• 상하수도학회지
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• 제34권1호
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• 대한토목학회논문집
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• 제26권4B호
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• pp.389-398
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• 2006

본 연구에서는 강우의 시 공간적 분포의 불규칙한 변동성을 고려한 강우-유출예측모형을 위해 인공신경망(Artificial Neural Networks: ANNs)의 기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론과 역전파 학습 알고리즘(Back Propagation Algorithm: BPA을 복합적으로 이용하였다. 기존의 인공신경망 연구에서 야기된 저 갈수기의 유출량에 대한 과대평가, 홍수기의 유출량에 대한 과소평가, 예측값이 연속적으로 선행 유출량을 나타내는 Persistence 현상을 해결하기 위하여 패턴분류 성능을 지닌 SOM 이론을 예측모형의 전처리 과정으로 이용하였다. 먼저, 본 연구에서 제안한 방법은 SOM에 의해 강우-유출 관계를 분류하고, SOM에 의한 분류에 따라 각각의 모형을 구성한다. 개별적으로 구축된 모형은 유출량의 예측을 위해 각각의 양상에 따라 분류된 자료를 이용한다. 결과적으로 본 연구에서 제안한 방법은 과거의 인공신경망의 일반적인 적용에 의한 결과보다 더 나은 예측능력을 보여주었으며, 더불어 유출량의 과소 및 과대추정과 Persistence 현상과 같은 문제점이 나타나지 않았다.

• 한국항해항만학회지
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• 제32권1호
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• pp.81-88
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• 2008

컨테이너항만의 물동량 예측은 항만의 개발 및 운영계획을 위해 매우 중요한 과정이다. 일반적으로 회귀분석, ARIMA모형 등의 통계적 방법론을 통해 많은 예측이 이뤄져왔다. 최근의 연구에서는 인공 신경망(ANN)기법을 통한 예측이 이뤄지고 있으며 기존의 선형적인 기법을 대신하고 있다. 본 연구에서는 선형모형과 비선형모형에 강점이 있는 ARIMA모형과 신경망모형을 결합해 보다 효과적인 예측 모형을 개발하고자 한다. 실제 항만의 과거 자료를 통해 모델의 적합성을 측정하였고 항만의 특성에 따라 모형의 적합성이 다양하게 나타났다.

• 한국지반신소재학회논문집
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• 제18권4호
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• pp.39-52
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• 2019

In this paper the development and implementation of an artificial intelligence (AI)-based Tunnelling Impact prediction and assessment program (SKKU-iTunnel) is presented. Program predicts tunnelling induced surface settlement and groundwater drawdown by utilizing well trained ANNs and uses these predicted values to perform the damage assessment likely to occur in nearby structures and pipelines/utilities for a given tunnel problem. Generalised artificial neural networks (ANNs) were trained, to predict the induced parameters, through databases generated by combining real field data and numerical analysis for cases that represented real field conditions. It is shown that program equipped with carefully trained ANN can predict tunnel impact assessments and perform damage assessments quiet efficiently and comparable accuracy to that of numerical analysis. This paper describes the idea and implementation details of the SKKU-iTunnel with an example for demonstration.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2007년도 추계학술대회 및 제23회 정기총회
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• pp.259-260
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• 2007

컨테이너항만의 물동량 예측은 항만의 계발 및 운영계획을 위해 매우 중요한 과정이다. 일반적으로 회귀분석, ARIMA 등의 통계적 방법론을 통해 많은 예측이 이뤄져왔다. 최근의 연구에서는 인공 신경망(ANN)기법을 통한 예측이 이뤄지고 있으며 기존의 선형적인 기법을 대신하고 있다. 본 연구에서는 선형모델과 비선형모델에 강점이 있는 ARIMA와 신경망 모델을 결합해 보다 효과적인 예측 모델을 개발하고자 한다. 실제 항만의 과거 자료를 통해 모델의 적합성을 측정하였고 항만의 특성에 따라 모형의 적합성이 다양하게 나타났다.

• 정보처리학회논문지B
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• 제9B권4호
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• pp.407-414
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• 2002

최근 자신의 건강에 관한 관심이 고조됨에 따라 웹 상에서 많은 증상 진단 사이트들이 대두되고 있다. 그러나 기존의 건강정보 사이트들은 사용자에게 매우 제한된 기능만을 제공하고 있다. 본 논문에서는 신경망의 학습 효과를(전문가의 지식이 아니라) 진단 과정에 통합되도록 함으로써 유연한 증상-진단 도구를 제안한다. 즉 사용자(흑은 전문가)가 웹 상에서 단계별로 지정한 증상들을 바탕으로 하여 신경망 모델에 적용함으로써 보다 유연하게 사용자의 질병을 예측할 수 있는 새로운 알고리즘을 개발한다. 제안한 알고리즘은 두 가지 중요한 특징을 가진다 : 1) 일반 사용자들은 조기에 자신의 질병에 대한 진단을 받을 수 있고, 2) 전문가는 예상 질병 목록과 함께 각 질병의 가능성(확률)을 참조함으로써 진단의 정확성을 높일 수 있다는 점이다.

• Structural Engineering and Mechanics
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• 제21권2호
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• pp.205-220
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• 2005

This paper presents the possibilities of adapting artificial neural networks (ANNs) to predict the dimensionless parameters related to the maximum contact pressures of an elasticity problem. The plane symmetric double receding contact problem for a rigid stamp and two elastic strips having different elastic constants and heights is considered. The external load is applied to the upper elastic strip by means of a rigid stamp and the lower elastic strip is bonded to a rigid support. The problem is solved under the assumptions that the contact between two elastic strips also between the rigid stamp and the upper elastic strip are frictionless, the effect of gravity force is neglected and only compressive normal tractions can be transmitted through the interfaces. A three layered ANN with backpropagation (BP) algorithm is utilized for prediction of the dimensionless parameters related to the maximum contact pressures. Training and testing patterns are formed by using the theory of elasticity with integral transformation technique. ANN predictions and theoretical solutions are compared and seen that ANN predictions are quite close to the theoretical solutions. It is demonstrated that ANNs is a suitable numerical tool and if properly used, can reduce time consumed.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.987-997
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• 2008

Successful design, construction and maintenance of geotechnical structure in soft ground and marine clay demands prediction, control, stability estimation and monitoring of settlement with high accuracy. It is important to predict and to estimate the compression index of soil for predicting of ground settlement. Lab. and field tests have been and are indispensable tools to achieve this goal. In this paper, Artificial Neural Networks (ANNs) model with Levenberg-Marquardt Algorithm and field database were used to predict compression index of soil in Korea. Based on soil property database obtained from more than 1800 consolidation tests from soils samples, the ANNs model were proposed in this study to estimate the compression index, using multiple soil properties. The compression index from the proposed ANN models including multiple soil parameters were then compared with those from the existing empirical equations.

• Structural Engineering and Mechanics
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• 제54권2호
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• pp.337-362
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• 2015

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

• Steel and Composite Structures
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• 제36권5호
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• pp.493-506
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• 2020

The load-slip relationship of the shear connection is an important parameter in design and analysis of composite structures. In this paper, a load-slip curve prediction method of the shear connection based on the artificial neural networks (ANNs) is proposed. The factors which are significantly related to the structural and deformation performance of the connection are selected, and the shear stiffness of shear connections and the transverse coordinate slip value of the load-slip curve are taken as the input parameters of the network. Load values corresponding to the slip values are used as the output parameter. A twolayer hidden layer network with 15 nodes and 10 nodes is designed. The test data of two different forms of shear connections, the stud shear connection and the perforated shear connection with flange heads, are collected from the previous literatures, and the data of six specimens are selected as the two prediction data sets, while the data of other specimens are used to train the neural networks. Two trained networks are used to predict the load-slip curves of their corresponding prediction data sets, and the ratio method is used to study the proximity between the prediction loads and the test loads. Results show that the load-slip curves predicted by the networks agree well with the test curves.