[논문]낙동강 조간대 연약지반의 지역별 점성토층 두께 추정 모델 개발에 관한 연구

안성인; 류동우

doi:10.7474/tus.2022.32.6.586

낙동강 조간대 연약지반의 지역별 점성토층 두께 추정 모델 개발에 관한 연구
A Study on the Development of Model for Estimating the Thickness of Clay Layer of Soft Ground in the Nakdong River Estuary 원문보기

터널과 지하공간: 한국암반공학회지 = Tunnel and underground space, v.32 no.6, 2022년, pp.586 - 597

안성인 (한국지질자원연구원 광물자원연구본부 자원탐사개발연구센터) , 류동우 (한국지질자원연구원 광물자원연구본부 자원탐사개발연구센터)

초록
AI-Helper

본 연구에서는 국내 주요 연약지반으로 알려진 낙동강 조간대 지역의 압밀침하 취약성 평가에 활용할 상부 점성토층의 위치별 두께 정보를 추정할 수 있는 모델을 개발하였다. 두께정보 추정을 위하여 기계학습 알고리즘인 RF (Random Forest), SVR (Support Vector Regression), GPR (Gaussian Process Regression)과 지구통계기법인 정규크리깅(Ordinary Kriging)을 이용한 4가지 공간추정 모델을 개발하고 상호 비교하였다. 모델 개발을 위하여 수집한 연구지역의 시추공 자료 4,712개 중 상부점성토층이 존재하는 2,948개의 시추공 자료를 사용하였으며, 개발된 모델들의 성능을 정량적으로 평가하기 위하여 피어슨(Pearson) 상관계수와 오차제곱평균(mean squared error)을 사용하였다. 또한, 정성적 평가를 위하여 연구지역 전역에 상부점성토층의 두께를 추정하여 점성토층의 지역별 분포 특성을 상호 비교하였다.

Abstract ▼ AI-Helper

In this study, a model was developed for the estimating the locational thickness information of the upper clay layer to be used for the consolidation vulnerability evaluation in the Nakdong river estuary. To estimate ground layer thickness information, we developed four spatial estimation models using machine learning algorithms, which are RF (Random Forest), SVR (Support Vector Regression) and GPR (Gaussian Process Regression), and geostatistical technique such as Ordinary Kriging. Among the 4,712 borehole data in the study area collected for model development, 2,948 borehole data with an upper clay layer were used, and Pearson correlation coefficient and mean squared error were used to quantitatively evaluate the performance of the developed models. In addition, for qualitative evaluation, each model was used throughout the study area to estimate the information of the upper clay layer, and the thickness distribution characteristics of it were compared with each other.

주제어

표/그림 (10)

표 Table 1. Summary of types and characteristics for ground layer modelling
그림 Fig. 1. Areal view of the study area with borehole data (image source: Esri)
그림 Fig. 2. Target data information. (a) Distribution of borehole with upper clay and (b) Histogram for upper clay thickness
그림 Fig. 3. Correlation analysis between target data and each elevation
그림 Fig. 4. Results of dividing sample data into five folds (a) without considering regional bias and (b) with considering it
그림 Fig. 5. Results of correlation analysis between the predicted and ground truth. (a) RF, (b) SVR, (c) GPR, (d) OK
표 Table 2. Summary of the results for prediction performance of each methodology with basic statistics
그림 Fig. 5. Results of correlation analysis between the predicted and ground truth. (a) RF, (b) SVR, (c) GPR, (d) OK (Continued)
그림 Fig. 6. Comparison results of histogram between predicted data of each methodology and ground truth. (a) GPR, (b) OK
그림 Fig. 7. Result of (a) masking the area where target data is expected to exist and comparison of estimation based on each methodology for predicting upper clay thickness of the study area. (b) RF, (c) SVR, (d) GPR, (e) OK

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