Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Estimation of Local Scour at Piers Using Artificial Neural Network

인공신경망을 이용한 피어의 국부세굴 평가

  • Published : 2008.11.30
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Abstract

It is known that scour at bridge piers is one of the leading causes of bridge failure. However, the mechanism of flow around a pier structure is so complicated that it is difficult to establish a general empirical model to provide accurate estimation for scour. Especially, each of the proposed empirical formula yields good results for a particular data set but can't show reliable predictability for various scouring data set. In this study, an alternative approach, that is, artificial neural networks (ANN), is proposed to estimate the local scour depth with numerous field data base. The local scour depth was modeled as a function of seven variables; pier shape, pier width, pier length, skew angle, stream velocity, water depth, $D_{50}$. 426 field data were used for the training and testing of ANN model. The predicted results showed that the neural network could provide a better alternative to the empirical equations.

하천 교각에서 유발되는 국부세굴은 교량의 붕괴를 유발하는 요인들 가운데 하나로 알려져 있다. 그러나, 교각주위 하천 흐름은 매우 복잡하기 때문에 국부세굴을 정확하게 산정하는 경험식을 도출하기가 쉽지 않다. 따라서, 기존의 경험식들은 특정 세굴 자료에는 좋은 상관 관계를 보이지만 다양한 현장 세굴자료들에 대해 신뢰성 있는 예측 정도를 갖기는 어렵다. 본 연구에서는 많은 현장 계측자료를 바탕으로 국부세굴심을 산정할 수 있는 인공신경망 모델을 제안하고자 하였다. 제안된 산정식은 교각 형상, 교각 폭, 교각 길이, 흐름 입사각, 흐름 속도, 수심 및 $D_{50}$의 총 7개의 변수의 함수로 구성되었다. 인공신경망 모델의 학습과 검증에 총 426개의 현장 계측자료들이 사용되었으며, 인공신경망 모델이 기존 경험 식들에 비하여 개선된 예측정도를 보임을 확인하였다.

Keywords

References

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