This paper investigates the free vibration and transient response of interconnected structures including double curved beams and intermediate straight beams,which are all constructed from functionally graded porous(FG...This paper investigates the free vibration and transient response of interconnected structures including double curved beams and intermediate straight beams,which are all constructed from functionally graded porous(FGP)materials.The strain potential and kinetic energies of each beam along with the work done by the external force are calculated.Additionally,a higher-order beam element is introduced to derive stiffness and mass matrices,along with the force vector.The curved and straight beams are discretized,and their assembled stiffness,mass matrices,and force vectors,are obtained.Continuity conditions at the joints are used to derive the total matrices of the entire structure.Subsequently,the natural frequencies and transient response of the system are determined.The accuracy of the mathematical model and the self-developed computer program is validated through the comparison of the obtained results with those of the existing literature and commercial software ANSYS,demonstrating excellent agreement.Furthermore,a comprehensive study is conducted to investigate the effects of various parameters on the free vibration and transient response of the considered structure.展开更多
Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the ...Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature convergence.In response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as OChOA.Then,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called RLOChOA.This way effectively avoids the algorithm falling into local optimum.The performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world problems.Numerical results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.展开更多
In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to ...In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.展开更多
The modeling of cross-ply composite laminates using numerical methods has been a difficult task,leading to the development of various finite element method and other analytical solutions.However,as materials science a...The modeling of cross-ply composite laminates using numerical methods has been a difficult task,leading to the development of various finite element method and other analytical solutions.However,as materials science advances,this problem has become more complex,presenting new challenges that require reliable and novel approaches.In this study,we propose the utilization of machine learning,specifically physics informed neural networks(PINN),for the first time to examine the behavior of composite plate.By solving a system of partial differential equations derived from the virtual work equilibrium principle,PINN are employed as a method to solve these equations using a generalized strong-form approach.To address the issue of imbalanced loss functions,we also propose adjusting the loss function in this research.Once trained,PINN serve as a surrogate model capable of predicting displacements and stresses in cross-ply composite laminates.To demonstrate the effectiveness and reliability of PINN,we investigate two examples of laminates with different material distributions and boundary conditions including boundary conditions on displacement and boundary conditions on stress,comparing the results with the benchmark Navier solution.The research and obtained results showcase the performance and accuracy of PINN,highlighting their potential as a surrogate model for solving problems related to cross-ply composite laminates.展开更多
Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding o...Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important properties.To facilitate engineering work on the material,an efficient predicting model is needed.In this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process.The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is ensured.The effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting process.The three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and SVM.The numerical results suggest that the DNN model generally outperforms the other two models.展开更多
文摘This paper investigates the free vibration and transient response of interconnected structures including double curved beams and intermediate straight beams,which are all constructed from functionally graded porous(FGP)materials.The strain potential and kinetic energies of each beam along with the work done by the external force are calculated.Additionally,a higher-order beam element is introduced to derive stiffness and mass matrices,along with the force vector.The curved and straight beams are discretized,and their assembled stiffness,mass matrices,and force vectors,are obtained.Continuity conditions at the joints are used to derive the total matrices of the entire structure.Subsequently,the natural frequencies and transient response of the system are determined.The accuracy of the mathematical model and the self-developed computer program is validated through the comparison of the obtained results with those of the existing literature and commercial software ANSYS,demonstrating excellent agreement.Furthermore,a comprehensive study is conducted to investigate the effects of various parameters on the free vibration and transient response of the considered structure.
文摘Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature convergence.In response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as OChOA.Then,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called RLOChOA.This way effectively avoids the algorithm falling into local optimum.The performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world problems.Numerical results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.
文摘In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation.In this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system.We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism.We also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters effectively.We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark functions.CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm.For the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection analyses.We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective.The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds.Lastly,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases.We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.
基金the support from Ho Chi Minh City Open University under the Basic Research Fund(No.E2023.03.2).
文摘The modeling of cross-ply composite laminates using numerical methods has been a difficult task,leading to the development of various finite element method and other analytical solutions.However,as materials science advances,this problem has become more complex,presenting new challenges that require reliable and novel approaches.In this study,we propose the utilization of machine learning,specifically physics informed neural networks(PINN),for the first time to examine the behavior of composite plate.By solving a system of partial differential equations derived from the virtual work equilibrium principle,PINN are employed as a method to solve these equations using a generalized strong-form approach.To address the issue of imbalanced loss functions,we also propose adjusting the loss function in this research.Once trained,PINN serve as a surrogate model capable of predicting displacements and stresses in cross-ply composite laminates.To demonstrate the effectiveness and reliability of PINN,we investigate two examples of laminates with different material distributions and boundary conditions including boundary conditions on displacement and boundary conditions on stress,comparing the results with the benchmark Navier solution.The research and obtained results showcase the performance and accuracy of PINN,highlighting their potential as a surrogate model for solving problems related to cross-ply composite laminates.
文摘Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important properties.To facilitate engineering work on the material,an efficient predicting model is needed.In this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process.The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is ensured.The effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting process.The three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and SVM.The numerical results suggest that the DNN model generally outperforms the other two models.
