• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.519-527
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• 2020

Dynamic analysis of a vehicle-track coupling system is important to structural design, damage detection and condition assessment of the structural system. Deterministic analysis of the vehicle-track coupling system has been extensively studied in the past, however, the structural parameters of the coupling system have uncertainties in engineering practices. It is essential to treat the parameters of the vehicle-track coupling system with consideration of uncertainties. In this paper, a method for predicting the bounds of the vehicle-track coupling system responses with uncertain parameters is presented. The uncertain system parameters are modeled as fuzzy variables instead of conventional random variables with known probability distributions. Then, the dynamic response functions of the coupling system are transformed into a component function based on the high dimensional representation approximation. The Lagrange interpolation method is used to approximate the component function. Finally, the bounds of the system's dynamic responses can be predicted by using Monte Carlo method for the interpolation polynomials of the Lagrange interpolation function. A numerical example is introduced to illustrate the ability of the proposed method to predict the bounds of the system's dynamic responses, and the results are compared with the direct Monte Carlo method. The results show that the proposed method is effective and efficient to predict the bounds of the system's dynamic responses with fuzzy variables.

• Computers and Concrete
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• v.2 no.4
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• pp.325-343
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• 2005

Chloride ingress is a common cause of deterioration of reinforced concrete located in coastal zone. Modeling the chloride ingress is an important basis for designing reinforced concrete structures and for assessing the reliability of an existing structure. The modeling is also needed for predicting the deterioration of a reinforced structure. The existing deterministic solution for prediction model of corrosion initiation cannot reflect uncertainties which input variables have. This paper presents an approach to the fuzzy arithmetic based modeling of the chloride-induced corrosion of reinforcement in concrete structures that takes into account the uncertainties in the physical models of chloride penetration into concrete and corrosion of steel reinforcement, as well as the uncertainties in the governing parameters, including concrete diffusivity, concrete cover depth, surface chloride concentration and critical chloride level for corrosion initiation. There are a lot of prediction model for predicting the time of reinforcement corrosion of structures exposed to chloride-induced corrosion environment. In this work, RILEM model formula and Crank's solution of Fick's second law of diffusion is used. The parameters of the models are regarded as fuzzy numbers with proper membership function adapted to statistical data of the governing parameters instead of random variables of probabilistic modeling of Monte Carlo Simulation and the fuzziness of the time to corrosion initiation is determined by the fuzzy arithmetic of interval arithmetic and extension principle. An analysis is implemented by comparing deterministic calculation with fuzzy arithmetic for above two prediction models.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3021-3023
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• 1999

엄격한 시간 제약성에 의해 특성화되는 실시간 시스템의 성능을 평가하기 위해서 퍼지-랜덤 변수가 포함된 이산 사건 모델을 제시한다. 실시간 시스템의 정확성은 출력의 논리적 결과 뿐 아니라 반응시간에도 의존하므로, 본 논문에서는 실시간 시스템의 성능을 유연하게 평가하기 위해서 퍼지-랜덤 변수에 의해 적절하게 변형된 상태 오토마타를 제시하고 그 오토마타를 적용한 수치 예제를 제시한다.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1100-1102
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• 1997

This paper presents a generalized multi-parameter distribution method for the convolution of linear combination of random variables to calculate system load flow in a conventional probabilistic approach and also presents a conceptual possibilistic approach using fuzzy set theory to manage uncertainties. The probability distribution function is transformed into an appropriate possibilistic representation under the compromise between the transformation consistency and the human updating experience. The IEEE 25-bus system is used to demonstrate the capability of the proposed algorithm.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Economic and Environmental Geology
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• v.41 no.6
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• pp.763-771
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• 2008

Uncertainties are pervasive in engineering geological problems. Therefore, the presence of uncertainties and their significance in analysis and design of slopes have been recognized. Since the uncertainties cannot be taken into account by the conventional deterministic approaches in slope stability analysis, the probabilistic analysis has been considered as the primary tool for representing uncertainties in mathematical models. However, some uncertainties are caused by incomplete information due to lack of information, and those uncertainties cannot be handled appropriately by the probabilistic approach. For those uncertainties, the theory of fuzzy sets is more appropriate. Therefore, in this study, fuzzy reliability analysis has been proposed in order to deal with the uncertainties which cannot be quantified in the probabilistic analysis due to the limited information. For the practical example, a slope is selected in this study and both the probabilistic analysis and the fuzzy reliability analysis have been carried out for planar failure. In the fuzzy reliability analysis, the dip angle and internal friction angle of discontinuity are considered as triangular fuzzy numbers since the random properties of the variables cannot be obtained completely under the conditions of limited information. In the study, the fuzzy reliability index and the probabilities of failure are evaluated from fuzzy arithmetic and compared to those from the probabilistic approach using Monte Carlo simulation and point estimate method. The analysis results show that the fuzzy reliability analysis is more appropriate for the condition that the uncertainties arise due to incomplete information.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.683-690
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• 2012

In this work, the semi-active control of a large-sized bus suspension system with an MR damper was studied. An MR damper model that can aptly describe the hysteretic characteristics of an MR damper was adopted. Parameter values of the MR damper model were suitably modified by considering the maximum damping force of a passive damper used in the suspension system of a real large-sized bus. In addition, a fuzzy logic controller was developed for semi-active control of a suspension system with an MR damper. The vertical acceleration at the attachment point of the MR damper and the relative velocity between sprung and unsprung masses were used as input variables, while voltage was used as the output variable. Straight-ahead driving simulations were performed on a road with a random road profile and on a flat road with a bump. In straight-ahead driving simulations, the vertical acceleration and pitch angle were measured to compare the riding performance of a suspension system with a passive damper with that of a suspension with an MR damper. In addition, a single lane change simulation was performed. In the simulation, the lateral acceleration and roll angle were measured in order to compare the handling performance of a suspension system using a passive damper with that of a suspension system using an MR damper.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.257-263
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• 2022

The objective of this study was to see how artificial intelligence applications affected organizational innovation in Jordanian commercial banks. Both independent and dependent variables were measured in three dimensions: expert systems, neural network systems, and fuzzy logic systems for artificial intelligence applications variable. Product innovation, process innovation, and management innovation for the organizational innovation variable. To achieve study objectives, a questionnaire was developed and distributed to a sample of one hundred fifty-three managers in Jordanian commercial banks, who were selected according to the simple random sampling method. Except for the neural network systems dimension, which comes in at an average level, the study indicated that there is a high level of organizational innovation and artificial intelligence applications. Furthermore, the findings revealed that artificial intelligence applications have a significant impact on organizational innovation in Jordanian commercial banks, with the most important artificial intelligence application being a fuzzy logic system. The study suggested keeping track of technological advancements in the field of artificial intelligence applications and incorporating them into banking operations by benchmarking with the best commercial bank practices and allocating a portion of the budget to technological applications and infrastructure development, as well as balancing between technology use and information security risks to ensure client privacy is protected.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.794-796
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• 1999

엄격한 시간 제약성에 의해 특성화되는 실시간 전력 시스템의 성능 및 신뢰도를 평가하기 위해서 퍼지-랜덤 변수가 포함된 이산 사건 모델 및 확장된 path-space 기법을 제시한다. 실시간 시스템의 정확성은 출력의 논리적 결과 뿐 아니라 반응시간에도 의존하므로, 본 논문에서는 실시간 전력 시스템의 성능 및 신뢰도를 유연하게 평가하기 위해서 퍼지-랜덤 변수에 의해 적절하게 변형된 상태 오토마타를 제시하고 몇가지 수치 예제를 제시함으로써 제안한 기법의 효용성을 검증한다.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.681-686
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• 2016

Software defect prediction is an important factor in efficient project management and success. The severity of the defect usually determines the degree to which the project is affected. However, existing studies focus only on the presence or absence of a defect and not the severity of defect. In this study, we proposed an ensemble model using FCM based on defect severity. The severity of the defect of NASA data set's PC4 was reclassified. To select the input column that affected the severity of the defect, we extracted the important defect factor of the data set using Random Forest (RF). We evaluated the performance of the model by changing the parameters in the 10-fold cross-validation. The evaluation results were as follows. First, defect severities were reclassified from 58, 40, 80 to 30, 20, 128. Second, BRANCH_COUNT was an important input column for the degree of severity in terms of accuracy and node impurities. Third, smaller tree number led to more variables for good performance.