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

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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