Resumen
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.
Idioma original | Inglés |
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Título de la publicación alojada | Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023 |
Editores | Georgios Anagnostou, Andreas Benardos, Vassilis P. Marinos |
Editorial | CRC Press/Balkema |
Páginas | 2853-2860 |
Número de páginas | 8 |
ISBN (versión impresa) | 9781003348030 |
DOI | |
Estado | Publicada - 2023 |
Publicado de forma externa | Sí |
Evento | ITA-AITES World Tunnel Congress, ITA-AITES WTC 2023 and the 49th General Assembly of the International Tunnelling and Underground Association, 2023 - Athens, Grecia Duración: 12 may. 2023 → 18 may. 2023 |
Serie de la publicación
Nombre | 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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Conferencia
Conferencia | ITA-AITES World Tunnel Congress, ITA-AITES WTC 2023 and the 49th General Assembly of the International Tunnelling and Underground Association, 2023 |
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País/Territorio | Grecia |
Ciudad | Athens |
Período | 12/05/23 → 18/05/23 |
Nota bibliográfica
Publisher Copyright:© 2023 The Author(s).