[논문]다층 퍼셉트론 신경망을 이용한 사면원호 파괴 예측

마지훈; 윤태섭

doi:10.7843/kgs.2022.38.8.39

다층 퍼셉트론 신경망을 이용한 사면원호 파괴 예측
Prediction of Slope Failure Arc Using Multilayer Perceptron 원문보기

韓國地盤工學會論文集 = Journal of the Korean geotechnical society, v.38 no.8, 2022년, pp.39 - 52

마지훈 (연세대학교 건설환경공학과) , 윤태섭 (연세대학교 건설환경공학과)

초록
AI-Helper

사면의 안전율과 임계활동면을 다층 퍼셉트론 신경망(multi-layer perceptron, MLP)을 이용하여 구할 수 있도록 훈련하였다. 사면의 형상은 한국의 설계기준을 참고한 단순 사면으로, 건조한 경우와 지하수위가 존재하는 경우를 모두 고려하였으며 사면을 구성하는 토질의 물성은 세립분을 포함한 사질토로 고려하였다. 훈련에 필요한 데이터를 만들 때 한계평형해석법을 이용하여 42,000가지 경우의 사면안정해석을 수행하였고, 지하수위가 고려된 도메인의 해석에서 불포화토의 모관흡수력으로 인한 유효응력 증가를 고려하였다. 지하수와 유효응력의 분포를 사면안정해석에 적용할 수 있도록 정상상태 침투 해석을 수행하였다. 사면을 표현하는 물성을 입력하면 안전율과 원호 파괴면을 예측할 수 있는 MLP 모델과 모델의 성능을 정량적으로 평가할 수 있는 방법을 제시하였다.

Abstract ▼ AI-Helper

Multilayer perceptron neural network was trained to determine the factor of safety and slip surface of the slope. Slope geometry is a simple slope based on Korean design standards, and the case of dry and existing groundwater levels are both considered, and the properties of the soil composing the slope are considered to be sandy soil including fine particles. When curating the data required for model training, slope stability analysis was performed in 42,000 cases using the limit equilibrium method. Steady-state seepage analysis of groundwater was also performed, and the results generated were applied to slope stability analysis. Results show that the multilayer perceptron model can predict the factor of safety and failure arc with high performance when the slope's physical properties data are input. A method for quantitative validation of the model performance is presented.

주제어

표/그림 (15)

그림 Fig. 1. Distribution of input parameters used to generate slope stability analysis data to be used for training the MLP model: (a) slope height (H), (b) cotangent of slope angle (β), (c) groundwater height normalized to slope height (H_w*) (d) dry unit weight of soil (γ_d), (e) friction angle of soil (ϕ), and (f) cohesion of soil (c)
표 Table 1. Range and distribution of input parameters used in seepage and slope stability analysis
그림 Fig. 2. MLP model for (a) FS and (b) failure arc prediction used in this study and (c) overall sequence of data generation, MLP model training, and analysis (M-P method: Morgenstern-Price method)
그림 Fig. 3. Data set configuration and MLP model training, validation, and test process
그림 Fig. 4. Loss convergence according to epoch in (a) FS prediction model and (b) circular failure arc prediction model training
그림 Fig. 5. Factor of safety prediction performance of MLP model through comparison of the factor of safety from SLOPE/W and MLP model
그림 Fig. 6. SLOPE/W results at (a) point a, (b) point b, (c) point c, (d) point d, (e) point e, and (f) point f in Fig. 5
그림 Fig. 7. Expression of the slip surface with abscissa of both ends and radius of the failure arc
그림 Fig. 8. Calculation of the factor of safety of a specific slip surface on a dry slope using the Morgenstern-Price method
표 Table 2. Input parameters, factor of safety, and error of data corresponding to a~f in Fig. 6
그림 Fig. 9. Circular slip surface prediction performance of MLP model through comparison of the factor of safety calculated using the Morgenstern-Price method
표 Table 3. Input parameters, factor of safety, and error of data corresponding to a~f in Fig. 10
그림 Fig. 10. SLOPE/W and MLP model results at (a) point a, (b) point b, (c) point c, (d) point d, (e) point e, and (f) point f in Fig. 9
그림 Fig. 11. SLOPE/W analysis result and MLP predicted failure arc of c data in Fig. 9
그림 Fig. 12. Mean and standard deviation of sensitivity of each input parameter predicting (a) factor of safety (FS), (b) x_in, (c) x_out, and (d) R by MLP model

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저자의 다른 논문 :

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