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머신러닝과 매개변수 분석을 통한 대심도 지하인프라 안정성 예측

Prediction of Stability of Deep Underground Infrastructure Through Machine Learning and Parameter Analysis

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키워드

주제분류자원공학

초록

본 연구는 다양한 머신러닝 알고리즘을 적용하여 터널 압착 현상을 예측하고 머신러닝에 활용되는 매개 변수의 영향도 분석을 실시하였다. 본 연구에서는 117개의 현장 데이터를 이용해 연구를 진행하였으며 매개변수로는 토피고(H), 터널 직경(D), Q 분류법, 암반 분류 지수(RMR), 변형계수(E)를 활용하였다. 머신러닝 기법으로는 랜덤 포레스트, 그래디언트 부스팅, 서포트 벡터 머신(SVM), 입자 군집 최적화가 결합된 SVM-PSO 모델을 사용하여 터널 압착을 예측하였다. 분석 결과 토피고와 터널 직경이 압착 예측 에 중요한 영향을 미치는 것으로 나타났으며 Q값을 RMR으로 변환하여 머신러닝에 적용하는 것이 더 바 람직하다고 분석하였다. 또한 SVM-PSO 기법이 94%로 가장 높은 정확도를 나타냈다.

Abstract

This study utilized various machine learning algorithms to predict tunnel squeezing phenomena and analyzed the influence of different parameters on the predictions.This research was based on 117 field data samples, with parameters including cover height, tunnel diameter, rock quality index (Q), rock mass rating (RMR), and deformation modulus (E). The RMR and E values were calculated by converting the existing Q values. Machine learning techniques used in the study included the SVM-PSO model, which combines Random Forest, Gradient Boosting, Support Vector Machine (SVM), and Particle Swarm Optimization (PSO) for parameter tuning. The analysis revealed that cover height and tunnel diameter had a significant impact on squeezing predictions, and it was found to be more effective to convert Q values to RMR for machine learning applications. Additionally, the SVM-PSO model achieved the highest prediction accuracy at 94%, suggesting that the use of PSO for parameter optimization in an SVM-based model is particularly effective for predicting tunnel squeezing.

참고문헌 (40)

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