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 cur...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.展开更多
This paper is devoted to the analysis of the Cauchy problem for a system of PDEs arising in radiative hydrodynamics. This system, which comes from the so-called equilibrium diffusion regime, is a variant of the usual ...This paper is devoted to the analysis of the Cauchy problem for a system of PDEs arising in radiative hydrodynamics. This system, which comes from the so-called equilibrium diffusion regime, is a variant of the usual Euler equations, where the energy and pressure functionals are modified to take into account the effect of radiation and the energy balance containing a nonlinear diffusion term acting on the temperature. The problem is studied in the multi-dimensional framework. The authors identify the existence of a strictly convex entropy and a stability property of the system, and check that the Kawashima-Shizuta condition holds. Then, based on these structure properties, the wellposedness close to a constant state can be proved by using fine energy estimates. The asymptotic decay of the solutions are also investigated.展开更多
基金supported by National Natural Science Foundation of China(No.51739007)the National Science Fund for Excellent Young Scholars(No.51922067)+3 种基金Joint Funds of the National Natural Science Foundation of China(No.U1806226)Taishan Scholars Program of Shandong Province(tsqn20190900,tsqn201909044)the Key Research and Development Program of Shandong Province(No.Z135050009107)the Interdisciplinary Development Program of Shandong University(No.2017JC002).
文摘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.
基金Project supported by the Fundamental Research Funds for the Central Universities (No. 2009B27514)the National Natural Science Foundation of China (No. 10871059)
文摘This paper is devoted to the analysis of the Cauchy problem for a system of PDEs arising in radiative hydrodynamics. This system, which comes from the so-called equilibrium diffusion regime, is a variant of the usual Euler equations, where the energy and pressure functionals are modified to take into account the effect of radiation and the energy balance containing a nonlinear diffusion term acting on the temperature. The problem is studied in the multi-dimensional framework. The authors identify the existence of a strictly convex entropy and a stability property of the system, and check that the Kawashima-Shizuta condition holds. Then, based on these structure properties, the wellposedness close to a constant state can be proved by using fine energy estimates. The asymptotic decay of the solutions are also investigated.
