Bridging Interpretability and Deep Intelligence: An Interpretable-to-Deep (I2D) AI Framework for Tunnel Convergence Prediction

This publication has 58 references indexed in Scilit:

• Probabilistic Back Analysis Based on Nadam, Bayesian, and Matrix-Variate Deep Gaussian Process for Rock Tunnels
  Rock Mechanics and Rock Engineering, 2024
• Probabilistic Back Analysis Based on Adam, Bayesian and Multi-output Gaussian Process for Deep Soft-Rock Tunnel
  Rock Mechanics and Rock Engineering, 2023
• Inversion of Surrounding Rock Mechanical Parameters in a Soft Rock Tunnel Based on a Hybrid Model EO-LightGBM
  Rock Mechanics and Rock Engineering, 2023
• Development and application of a novel probabilistic back-analysis framework for geotechnical parameters in shield tunneling based on the surrogate model and Bayesian theory
  Acta Geotechnica, 2023
• Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection
  Geomechanics and Tunnelling, 2022
• Exploring the deformation features and control techniques for surrounding rock of slate section tunnel
  Arabian Journal of Geosciences, 2020
• Research on tunnel engineering monitoring technology based on BPNN neural network and MARS machine learning regression algorithm
  Neural Computing and Applications, 2020
• Prediction of the tunnel displacement induced by laterally adjacent excavations using multivariate adaptive regression splines
  Acta Geotechnica, 2020
• Research on deformation prediction of tunnel surrounding rock using the model combining firefly algorithm and nonlinear auto-regressive dynamic neural network
  Engineering with Computers, 2019
• Back analysis for mechanical parameters of surrounding rock for underground roadways based on new neural network
  Engineering with Computers, 2017