A simulation-based software to support the real-time operational parameters selection of tunnel boring machines

With the fact that the main operational parameters of the construction process in mechanized tunneling are currently selected based on monitoring data and engineering experience without exploiting the advantages of computer methods,the focus of this work is to develop a simulation-based real-time assistant system to support the selection of operational parameters.The choice of an appropriate set of these parameters(i.e.,the face support pressure,the grouting pressure,and the advance speed)during the operation of tunnel boring machines(TBM)is determined by evaluating different tunneling-induced soil-structure interactions such as the surface settlement,the associated risks on existing structures and the tunnel lining behavior.To evaluate soil-structure behavior,an advanced process-oriented numerical simulation model based on the finite cell method is utilized.To enable the real-time prediction capability of the simulation model for a practical application during the advancement of TBMs,surrogate models based on the Proper Orthogonal Decomposition and Radial Basis Functions(POD-RBF)are adopted.The proposed approach is demonstrated through several synthetic numerical examples inspired by the data of real tunnel projects.The developed methods are integrated into a user-friendly application called SMART to serve as a support platform for tunnel engineers at construction sites.Corresponding to each user adjustment of the input parameters,i.e.,each TBM driving scenario,approximately two million outputs of soil-structure interactions are quickly predicted and visualized in seconds,which can provide the site engineers with a rough estimation of the impacts of the chosen scenario on structural responses of the tunnel and above ground structures.

Real-time estimation of the structural utilization level of segmental tunnel lining

Over the last decades,an expansion of the underground network has been taking place to cope with the increasing amount of moving people and freight.As a consequence,it is of vital importance to guarantee the full functionality of the tunnel network by means of preventive maintenance and the monitoring of the tunnel lining state over time.A new method has been developed for the real-time prediction of the utilization level in tunnel segmental linings based on input monitoring data.The new concept is founded on a framework,which encompasses an offline and an online stage.In the former,the generation of feedforward neural networks is accomplished by employing synthetically produced data.Finite element simulations of the lining structure are conducted to analyze the structural response under multiple loading conditions.The scenarios are generated by assuming ranges of variation of the model input parameters to account for the uncertainty due to the not fully determined in situ conditions.Input and target quantities are identified to better assess the structural utilization of the lining.The latter phase consists in the application of the methodological framework on input monitored data,which allows for a real-time prediction of the physical quantities deployed for the estimation of the lining utilization.The approach is validated on a full-scale test of segmental lining,where the predicted quantities are compared with the actual measurements.Finally,it is investigated the influence of artificial noise added to the training data on the overall prediction performances and the benefits along with the limits of the concept are set out.