Settlements in conventional tunneling with deep neural networks

M. Parra; F. Ochoa-Cornejo; F. Hernandez; G. Corral

doi:10.1201/9781003348030-344

Settlements in conventional tunneling with deep neural networks

M. Parra, F. Ochoa-Cornejo, F. Hernandez, G. Corral

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The construction of a tunnel can induce subsidence in the ground at surface level, which can affect and cause damage to existing structures, especially in areas with high building density. This paper presents a deep neural network model (DNN) to estimate the maximum surface settlement “S_max ” in a tunnel excavated with using conventional tunnelling. The structuring of the deep learning algorithm was performed using the TensorFlow library in Python 3.0. The DNN model was trained, tested, and validated using a synthetic database composed of several numerical models automated with Python in the finite element program PLAXIS2D. Variables associated with soil properties, support characteristics, surface overburden in the context of the conventional tunnelling process were considered for the modelling. To verify the estimation capacity of the DNN model, performance tests and statistical analyses were carried out, with the results showing good predictive capacity of the model for the three variables analysed.

Original language	English
Title of host publication	Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023
Editors	Georgios Anagnostou, Andreas Benardos, Vassilis P. Marinos
Publisher	CRC Press/Balkema
Pages	2853-2860
Number of pages	8
ISBN (Print)	9781003348030
DOIs	https://doi.org/10.1201/9781003348030-344
State	Published - 2023
Externally published	Yes
Event	ITA-AITES World Tunnel Congress, ITA-AITES WTC 2023 and the 49th General Assembly of the International Tunnelling and Underground Association, 2023 - Athens, Greece Duration: 12 May 2023 → 18 May 2023

Publication series

Name	Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023

Conference

Conference	ITA-AITES World Tunnel Congress, ITA-AITES WTC 2023 and the 49th General Assembly of the International Tunnelling and Underground Association, 2023
Country/Territory	Greece
City	Athens
Period	12/05/23 → 18/05/23

Bibliographical note

Publisher Copyright:
© 2023 The Author(s).

Access to Document

10.1201/9781003348030-344

Cite this

Parra, M., Ochoa-Cornejo, F., Hernandez, F., & Corral, G. (2023). Settlements in conventional tunneling with deep neural networks. In G. Anagnostou, A. Benardos, & V. P. Marinos (Eds.), Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023 (pp. 2853-2860). (Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023). CRC Press/Balkema. https://doi.org/10.1201/9781003348030-344

Parra, M. ; Ochoa-Cornejo, F. ; Hernandez, F. et al. / Settlements in conventional tunneling with deep neural networks. Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023. editor / Georgios Anagnostou ; Andreas Benardos ; Vassilis P. Marinos. CRC Press/Balkema, 2023. pp. 2853-2860 (Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023).

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abstract = "The construction of a tunnel can induce subsidence in the ground at surface level, which can affect and cause damage to existing structures, especially in areas with high building density. This paper presents a deep neural network model (DNN) to estimate the maximum surface settlement “Smax ” in a tunnel excavated with using conventional tunnelling. The structuring of the deep learning algorithm was performed using the TensorFlow library in Python 3.0. The DNN model was trained, tested, and validated using a synthetic database composed of several numerical models automated with Python in the finite element program PLAXIS2D. Variables associated with soil properties, support characteristics, surface overburden in the context of the conventional tunnelling process were considered for the modelling. To verify the estimation capacity of the DNN model, performance tests and statistical analyses were carried out, with the results showing good predictive capacity of the model for the three variables analysed.",

author = "M. Parra and F. Ochoa-Cornejo and F. Hernandez and G. Corral",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s).; ITA-AITES World Tunnel Congress, ITA-AITES WTC 2023 and the 49th General Assembly of the International Tunnelling and Underground Association, 2023 ; Conference date: 12-05-2023 Through 18-05-2023",

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Parra, M, Ochoa-Cornejo, F, Hernandez, F & Corral, G 2023, Settlements in conventional tunneling with deep neural networks. in G Anagnostou, A Benardos & VP Marinos (eds), Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023. Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023, CRC Press/Balkema, pp. 2853-2860, ITA-AITES World Tunnel Congress, ITA-AITES WTC 2023 and the 49th General Assembly of the International Tunnelling and Underground Association, 2023, Athens, Greece, 12/05/23. https://doi.org/10.1201/9781003348030-344

Settlements in conventional tunneling with deep neural networks. / Parra, M.; Ochoa-Cornejo, F.; Hernandez, F. et al.
Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023. ed. / Georgios Anagnostou; Andreas Benardos; Vassilis P. Marinos. CRC Press/Balkema, 2023. p. 2853-2860 (Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hernandez, F.

AU - Corral, G.

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N2 - The construction of a tunnel can induce subsidence in the ground at surface level, which can affect and cause damage to existing structures, especially in areas with high building density. This paper presents a deep neural network model (DNN) to estimate the maximum surface settlement “Smax ” in a tunnel excavated with using conventional tunnelling. The structuring of the deep learning algorithm was performed using the TensorFlow library in Python 3.0. The DNN model was trained, tested, and validated using a synthetic database composed of several numerical models automated with Python in the finite element program PLAXIS2D. Variables associated with soil properties, support characteristics, surface overburden in the context of the conventional tunnelling process were considered for the modelling. To verify the estimation capacity of the DNN model, performance tests and statistical analyses were carried out, with the results showing good predictive capacity of the model for the three variables analysed.

AB - The construction of a tunnel can induce subsidence in the ground at surface level, which can affect and cause damage to existing structures, especially in areas with high building density. This paper presents a deep neural network model (DNN) to estimate the maximum surface settlement “Smax ” in a tunnel excavated with using conventional tunnelling. The structuring of the deep learning algorithm was performed using the TensorFlow library in Python 3.0. The DNN model was trained, tested, and validated using a synthetic database composed of several numerical models automated with Python in the finite element program PLAXIS2D. Variables associated with soil properties, support characteristics, surface overburden in the context of the conventional tunnelling process were considered for the modelling. To verify the estimation capacity of the DNN model, performance tests and statistical analyses were carried out, with the results showing good predictive capacity of the model for the three variables analysed.

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T3 - Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023

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Parra M, Ochoa-Cornejo F, Hernandez F, Corral G. Settlements in conventional tunneling with deep neural networks. In Anagnostou G, Benardos A, Marinos VP, editors, Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023. CRC Press/Balkema. 2023. p. 2853-2860. (Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023). doi: 10.1201/9781003348030-344

Settlements in conventional tunneling with deep neural networks

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