• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 춘계 학술발표회
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• pp.456-463
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• 2009

The preconsolidation pressure has been commonly determined by oedometer test. However, it can also be determined by in-situ test, such as piezocone test with theoretical and(or) empirical correlations. Recently, Neural Network(NN) theory was applied and some models were proposed to estimate the preconsolidation pressure or OCR. However, since the optimization process of synaptic weights of NN model is dependent on the initial synaptic weights, NN models which are trained with different initial weights can't avoid the variability on prediction result for new database even though they have same structure and use same transfer function. In this study, Committee Neural Network(CNN) model is proposed to improve the initial weight dependency of multi-layered neural network model on the prediction of preconsolidation pressure of soft clay from piezocone test result. It was found that even though the NN model has the optimized structure for given training data set, it still has the initial weight dependency, while the proposed CNN model can improve the initial weight dependency of the NN model and provide a consistent and precise inference result than existing NN models.

• 한국소음진동공학회논문집
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• 제23권4호
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• pp.347-355
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• 2013

A damage estimation method for monopile support structure of offshore wind turbine using modal properties and committee of neural networks is presented for effective structural health monitoring. An analytical model for a monopile support structure is established, and the natural frequencies, mode shapes, and mode shape slopes for the support structure are calculated considering soil condition and added mass. The input to the neural networks consists of the modal properties and the output is composed of the stiffness indices of the support structure. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated stiffness indices from different neural networks are averaged. Ten damage cases are estimated using the proposed method, and the identified damage locations and severities agree reasonably well with the exact values. The accuracy of the estimation can be improved by applying the committee of neural networks which is a statistical approach averaging the damage indices in the functional space.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.320-324
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• 2013

A crack identification method using an equivalent bending stiffness for cracked beam and committee of neural networks is presented. The equivalent bending stiffness is constructed based on an energy method for a straight thin-walled pipe, which has a through-the-thickness crack, subjected to bending. Several numerical analysis for a steel cantilever pipe using the equivalent bending stiffness are carried out to extract the natural frequencies and mode shapes of the cracked beam. The extracted modal properties are used in constructing a training patterns of a neural network. The input to the neural network consists of the modal properties and the output is composed of the crack location and size. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated crack locations and sizes from different neural networks are averaged. Experimental crack detection is carried out for 3 damage cases using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.

• 한국관개배수논문집
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• 제7권2호
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• pp.98-108
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• 2000

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2006년 창립20주년기념 정기학술대회 및 국제워크샵
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• pp.213-216
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• 2006

During the last decade several different methods have been proposed for the estimation of thermal deformations in the line heating process. These are mainly based on the assumption of residual strains in the heat-affected zone or simulated relations between heating conditions and residual deformations. However these results were restricted in the application from the too simplified heating conditions or the shortage of the data. The purpose of this paper is to develop a simulator of thermal deformation in the line heating using the artificial neural network. Two neural network predicting the maximum temperature and deformations at the heating line are studied. Deformation data from the line heating experiments are used for learning data for the network. It was observed that thermal deformation predicted by the neural network correlate well with the experimental result.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2001년도 추계학술대회 논문집
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• pp.91-96
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• 2001

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamics, and a sliding mode control is introduced to attenuate the effects of the neural network's reconstruction errors and the disturbances of AUV's dynamics. The presented controller is consist of three parallel schemes; linear feedback control, sliding mode control and neural network. Lyapunov theory is used to guarantee the asymptotic convergence of trajectory tracking errors and the neural network's weights errors. Numerical simulations for motion control of an AUV are performed to illustrate to effectiveness of the proposed techniques.

• Computers and Concrete
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• 제21권1호
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• pp.103-110
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• 2018

This study introduces the supervised committee fuzzy model as a hybrid fuzzy model to predict compressive strength (CS) of geopolymers prepared from alumina-silica products. For this purpose, more than 50 experimental data that evaluated the effect of $Al_2O_3/SiO_2$, $Na_2O/Al_2O_3$, $Na_2O/H_2O$ and Na/[Na+K] on (CS) of geopolymers were collected from the literature. Then, three different Fuzzy Logic (FL) models (Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL)) were adopted to overcome the inherent uncertainty of geochemical parameters and to predict CS. After validating the model, it was found that the SFL model is superior to MFL and LFL models, but each of the FL models has advantages to predict CS. Therefore, to achieve the optimal performance, the supervised committee fuzzy logic (SCFL) model was developed as a hybrid method to combine the benefits of individual FL models. The SCFL employs an artificial neural network (ANN) model to re-predict the CS of three FL model predictions. The results also show significant fitting improvement in comparison with individual FL models.

• 대한토목학회논문집
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• 제29권3C호
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• pp.115-125
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• 2009

지반의 응력이력을 정의하는데 이용되는 선행압밀하중은 일반적으로 일차원 실내압밀실험으로부터 결정되어져 왔으나 피에조콘과 같은 원위치 시험의 관측값을 이용한 이론적인 방법과 경험적인 상관관계를 통한 결정도 가능하다. 최근 선행압밀하중을 결정하기 위한 인공신경망 모델들이 제안된 바 있으며, 기존의 이론적 경험적 선행압밀하중 추정 방법들이 갖는 지역의존성의 문제를 극복하고 예측 정확도 면에서도 크게 개선된 것으로 보고되었다. 그러나 인공신경망 모델은 모델구조와 학습과정에서 초기에 무작위로 부여되는 연결강도에 영향을 받아 예측에 변동성이 존재한다. 본 연구에서는 기존의 피에조콘 결과를 이용한 선행압밀하중 추정 인공신경망 모델이 연약지반에서 선행압밀하중 예측 시 보이는 변동성을 개선하기 위하여 신경망 모델의 구조 최적화를 수행하고 군집신경망 모델을 구축하였다. 제안된 군집신경망 모델을 이용한 예측결과는 기존의 다층신경망 모델 및 이론적 경험적 모델들과 비교되었다. 연구결과, 최적화된 구조를 갖는 다층신경망 모델일지라도 초기 연결강도에 따라 최종 학습 후 예측결과의 변동성이 여전히 존재하나, 다층신경망을 네트워크로 연결하여 제안된 군집신경망 모델은 기존의 다층신경망 모델들이 갖는 초기 연결강도 의존성을 개선하여 다층신경망 모델에 비해 일관성 있으며 보다 정확한 예측이 가능한 것으로 나타났다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.492-495
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• 2003

Fuzzy Min-Max Neural Network(FMMNN) is a powerful classifier, It has, however, some problems. Learning result depends on the presentation order of input data and the training parameter that limits the size of hyperbox. The latter problem affects the result seriously. In this paper, the new approach to alleviate that without loss of on-line learning ability is proposed. The committee machine is used to achieve the multi-resolution FMMNN. Each expert is a FMMNN with fixed training parameter. The advantages of small and large training parameters are used at the same time. The parameters are selected by performance and independence measures. The Decision of each expert is guided by the gating network. Therefore the regional and parametric divide and conquer scheme are used. Simulation shows that the proposed method has better classification performance.

• 한국지반공학회논문집
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• 제23권7호
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• pp.65-76
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• 2007

최근 급속한 경제발전과 지역산업의 성장으로 인하여 많은 물류이동 발생과 함께 연약지반에 도로를 건설하는 경우가 많아지면서 연약지반 상에 축조된 지반구조물과 관련한 제반 문제점들이 대두되고 있다. 특히 말뚝 기초 형식의 교대나 건축물을 연약지반에 시공할 경우 비정상적인 측방유동에 의한 변위가 기초지반에 발생하여 상부 구조물의 안정성과 사용성에 많은 문제를 야기하고 있다. 측방유동은 말뚝의 파손원인과 측방변위에 대한 상관관계 연구, 연약지반 상에 설치된 말뚝의 변형과 모멘트에 대한 연구, 수치해석법을 이용한 연약지반상의 성토에 따른 측방변위 특성 및 현장계측을 통한 측방변위 특성 등 많은 연구가 수행되어지고 있으나 측방유동현상은 지금까지도 그 역학적 메커니즘이 정량적으로 파악하기 어렵고, 측방유동에 대한 합리적인 설계법이 확립되어 있지 않는 실정이다. 본 연구에서는 국내 및 일본 측방유동 발생 사례를 바탕으로 효과적이고 보다 정확한 측방유동 판정을 위하여 확률신경망과 군집신경망이론을 이용한 평가모델을 구축하고 기존의 측방유동 판정식과 비교하였다. 연구결과 제안된 확률신경망과 군집신경망 모델들의 측방유동 판정 성공률이 기존의 판정식들에 비해 매우 높은 정확도를 가지며 효과적인 것으로 나타났다.