를 제시하고자 한다. 본 연구에서 강수지역을 구분하기 위하여 적용한 인자는 월 평균 강수량, 연 평균 강수량, 월 최대 강수량, 경도, 위도, 고도 등이며, 이를 K-means, PAM 및 친근도 전파 기법을 통하여 강수지역을 구분하였다. 적정 군집수를 임의적으로 증가시켜 가면서 Dunn 지수를 산정하였다. 산정된 결과를 통하여 최적 군집수를 결정하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.312-316
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• 2010

현재 국내에서 주로 사용되고 있는 홍수기 다목적 댐의 홍수조절효과에 대한 정량적인 평가지표로는 유량조절률, 저수지 방류율, 저수지 저류율, 저수지 이용률 등이 있다. 이러한 평가지표들은 유입량, 방류량, 저류량 등의 자료를 단순 비교하는 방법을 사용하고 있는데, 홍수조절효과 평가지표 산정식에 사용되는 자료들이 가지는 불확실성이 평가에 고려되지 못하고 있으며, 다목적댐에서 얻을 수 있는 자료만을 사용함으로써 댐 하류 지점에서의 홍수조절효과를 평가하지 못하고 있다. 또한 수자원 시스템의 설계에 있어서 허용 가능한 부분적인 실패를 고려하지 못하는 등의 문제점이 존재하므로, 홍수조절효과를 정량화 할 수 있는 새로운 지표의 개발이 요구된다. 본 연구에서는 각종 변수들이 가지는 불확실성, 댐 하류지점에서의 홍수조절효과, 수자원 시스템에서 허용 가능한 부분적 실패를 고려하기 위하여 홍수조절효과 정량화 지표 개발에 퍼지집합 이론을 적용하였고, 충주댐 유역을 시험 유역으로 선정하여 연구를 수행하였다. 대상 홍수사상으로는 2006년도 7월의 홍수사상을 적용하였으며 그에 따른 인자들을 퍼지화하고 시스템의 상태로부터 허용 가능한 부분적인 실패 영역을 구분하였고, 통합 신뢰도-취약도 지수를 적용하여 홍수조절효과 정량화 지표를 개발하였다. 적용 결과, 본 연구를 통하여 개발된 통합 신뢰도-취약도 지수는 저수지의 홍수조절효과를 보다 구체적이고 객관화하여 나타낼 수 있었다.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.2
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• pp.112-122
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• 2012

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.