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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

Research article (Asian Journal of Water Environment and Pollution, 2019) · cited 36× · AI/ML
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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

Summary

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 is a scholarly article[1].

Key Facts

  • 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's Case Study: Beheshtabad Water Conveyance Tunnel in Iran — instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). 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. Retrieved May 24, 2026, from https://4ort.xyz/entity/prediction-of-the-penetration-rate-and-number-of-consumed-disc-cutters-of-tunnel-boring-machines-tbms-using-artificial-n
MLA “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.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/prediction-of-the-penetration-rate-and-number-of-consumed-disc-cutters-of-tunnel-boring-machines-tbms-using-artificial-n.
BibTeX @misc{4ortxyz_prediction-of-the-penetration-rate-and-number-of-consumed-disc-cutters-of-tunnel-boring-machines-tbms-using-artificial-n_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{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}}, year = {2026}, url = {https://4ort.xyz/entity/prediction-of-the-penetration-rate-and-number-of-consumed-disc-cutters-of-tunnel-boring-machines-tbms-using-artificial-n}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): 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 — https://4ort.xyz/entity/prediction-of-the-penetration-rate-and-number-of-consumed-disc-cutters-of-tunnel-boring-machines-tbms-using-artificial-n (retrieved 2026-05-24)

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