Real-time prediction of shield moving trajectory during tunnelling

This paper presents a novel deep learning model for real-time prediction of shield moving trajectory during tunnelling. The proposed model incorporates a wavelet transform (WT) into Adam-optimised long short-term memory (LSTM) (WT-Adam-LSTM). The WT is employed to remove the irrelevant noise of data...

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Veröffentlicht in:	Acta geotechnica 2022-04, Vol.17 (4), p.1533-1549
Hauptverfasser:	Shen, Shui-Long, Elbaz, Khalid, Shaban, Wafaa Mohamed, Zhou, Annan
Format:	Artikel
Sprache:	eng
Schlagworte:	Airports Algorithms Complex Fluids and Microfluidics Deep learning Engineering Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Hydraulics Long short-term memory Machine learning Network reliability Neural networks Real time Recurrent neural networks Research Paper Soft and Granular Matter Soil conditions Soil Science & Conservation Solid Mechanics Support vector machines Wavelet transforms
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