[논문]기계학습을 이용한 비점성토 및 점성토 지반에서 시간의존 교각주위 국부세굴의 예측

최성욱; 최성욱; 최병웅

doi:10.3741/jkwra.2021.54.12.1275

기계학습을 이용한 비점성토 및 점성토 지반에서 시간의존 교각주위 국부세굴의 예측
Prediction of time dependent local scour around bridge piers in non-cohesive and cohesive beds using machine learning technique 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.12, 2021년, pp.1275 - 1284

최성욱 (연세대학교 건설환경공학과) , 최성욱 (연세대학교 건설환경공학과) , 최병웅 (환경부 국립환경과학원 영산강 물환경연구소)

초록
AI-Helper

본 논문에서는 기계학습을 이용하여 비점성토 및 점성토 지반에서 시간에 따른 교각주위 국부세굴을 예측하였다. 기계학습 기법으로는 과적합 오차를 유발하지 않는다고 알려진 Support Vector Machines (SVM) 기법이 사용되었다. 비점성토 지반 및 점성토 지반에서 시간에 따라 발달하는 세굴심을 7개 및 9개의 변수를 각각 이용하여 표현하였다. 여러 실험을 통해 얻어진 시계열 자료를 이용하여 개발된 모형을 학습시키고 검증하였다. 계산된 평균절대비오차(MAPE)에 의하면 모형의 학습과 검증이 적절하게 수행된 것으로 나타났다. 실험 결과뿐 아니라 Choi and Cho 공식과 Briaud et al.이 제시한 SRICOS 방법에 의한 결과와 비교하였다. 본 연구를 통해 양질의 자료가 충분히 제공되는 경우 SVM 모형이 비점성토 및 점성토 지반 시간의존 국부세굴을 예측할 수 있음을 보여주었다.

Abstract ▼ AI-Helper

This paper presents a machine learning technique applied to prediction of time-dependent local scour around bridge piers in both non-cohesive and cohesive beds. The support vector machines (SVM), which is known to be free from overfitting, is used. The time-dependent scour depths are expressed by 7 and 9 variables for the non-cohesive and cohesive beds, respectively. The SVM models are trained and validated with time series data from different sources of experiments. Resulting Mean Absolute Percentage Error (MAPE) indicates that the models are trained and validated properly. Comparisons are made with the results from Choi and Choi's formula and Scour Rate in Cohesive Soils (SRICOS) method by Briaud et al., as well as measured data. This study reveals that the SVM is capable of predicting time-dependent local scour in both non-cohesive and cohesive beds under the condition that sufficient data of good quality are provided.

주제어

표/그림 (10)

그림 Fig. 1. Local scour around bridge pier
표 Table 1. Range of variables for training SVM model for time dependent pier scour in the non-cohesive bed
표 Table 2. Range of variables for validating SVM model for time dependent pier scour in the non-cohesive bed
그림 Fig. 2. Training of machine learning techniques for time-dependent pier scour in the non-cohesive bed
그림 Fig. 3. Validation of machine learning techniques for time-dependent pier scour in the non-cohesive bed
그림 Fig. 4. SVM prediction of time-dependent pier scour in the non-cohesive bed
표 Table 3. Range of variables for training SVM model for time dependent pier scour in the cohesive bed
표 Table 4. Range of variables for validating SVM model for time dependent pier scour in the cohesive bed
그림 Fig. 5. Training and validation of the SVM model for time-dependent pier scour in the cohesive bed
그림 Fig. 6. SVM prediction of time-dependent pier scour in the cohesive bed

참고문헌 (35)

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저자의 다른 논문 :

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