Asphalt mixture is a highly heterogeneous material, which is one of the reasons for high measurements uncertainty when subjected to tests. The results of such tests are often unreliable, which may lead to making bad p...Asphalt mixture is a highly heterogeneous material, which is one of the reasons for high measurements uncertainty when subjected to tests. The results of such tests are often unreliable, which may lead to making bad professional judgments. They can be avoided by carrying out reliable analyses of measurement uncertainty adequate for the research methods used and conducted before the actual research is done. This paper presents the calculation of measurements uncertainty using as an example--the determination of the stiffness modulus of the asphalt mixture, which, in turn, was accomplished using the indirect tension method. The paper also shows the employment of the basic methods of statistical analysis, such as testing two mean values and conformity tests. Essential concepts in measurements uncertainty have been compiled and the determination of the stiffness module parameters are discussed. It has been demonstrated that the biggest source of error in the stiffness modulus measuring process is the displacement measure. The aim of the research was to find the measurement uncertainty for stiffness modulus by an indirect tensile test and the presentation of examples of the used statistical methods.展开更多
We have investigated site occupancy and mechanical properties of a vanadium (V) Σ 5(310)/[001] grain boundary (GB) with hydrogen (H) using a first-principles method. The segregation energy is calculated to be 0.29 eV...We have investigated site occupancy and mechanical properties of a vanadium (V) Σ 5(310)/[001] grain boundary (GB) with hydrogen (H) using a first-principles method. The segregation energy is calculated to be 0.29 eV for the energetically favora- ble V GB interstitial site, indicating that H energetically prefers to segregate into the V GB. We demonstrate that H can largely affect the mechanical properties of the V GB. The tensile strength and the Griffith fracture energy are reduced by approximately 13% (to 18.42 GPa) and 10% (to 1.74 J/m2) because of H segregation in comparison with that of the clean V GB, respectively. Our total energy calculations show that H acts as an embrittler to the V GB based on the Rice-Wang model. The atomic configurations and charge transfer analysis show that the segregated H weakens the surrounding interfacial V-V bonds, leading to the V GB mechanical properties degradation.展开更多
In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have be...In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of 8 input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network was gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network has predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.展开更多
文摘Asphalt mixture is a highly heterogeneous material, which is one of the reasons for high measurements uncertainty when subjected to tests. The results of such tests are often unreliable, which may lead to making bad professional judgments. They can be avoided by carrying out reliable analyses of measurement uncertainty adequate for the research methods used and conducted before the actual research is done. This paper presents the calculation of measurements uncertainty using as an example--the determination of the stiffness modulus of the asphalt mixture, which, in turn, was accomplished using the indirect tension method. The paper also shows the employment of the basic methods of statistical analysis, such as testing two mean values and conformity tests. Essential concepts in measurements uncertainty have been compiled and the determination of the stiffness module parameters are discussed. It has been demonstrated that the biggest source of error in the stiffness modulus measuring process is the displacement measure. The aim of the research was to find the measurement uncertainty for stiffness modulus by an indirect tensile test and the presentation of examples of the used statistical methods.
基金supported by the National Natural Science Foundation of China(Grant No. 51061130558)
文摘We have investigated site occupancy and mechanical properties of a vanadium (V) Σ 5(310)/[001] grain boundary (GB) with hydrogen (H) using a first-principles method. The segregation energy is calculated to be 0.29 eV for the energetically favora- ble V GB interstitial site, indicating that H energetically prefers to segregate into the V GB. We demonstrate that H can largely affect the mechanical properties of the V GB. The tensile strength and the Griffith fracture energy are reduced by approximately 13% (to 18.42 GPa) and 10% (to 1.74 J/m2) because of H segregation in comparison with that of the clean V GB, respectively. Our total energy calculations show that H acts as an embrittler to the V GB based on the Rice-Wang model. The atomic configurations and charge transfer analysis show that the segregated H weakens the surrounding interfacial V-V bonds, leading to the V GB mechanical properties degradation.
文摘In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of 8 input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network was gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network has predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.
