Developing the Rule of Thumb for Evaluating Penetration Rate of TBM, Using Binary Classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geology,Soil Science,Geotechnical Engineering and Engineering Geology,Architecture
Link
https://link.springer.com/content/pdf/10.1007/s10706-022-02178-7.pdf
Reference112 articles.
1. Afradi A, Ebrahimabadi A, Hallajian T (2019) Prediction of the penetration rate and number of consumed disc cutters of tunnel boring machines (TBMs) using artificial neural network (ANN) and support vector machine (SVM)—Case study: Beheshtabad water conveyance tunnel in iran. Asian J Water Environ Pollut 16(1):49–57
2. Afradi, A., Ebrahimabadi, A., & Hallajian, T. (2021). Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm. Geotechnical and Geological Engineering, 1–24.
3. Armaghani DJ, Mohamad ET, Narayanasamy MS, Narita N, Yagiz S (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Space Technol 63:29–43
4. Armaghani DJ, Faradonbeh RS, Momeni E, Fahimifar A, Tahir MM (2018) Performance prediction of tunnel boring machine through developing a gene expression programming equation. Engineering with Computers 34(1):129–141
5. Armaghani DJ, Hasanipanah M, Amnieh HB, Bui DT, Mehrabi P, Khorami M (2020) Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm. Engineering with Computers 36:1379–1391
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