Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an au...The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.展开更多
Cyclone separators are extensively utilized for the efficient separation of solid particles from fluid flows,where their operational effectiveness is intrinsically linked to the equilibrium between pressure drop and c...Cyclone separators are extensively utilized for the efficient separation of solid particles from fluid flows,where their operational effectiveness is intrinsically linked to the equilibrium between pressure drop and collection efficiency.However,in extreme industrial environments,such as fluidized catalytic cracking processes,severe wall erosion poses a significant challenge that compromises equipment lifespan.The present study aims to identify an optimal trade-off among separation efficiency,energy consumption,and erosion rate through the optimization of geometric ratios in cyclone separators.By adjusting specific key dimensions,erosion can be mitigated,extending the separator’s lifespan in harsh conditions.The relationships between six geometric dimension ratios and inlet gas velocity with respect to performance indicators are systematically investigated using design of experiments and computational fluid dynamics simulations.To develop a robust performance prediction model that accounts for multiple influencing factors,an auto machine learning approach is employed,incorporating ensemble learning strategies and automatic hyperparameter optimization techniques,which demonstrate superior performance compared to traditional artificial neural network methodologies.Furthermore,pareto-optimal solutions for maximizing separation efficiency while minimizing pressure drop and erosion rate are derived using the nondominated sorting genetic algorithm II,which is wellsuited for addressing complex nonlinear optimization problems.The results show that the optimized cyclone separator design enhances separation efficiency from 76.19% to 87.95%,reduces pressure drop from 1698 to 1433 Pa,and decreases the erosion rate from 8.06×10^(–5) to 7.32×10^(-5) kg·s^(-1),outperforming the conventional Stairmand design.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金supported by the National Natural Science Foundation of China(Grant Nos.51978517,52090082,and 52108381)Innovation Program of Shanghai Municipal Education Commission(Grant No.2019-01-07-00-07-456 E00051)Shanghai Science and Technology Committee Program(Grant Nos.21DZ1200601 and 20DZ1201404).
文摘The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.
基金financially supported by the National Natural Science Foundation of China(Grant No.22021004).
文摘Cyclone separators are extensively utilized for the efficient separation of solid particles from fluid flows,where their operational effectiveness is intrinsically linked to the equilibrium between pressure drop and collection efficiency.However,in extreme industrial environments,such as fluidized catalytic cracking processes,severe wall erosion poses a significant challenge that compromises equipment lifespan.The present study aims to identify an optimal trade-off among separation efficiency,energy consumption,and erosion rate through the optimization of geometric ratios in cyclone separators.By adjusting specific key dimensions,erosion can be mitigated,extending the separator’s lifespan in harsh conditions.The relationships between six geometric dimension ratios and inlet gas velocity with respect to performance indicators are systematically investigated using design of experiments and computational fluid dynamics simulations.To develop a robust performance prediction model that accounts for multiple influencing factors,an auto machine learning approach is employed,incorporating ensemble learning strategies and automatic hyperparameter optimization techniques,which demonstrate superior performance compared to traditional artificial neural network methodologies.Furthermore,pareto-optimal solutions for maximizing separation efficiency while minimizing pressure drop and erosion rate are derived using the nondominated sorting genetic algorithm II,which is wellsuited for addressing complex nonlinear optimization problems.The results show that the optimized cyclone separator design enhances separation efficiency from 76.19% to 87.95%,reduces pressure drop from 1698 to 1433 Pa,and decreases the erosion rate from 8.06×10^(–5) to 7.32×10^(-5) kg·s^(-1),outperforming the conventional Stairmand design.
