Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions an...Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.展开更多
Kawakawa Euthynnus affinis(Cantor,1849)is one of the commercially exploited tuna fishery resource of India.Except landing data,no other information was available on its stock status.Hence,present study investigated th...Kawakawa Euthynnus affinis(Cantor,1849)is one of the commercially exploited tuna fishery resource of India.Except landing data,no other information was available on its stock status.Hence,present study investigated the sustainability status of kawakawa fishery from Tamil Nadu,India.Data on annual catch and effort were reconstructed for the period of 2001-2020.The Catch-based MSY(CMSY),the Bayesian state-space Schaefer surplus production Model(BSM),and stochastic Surplus Production model in Continuous Time(SPiCT)packages were used to investigate the stock exploitation by running surplus production models with catch,catch per unit effort(CPUE),and other life history data.The biological reference points result of maximum sustainable yield(MSY)or biomass giving MSY(Bmsy)and fishing mortality giving MSY(Fmsy)of all the models indicated that the kawakawa stock in Tamil Nadu was subjected to overfishing and heading towards overfished status in recent decades.Results of the present study suggest reduction of fishing effort to keep the landing at MSY level and achieve a sustainable fishery.展开更多
基金supported by the Natural Science Foundation of China(Grant No.51679060)。
文摘Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.
文摘Kawakawa Euthynnus affinis(Cantor,1849)is one of the commercially exploited tuna fishery resource of India.Except landing data,no other information was available on its stock status.Hence,present study investigated the sustainability status of kawakawa fishery from Tamil Nadu,India.Data on annual catch and effort were reconstructed for the period of 2001-2020.The Catch-based MSY(CMSY),the Bayesian state-space Schaefer surplus production Model(BSM),and stochastic Surplus Production model in Continuous Time(SPiCT)packages were used to investigate the stock exploitation by running surplus production models with catch,catch per unit effort(CPUE),and other life history data.The biological reference points result of maximum sustainable yield(MSY)or biomass giving MSY(Bmsy)and fishing mortality giving MSY(Fmsy)of all the models indicated that the kawakawa stock in Tamil Nadu was subjected to overfishing and heading towards overfished status in recent decades.Results of the present study suggest reduction of fishing effort to keep the landing at MSY level and achieve a sustainable fishery.
