While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using po...While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).展开更多
A model based on the non-linear artificial neural network （ANN） is established to predict the thickness of the water film on road surfaces. The weight and the threshold can be determined by training test data, and t...A model based on the non-linear artificial neural network （ANN） is established to predict the thickness of the water film on road surfaces. The weight and the threshold can be determined by training test data, and the water film thickness on the road surface can be accurately predicted by the empirical verification based on sample data. Results show that the proposed ANN model is feasible to predict the water film thickness of the road surface.展开更多
A new technique for preparing TiO2 modified film on carbon steel was accomplished by electroless plating and sol-gel composite process. The artificial neural network was applied to optimize the preparing condition of ...A new technique for preparing TiO2 modified film on carbon steel was accomplished by electroless plating and sol-gel composite process. The artificial neural network was applied to optimize the preparing condition of TiO2 modified film. The optimized condition for forming TiO2 modified film on carbon steel was that NiP plating for 50 min, dip-coating times as 4, heat treatment time for 2 h, and the molar ratio of complexing agent and Ti(OC4HZ9)4 kept 1.5:1. The results showed that TiO2 modified film have good corrosion resistance. The result conformed that it is feasible to design the preparing conditions of TiO2 modified film by artificial neural network.展开更多
The elevated-temperature deformation behavior of Ti2AlNb superalloy was observed by isothermal compression experiments in a wide range of temperatures(950–1200°C)and strain rates(0.001–10 s^(-1)).The flow behav...The elevated-temperature deformation behavior of Ti2AlNb superalloy was observed by isothermal compression experiments in a wide range of temperatures(950–1200°C)and strain rates(0.001–10 s^(-1)).The flow behavior is nonlinear,strongly coupled,and multivariable.The constitutive models,namely the double multivariate nonlinear regression model,artificial neural network model,and modified artificial neural network model with an explicit expression,were applied to describe the Ti2AlNb superalloy plastic deformation behavior.The comparative predictability of those constitutive models was further evaluated by considering the correlation coefficient and average absolute relative error.The comparative results show that the modified artificial network model can describe the flow stress of Ti2AlNb superalloy more accurately than the other developed constitutive models.The explicit expression obtained from the modified artificial neural network model can be directly used for finite element simulation.The modified artificial neural network model solves the problems that the double multivariate nonlinear regression model cannot describe the nonlinear,strongly coupled,and multivariable flow behavior of Ti2AlNb superalloy accurately,and the artificial neural network model cannot be embedded into the finite element software directly.However,the modified artificial neural network model is mainly dependent on the quantity of high-quality experimental data and characteristic variables,and the modified artificial neural network model has not physical meanings.Besides,the processing maps were applied to obtain the optimum processing parameters.展开更多
The objective of this study is to evaluate the performance of the artificial neural network(ANN)approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure.To achieve...The objective of this study is to evaluate the performance of the artificial neural network(ANN)approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure.To achieve this goal,two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure.The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to flling weight deflectometer load.In addition,two proposed ANN models were verifed by comparing the results of ANN models with the results of PADAL and double multiple regression models.The measured pavement deflection basin data was used for the verifications.The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models.PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous.The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions.In addition,the back-calculation model avoided the back-calculation errors by considering the interlayer condition,which was barely considered by former models reported in the published studies.展开更多
文摘While various kinds of fibers are used to improve the hot mix asphalt(HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network(ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm(GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy(correlation coefficient of 0.96).
文摘A model based on the non-linear artificial neural network （ANN） is established to predict the thickness of the water film on road surfaces. The weight and the threshold can be determined by training test data, and the water film thickness on the road surface can be accurately predicted by the empirical verification based on sample data. Results show that the proposed ANN model is feasible to predict the water film thickness of the road surface.
文摘A new technique for preparing TiO2 modified film on carbon steel was accomplished by electroless plating and sol-gel composite process. The artificial neural network was applied to optimize the preparing condition of TiO2 modified film. The optimized condition for forming TiO2 modified film on carbon steel was that NiP plating for 50 min, dip-coating times as 4, heat treatment time for 2 h, and the molar ratio of complexing agent and Ti(OC4HZ9)4 kept 1.5:1. The results showed that TiO2 modified film have good corrosion resistance. The result conformed that it is feasible to design the preparing conditions of TiO2 modified film by artificial neural network.
基金China National Science and Technology Major Project(Grant No.2017-VI-0004-0075).
文摘The elevated-temperature deformation behavior of Ti2AlNb superalloy was observed by isothermal compression experiments in a wide range of temperatures(950–1200°C)and strain rates(0.001–10 s^(-1)).The flow behavior is nonlinear,strongly coupled,and multivariable.The constitutive models,namely the double multivariate nonlinear regression model,artificial neural network model,and modified artificial neural network model with an explicit expression,were applied to describe the Ti2AlNb superalloy plastic deformation behavior.The comparative predictability of those constitutive models was further evaluated by considering the correlation coefficient and average absolute relative error.The comparative results show that the modified artificial network model can describe the flow stress of Ti2AlNb superalloy more accurately than the other developed constitutive models.The explicit expression obtained from the modified artificial neural network model can be directly used for finite element simulation.The modified artificial neural network model solves the problems that the double multivariate nonlinear regression model cannot describe the nonlinear,strongly coupled,and multivariable flow behavior of Ti2AlNb superalloy accurately,and the artificial neural network model cannot be embedded into the finite element software directly.However,the modified artificial neural network model is mainly dependent on the quantity of high-quality experimental data and characteristic variables,and the modified artificial neural network model has not physical meanings.Besides,the processing maps were applied to obtain the optimum processing parameters.
基金This work was financially supported by the National Natural Science Foundation of China(Grant Nos.51278188,50808077,and 51778224)the Project of Young Core Instructor Growth from Hunan Province.The first author also acknowledges the financial support from the China Scholarship Council(CSC)under No.201606130003.The authors are sincerely grateful for their financial support.In addition,the manuscript has received the written quality improvement assistance from Michigan Tech Multilteracies Center during the revisions.The views and findings of this study represent those of the authors and may not rflect those of NSFC,Hunan University,and CSC.
文摘The objective of this study is to evaluate the performance of the artificial neural network(ANN)approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure.To achieve this goal,two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure.The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to flling weight deflectometer load.In addition,two proposed ANN models were verifed by comparing the results of ANN models with the results of PADAL and double multiple regression models.The measured pavement deflection basin data was used for the verifications.The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models.PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous.The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions.In addition,the back-calculation model avoided the back-calculation errors by considering the interlayer condition,which was barely considered by former models reported in the published studies.
