Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm
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/s10706-021-01982-x.pdf
Reference73 articles.
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2. 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. https://doi.org/10.3233/AJW190006
3. Afradi A, Ebrahimabadi A, Hallajian T (2020) Prediction of tunnel boring machine penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization, case study: Sabzkooh water conveyance tunnel. Min Miner Depos 14(2):75–84. https://doi.org/10.33271/mining14.02.075
4. Afradi A, Ebrahimabadi A (2021) Prediction of TBM penetration rate using the imperialist competitive algorithm (ICA) and quantum fuzzy logic. Innovative Infrastructure Solutions 6(2):1–17. https://doi.org/10.1007/s41062-021-00467-3
5. Alebouyeh A, Dehghan AN, Goshtasbi K (2019) Identifying the geological hazards during mechanized tunneling in urban areas–the case of Tehran alluvium conditions. In: Tunnels and underground cities: engineering and innovation meet archaeology, architecture and art. CRC Press, pp 5264–5274
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