Forecasting Tunnel-Induced Ground Settlement: A Hybrid Deep Learning Approach and Traditional Statistical Techniques With Sensor Data

Accurately forecasting tunnel-induced ground settlement is crucial for mitigating risks to urban infrastructure and ensuring the safety of tunnelling operations. This study introduces advanced predictive frameworks that incorporate enhancements to both deep learning (DL) models and statistical techn...

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Bibliographic Details
Main Authors:	Syed Mujtaba Hussaine, Linlong Mu, Yimin Lu, Syed Sajid Hussain
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Series:	IEEE Access
Subjects:	Time-series forecasting ground settlement sensor data hybrid deep learning models ARIMA/SARIMA convolution mechanisms
Online Access:	https://ieeexplore.ieee.org/document/10949176/
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Forecasting Tunnel-Induced Ground Settlement: A Hybrid Deep Learning Approach and Traditional Statistical Techniques With Sensor Data

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