The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration o...The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments.展开更多
One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural ne...One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.展开更多
Rotor airfoil design is investigated in this paper. There are many difficulties for this highdimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer...Rotor airfoil design is investigated in this paper. There are many difficulties for this highdimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer hierarchical constraint method is proposed by coupling principal component analysis(PCA) dimensionality reduction and e-constraint method to translate the original high-dimensional problem into a bi-objective problem. This paper selects the main design objectives by conducting PCA to the preliminary solution of original problem with consideration of the priority of design objectives. According to the e-constraint method, the design model is established by treating the two top-ranking design goals as objective and others as variable constraints. A series of bi-objective Pareto curves will be obtained by changing the variable constraints, and the favorable solution can be obtained by analyzing Pareto curve spectrum. This method is applied to the rotor airfoil design and makes great improvement in aerodynamic performance. It is shown that the method is convenient and efficient, beyond which, it facilitates decision-making of the highdimensional multi-objective engineering problem.展开更多
To expand the application of multi-layer graphene in water-based systems, modified multi-layer graphene was prepared by vacuum impregnation with silica sol and carbon-embedded heat treatment at 300, 500 or 700 ℃ for ...To expand the application of multi-layer graphene in water-based systems, modified multi-layer graphene was prepared by vacuum impregnation with silica sol and carbon-embedded heat treatment at 300, 500 or 700 ℃ for 3 h. The phase composition, microstructure and wettability of the modified multi-layer graphene heat treated at different temperatures were studied. The results show that the water wettability of the modified multi-layer graphene is improved after vacuum impregnation with silica sol and carbon-embedded heat treatment;the optimum heat treatment temperature is 300 ℃, and the modified multi-layer graphene has the water wetting angle of 64.7°.展开更多
To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross ...To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.展开更多
We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-base...We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-based atomic force microscope in ambient conditions, and the stacking particles form Au nanoparticle-aggregated nanowire while the nozzle retracts from the surface. Their mechanical properties with relatively low elastic modulus are in situ investigated by using the same apparatus.展开更多
The multi-layer ceramic capacitor (MLCC) alignment system aims at the inter-process automation between the first and the second plastic processes.As a result of testing performance verification of MLCC alignment syste...The multi-layer ceramic capacitor (MLCC) alignment system aims at the inter-process automation between the first and the second plastic processes.As a result of testing performance verification of MLCC alignment system,the average alignment rates are 95% for 3216 chip,88.5% for 2012 chip and 90.8% for 3818 chip.The MLCC alignment system can be accepted for practical use because the average manual alignment is just 80%.In other words,the developed MLCC alignment system has been upgraded to a great extent,compared with manual alignment.Based on the successfully developed MLCC alignment system,the optimal transfer conditions have been explored by using RSM.The simulations using ADAMS has been performed according to the cube model of CCD.By using MiniTAB,the model of response surface has been established based on the simulation results.The optimal conditions resulted from the response optimization tool of MiniTAB has been verified by being assigned to the prototype of MLCC alignment system.展开更多
采用多层感知器模型、随机森林模型为第一层子模型,极端树模型为第二层元模型,建立基于Stacking集成机器学习的波浪预报算法,并引入邻域平均法抑制在拐点处产生的数值震荡。以长江口外海2016年1-9月的风速和中国近海波高数据为数据源,...采用多层感知器模型、随机森林模型为第一层子模型,极端树模型为第二层元模型,建立基于Stacking集成机器学习的波浪预报算法,并引入邻域平均法抑制在拐点处产生的数值震荡。以长江口外海2016年1-9月的风速和中国近海波高数据为数据源,利用机器学习风速与有效波高之间的关系,将2016年10-11月的风速、波高数据用于预报结果的对比分析,预报前45 d R^2拟合优度达到0.97以上,平均误差最大值为0.08 m,平均相对误差最大值为0.05,预报结果与波浪谱模型结果趋势一致,准确度较高;预报结果后15 d误差增长较快,这与训练集数据中寒潮浪占比较少有关。展开更多
In the past two decades numerous studies were made to develop and improve the theory and practical computation techniques of synthesizing theoretical seismograms for the model of multi-layered half-space. Today, synth...In the past two decades numerous studies were made to develop and improve the theory and practical computation techniques of synthesizing theoretical seismograms for the model of multi-layered half-space. Today, synthesizing theoretical seismograms in multi-layered half-space is an important tool for understanding the structure of the Earth’s interior as well as the seismic source process from well-recorded seismograms data. As part of a review of the state-of-the-art, in this article I shall present a systematic and self-contained theory of elastic waves in multi-layered half-space media based on the developments in the past two decades.展开更多
The influence of temperature on the hydrogen diffusion behavior in X80 pipeline steel during stacking for slow cooling was studied using electrochemical penetration method, the temperature field and the hydrogen diffu...The influence of temperature on the hydrogen diffusion behavior in X80 pipeline steel during stacking for slow cooling was studied using electrochemical penetration method, the temperature field and the hydrogen diffusion in this pipeline steel during stacking for slow cooling were simulated by ABAQUS finite element method （FEM） software. The results show that in this process there is a reciprocal relationship between the natural logarithm of hydrogen diffusion coefficient and temperature. The cooling rate decreases gradually with the increase of steel plate thickness. The hydrogen content is higher at high temperature （500-400 ℃） than that in low temperature region （300-100 ℃）. The FEM simulation results are consistent with the experimental ones, and the model can be used to predict the hydrogen diffusion behavior in industrial production of X80 pipeline steel.展开更多
基金supported by the Basic Research Special Plan of Yunnan Provincial Department of Science and Technology-General Project(Grant No.202101AT070094)。
文摘The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12072217).
文摘One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.
基金supported by the National Natural Science Foundation of China (No. 11402288 and 11372254)the National Basic Research Program of China (No. 2014CB744804)
文摘Rotor airfoil design is investigated in this paper. There are many difficulties for this highdimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer hierarchical constraint method is proposed by coupling principal component analysis(PCA) dimensionality reduction and e-constraint method to translate the original high-dimensional problem into a bi-objective problem. This paper selects the main design objectives by conducting PCA to the preliminary solution of original problem with consideration of the priority of design objectives. According to the e-constraint method, the design model is established by treating the two top-ranking design goals as objective and others as variable constraints. A series of bi-objective Pareto curves will be obtained by changing the variable constraints, and the favorable solution can be obtained by analyzing Pareto curve spectrum. This method is applied to the rotor airfoil design and makes great improvement in aerodynamic performance. It is shown that the method is convenient and efficient, beyond which, it facilitates decision-making of the highdimensional multi-objective engineering problem.
基金financially supported by Natural Science foundation of Hebei Province (E2017209164) and (E2023209035)。
文摘To expand the application of multi-layer graphene in water-based systems, modified multi-layer graphene was prepared by vacuum impregnation with silica sol and carbon-embedded heat treatment at 300, 500 or 700 ℃ for 3 h. The phase composition, microstructure and wettability of the modified multi-layer graphene heat treated at different temperatures were studied. The results show that the water wettability of the modified multi-layer graphene is improved after vacuum impregnation with silica sol and carbon-embedded heat treatment;the optimum heat treatment temperature is 300 ℃, and the modified multi-layer graphene has the water wetting angle of 64.7°.
基金Educational Research Project of Social Science for Young and Middle Aged Teachers in Fujian Province,China(No.JAS19371)Social Science Research Project of Education Department of Fujian Province,China(No.JAS160571)Key Project of Education and Teaching Reform of Undergraduate Universities in Fujian Province,China(No.FBJG20190130)。
文摘To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.
基金supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (No. 200983512)Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2013R1A6A3A03063900)the Brain Korea 21
文摘We fabricate and characterize Au nanoparticle-aggregated nanowires by using the nano meniscus-induced colloidal stacking method. The Au nanoparticle solution ejects with guidance of nanopipette/quartz tuning fork-based atomic force microscope in ambient conditions, and the stacking particles form Au nanoparticle-aggregated nanowire while the nozzle retracts from the surface. Their mechanical properties with relatively low elastic modulus are in situ investigated by using the same apparatus.
基金supported by the Second Stage of Brain Korea 21 Projectssupported (in part) by the Solomon Mechanics Inc
文摘The multi-layer ceramic capacitor (MLCC) alignment system aims at the inter-process automation between the first and the second plastic processes.As a result of testing performance verification of MLCC alignment system,the average alignment rates are 95% for 3216 chip,88.5% for 2012 chip and 90.8% for 3818 chip.The MLCC alignment system can be accepted for practical use because the average manual alignment is just 80%.In other words,the developed MLCC alignment system has been upgraded to a great extent,compared with manual alignment.Based on the successfully developed MLCC alignment system,the optimal transfer conditions have been explored by using RSM.The simulations using ADAMS has been performed according to the cube model of CCD.By using MiniTAB,the model of response surface has been established based on the simulation results.The optimal conditions resulted from the response optimization tool of MiniTAB has been verified by being assigned to the prototype of MLCC alignment system.
文摘采用多层感知器模型、随机森林模型为第一层子模型,极端树模型为第二层元模型,建立基于Stacking集成机器学习的波浪预报算法,并引入邻域平均法抑制在拐点处产生的数值震荡。以长江口外海2016年1-9月的风速和中国近海波高数据为数据源,利用机器学习风速与有效波高之间的关系,将2016年10-11月的风速、波高数据用于预报结果的对比分析,预报前45 d R^2拟合优度达到0.97以上,平均误差最大值为0.08 m,平均相对误差最大值为0.05,预报结果与波浪谱模型结果趋势一致,准确度较高;预报结果后15 d误差增长较快,这与训练集数据中寒潮浪占比较少有关。
文摘In the past two decades numerous studies were made to develop and improve the theory and practical computation techniques of synthesizing theoretical seismograms for the model of multi-layered half-space. Today, synthesizing theoretical seismograms in multi-layered half-space is an important tool for understanding the structure of the Earth’s interior as well as the seismic source process from well-recorded seismograms data. As part of a review of the state-of-the-art, in this article I shall present a systematic and self-contained theory of elastic waves in multi-layered half-space media based on the developments in the past two decades.
文摘The influence of temperature on the hydrogen diffusion behavior in X80 pipeline steel during stacking for slow cooling was studied using electrochemical penetration method, the temperature field and the hydrogen diffusion in this pipeline steel during stacking for slow cooling were simulated by ABAQUS finite element method （FEM） software. The results show that in this process there is a reciprocal relationship between the natural logarithm of hydrogen diffusion coefficient and temperature. The cooling rate decreases gradually with the increase of steel plate thickness. The hydrogen content is higher at high temperature （500-400 ℃） than that in low temperature region （300-100 ℃）. The FEM simulation results are consistent with the experimental ones, and the model can be used to predict the hydrogen diffusion behavior in industrial production of X80 pipeline steel.
