Journal of the Korean Geotechnical Society (한국지반공학회논문집)

Korean Geotechnical Society (한국지반공학회)
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Prediction of Shear Wave Velocity on Sand Using Standard Penetration Test Results : Application of Artificial Neural Network Model

표준관입시험결과를 이용한 사질토 지반의 전단파속도 예측 : 인공신경망 모델의 적용

  • Received : 2013.12.31
  • Accepted : 2014.03.05
  • Published : 2014.05.31
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Abstract

Although shear wave velocity ($V_s$) is an important design factor in seismic design, the measurement is not usually made in typical field investigation due to time and economic limitations. In the present study, an investigation was made to predict sand $V_s$ based on the standard penetration test (SPT) results by using artificial neural network (ANN) model. A total of 650 dataset composed of SPT-N value ($N_{60}$), water content, fine content, specific gravity for input data and $V_s$ for output data was used to build and train the ANN model. The sensitivity analysis was then performed for the trained ANN to examine the effect of the input variables on the $V_s$. Also, the ANN model was compared with seven existing empirical models on the performance. The sensitivity analysis results revealed that the effect of the SPT-N value on $V_s$ is significantly greater compared to other input variables. Also, when compared with the empirical models using Nash-Sutcliffe Model Efficiency Coefficient (NSE) and Root Mean Square Error (RMSE), the ANN model was found to exhibit the highest prediction capability.

전단파 속도는 내진설계시 중요한 설계인자이나 지반조사의 목적으로는 흔히 경제적, 시간적 제약 등으로 시험을 통한 측정이 널리 이루어지지 않고 있다. 본 연구에서는 인공신경망 기법을 이용하여 가장 일반적인 현장 지반조사시험인 표준관입시험 결과를 바탕으로 사질토 지반에서의 전단파 속도를 예측하는 연구를 수행하였다. 650개 데이터 세트를 이용해 표준관입시험 저항치 $N_{60}$, 함수비, 세립분함량, 비중을 입력변수로 하여 전단파속도를 추정하는 인공신경망 모델을 구축하고 입력변수별 전단파속도에 미치는 영향을 민감도 해석을 통해 조사하였다. 그리고, 기존의 국내 외 7개의 표준관입시험을 이용한 전단파속도 예측 경험식들과 인공신경망에 의한 결과를 비교하였다. 민감도 분석결과 표준관입시험 저항치의 영향이 월등히 큰 것으로 나타났으며, 모델효율계수와 평균제곱근오차를 사용하여 기존의 경험식들과 인공신경망 모델의 예측 능력을 비교한 결과 인공신경망 모델의 예측 결과가 가장 좋은 것으로 나타났다.

Keywords

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