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...

Descrición completa

Detalles Bibliográficos

Principais autores:	Syed Mujtaba Hussaine, Linlong Mu, Yimin Lu, Syed Sajid Hussain
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2025-01-01
Series:	IEEE Access
Assuntos:	Time-series forecasting ground settlement sensor data hybrid deep learning models ARIMA/SARIMA convolution mechanisms
Acceso en liña:	https://ieeexplore.ieee.org/document/10949176/
Tags:	Engadir etiqueta Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!