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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Detaylı Bibliyografya
Asıl Yazarlar:	Syed Mujtaba Hussaine, Linlong Mu, Yimin Lu, Syed Sajid Hussain
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IEEE 2025-01-01
Seri Bilgileri:	IEEE Access
Konular:	Time-series forecasting ground settlement sensor data hybrid deep learning models ARIMA/SARIMA convolution mechanisms
Online Erişim:	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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