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

Underground Space 2024年第4期17卷 132-145页

作者： nicola gottardi Steffen Freitag Gunther Meschke Institute for Structural Mechanics Ruhr University BochumBochum 44801Germany Institute for Structural Analysis Karlsruhe Institute of TechnologyKarlsruhe 76131Germany

Over the last decades,an expansion of the underground network has been taking place to cope with the increasing amount of moving people and *** 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 *** new concept is founded on a framework,which encompasses an offline and an online *** the former,the generation of feedforward neural networks is accomplished by employing synthetically produced *** element simulations of the lining structure are conducted to analyze the structural response under multiple loading *** 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 *** and target quantities are identified to better assess the structural utilization of 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 *** approach is validated on a full-scale test of segmental lining,where the predicted quantities are compared with the actual ***,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.

关键词： Segmental lining Artificial neural networks Structural utilization level Real-time prediction Structural health monitoring Monitoring data

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