[논문]암반공학분야에 적용된 인공지능 알고리즘 분석

김양균

doi:10.7474/tus.2021.31.1.025

암반공학분야에 적용된 인공지능 알고리즘 분석
An Analysis of Artificial Intelligence Algorithms Applied to Rock Engineering 원문보기

터널과 지하공간: 한국암반공학회지 = Tunnel and underground space, v.31 no.1, 2021년, pp.25 - 40

김양균 (노르트론)

초록
AI-Helper

4차 산업혁명 시대의 도래에 따라 암반공학분야에서도 인공지능을 활용한 연구가 점차 증가하고 있다. 본 논문에서는 인공지능에 대한 이해와 그 활용도를 더욱 증진시키기 위하여, 암반공학기술의 주된 적용대상인 터널, 발파, 광산과 관련된 최근의 국내외 연구 중 인공지능이 활용된 논문들에서 그 알고리즘의 종류와 적용방법을 분석하였다. 터널에서는 암반분류, TBM굴진율 및 막장전방 지질 예측, 발파에서는 암반의 파쇄도 및 비산거리, 광산에서는 폐광의 침하가능성 예측을 위해 주로 활용되고 있으며, 기계학습의 다양한 알고리즘 중 인공신경망이 압도적으로 많이 활용되고 있는 것으로 나타났다. 연구결과의 정확도와 신뢰성 제고를 위해 사용하고자 하는 인공지능 알고리즘에 대한 정확하고 상세한 이해가 필수적이며, 현재는 접근이나 분석이 난해한 암반공학 분야의 다양한 문제해결을 위해 기계학습뿐 아니라 CNN 또는 RNN과 같은 딥러닝을 활용한 연구 아이디어들이 점차 증가될 것으로 기대된다.

Abstract ▼ AI-Helper

As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.

주제어

표/그림 (11)

그림 Fig. 1. Application level of smart construction technologies among local construction companies (CERIK, 2019)
그림 Fig. 2. Plan of local construction companies to introduce smart construction technologies in 10 years (CERIK, 2019)
표 Table 1. Subsets of AI
그림 Fig. 3. Mind map of machine learning algorithms (Downloaded from https://medium.com/datadriveninvestor/)
그림 Fig. 4. Typical architecture of ANN
그림 Fig. 5. Classification process of tunnel faces using Inception-ResNet-v2 model (Chen et al., 2021)
그림 Fig. 6. Probabilistic neural network block diagram (Feng et al., 2019)
그림 Fig. 7. Typical architecture of LSTM (Houdt et al, 2020)
그림 Fig. 8. ANN architecture used in the study of Das et al. (2019)
그림 Fig. 9. Process to produce MSR index(Kim and Lee, 2020)
표 Table 2. Algorithms applied to rock engineering

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