Multi-step real-time prediction of hard-rock TBM penetration rate combining temporal convolutional network and squeeze-and-excitation block.

Accurate penetration rate prediction enhances rock-breaking efficiency and reduces disc cutter damage in tunnel boring machine (TBM) construction. However, this process faces significant challenges such as the high uncertainty of ground conditions and the complexity of maintaining optimal TBM operation in long and large tunnels. To address these challenges, we propose TCN-SENet++, a novel hybrid multistep real-time penetration rate prediction model that combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block for aided tunneling.

A simplified log creep model for describing triaxial creep of mélange rocks.

This study aims to develop a simplified log creep model (LgCM) for predicting the triaxial three-stage creep behaviors of mélange rocks. The model was deduced from the creep deformation mechanism by considering the competition of strain rate hardening and damage during the steady and accelerating creep stages and was described by two simplified fractal functions. The model was then compared with the previous creep models on the uniaxial three-stage creep data of mortar, rock salt, and sandy shale, as well as the triaxial low-stress creep data of claystone.