Analysis and prediction of the mechanical behavior of corrugated plate as primary support in tunnels with elastoplastic constitution. (June 2022) - Sun, Keguo; Hong, Yiqin; Xu, Weiping; Liu, Huan; Zhen, Yingzhou; Qin, Jinhang
Highlights: Outcome of elastic-plastic model is closer to reality than that of elastic model. There are 2 phases for vertical deformation: elastic phase and plastic phase. There are 3 phases for stress: elastic phase, transition phase and plastic phase. The influence of thickness is the biggest, followed by height and width. LM algorithm is suitable for deformation, BR algorithm is suitable for stress. Abstract: Compared to the traditional primary support mainly made of shot concrete, new-type corrugated-plate support, which has been maturely used in pipes and culverts, has many advantages like good flexibility and high efficiency. To study its mechanical characteristics in tunnels, several finite-element models with different constitutions, sizes, loads and yield stresses were built and simulated. And for its correctness and utility, a verification experiment and 2 kinds of neural networks were also carried out. The result shows that: firstly, 90% of the result in the simulation is in accord with the experiment. Then, in the elastoplastic model, for vertical displacement, the maximum is at the vault. For Von-Mises stress, the stress is higher at the vault and invert (241 MPa as an example), while it is relatively lower at part of the haunch (7 MPa as an example). Next, in terms of the increase of d (maximum vertical displacement), there are 2 phases: elastic phase and plastic phase. In the former phase, d increases steadily, while in the latter phase it surges rapidly. In terms of s (maximum Von-Mises stress), there are 3 phases: elastic phase, transition phase and plastic phase. In elastic and plastic phase, s ascends normally. But in the transition phase, s increases slowly until all the structure turns plastic. Moreover, as for the bearing capacity, the influence of t is the biggest, followed by h and w . Finally, for prediction, neural networks are a good choice. Meanwhile, different settings are needed for predicting different values. For predicting d, LM (Levenberg-Marquardt) algorithm with appropriate number of neurons is efficient (13 s for each training, MSE (Mean Squared Error) = 2.24). For predicting s, BR (Bayesian Regularization) algorithm is better with enough neurons (120 s for each training, MSE = 0.7). Since the conditions are finite, within an applicable scope, this research provides some innovative suggestions for applying new-type corrugated-plate support and neural networks in tunnelling field.