Fiber optic sensing and performance evaluation of a water conveyance tunnel with composite linings under super-high internal pressures

For long-distance water conveyance shield tunnels in operation,the high internal water pressure may cause excessive deformation of composite linings,affecting their structural integrity and serviceability.However,the deformation and failure characteristics of lining structures under internal water pressure are not well investigated in the literature,particularly for three-layer composite linings.This study presents an in situ experimental investigation on the response of two types of composite linings(i.e.separated and combined lining structures)subjected to internal pressures,in which a fiber optic nerve system(FONS)equipped with distributed strain and displacement sensing nerves was employed to monitor the performance of the two composite linings during testing.The experimental results clearly show that the damage of the tunnel lining under different internal pressures was mainly located in the self-compaction concrete layer.The separated lining structure responded more aggressively to the variations in internal pressures than the combined one.Moreover,two evaluation indices,i.e.radial displacement and effective stiffness coefficient,are proposed for describing the changes in the structural bearing performance.The effective stiffness coefficients of the two types of lining structures were reduced by 39.4%and 29.5%,respectively.Considering the convenience of field monitoring,it is suggested that the average strains at different layers can be used as characteristic parameters for estimating the health conditions of lining structures in service.The analysis results provide a practical reference for the design and health evaluation of water conveyance shield tunnels with composite linings.

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

The inspection of water conveyance tunnels plays an important role in water diversion projects.Siltation is an essential factor threatening the safety of water conveyance tunnels.Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects.The remotely operated vehicle(ROV)can detect such siltation.However,it needs to improve its intelligent recognition of image data it obtains.This paper introduces the idea of ensemble deep learning.Based on the VGG16 network,a compact convolutional neural network(CNN)is designed as a primary learner,called Silt-net,which is used to identify the siltation images.At the same time,the fully-connected network is applied as the meta-learner,and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results.Finally,several evaluation metrics are used to measure the performance of the proposed method.The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%,which is far better than the accuracy of other classifiers.Furthermore,the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.

TBM penetration rate prediction based on the long short-term memory neural network

Tunnel boring machines(TBMs)are widely used in tunnel engineering because of their safety and efficiency.The TBM penetration rate(PR)is crucial,as its real-time prediction can reflect the adaptation of a TBM under current geological conditions and assist the adjustment of operating parameters.In this study,deep learning technology is applied to TBM performance prediction,and a PR prediction model based on a long short-term memory(LSTM)neuron network is proposed.To verify the performance of the proposed model,the machine parameters,rock mass parameters,and geological survey data from the water conveyance tunnel of the Hangzhou Second Water Source project were collected to form a dataset.Furthermore,2313 excavation cycles were randomly composed of training datasets to train the LSTM-based model,and 257 excavation cycles were used as a testing dataset to test the performance.The root mean square error and the mean absolute error of the proposed model are 4.733 and 3.204,respectively.Compared with Recurrent neuron network(RNN)based model and traditional time-series prediction model autoregressive integrated moving average with explanation variables(ARIMAX),the overall performance on proposed model is better.Moreover,in the rapidly increasing period of the PR,the error of the LSTM-based model prediction curve is significantly smaller than those of the other two models.The prediction results indicate that the LSTM-based model proposed herein is relatively accurate,thereby providing guidance for the excavation process of TBMs and offering practical application value.