Performances of belite-rich Portland cement, or HBC （high belite cement）, and the resultant concrete are introduced by comparing with that of alite based PC （Portland cement） and concrete. The comparison study of ...Performances of belite-rich Portland cement, or HBC （high belite cement）, and the resultant concrete are introduced by comparing with that of alite based PC （Portland cement） and concrete. The comparison study of cement properties indicates that HBC possesses the properties of less water demand for normal consistency, better compatibility with water reducer, higher later age strength after 28-day under standard curing temperature of 20 ℃, unique strength gain under elevated curing temperatures of 38-70 ℃, lower hydration heat evolution and temperature rise, lower drying shrinkage and excellent resistance to sulphate attack. These results have been demonstrated by the comparison performance evaluation of concretes prepared by HBC and PC in terms of workability, physical mechanical properties and durability when making high performance high strength concrete and massive concrete.展开更多
The paper presents and compares the conventional models for creep in cement concrete included in the ACI （American Concrete Institute） guide, Eurocode 2 andfib （International Federation for Structural Concrete） Mo...The paper presents and compares the conventional models for creep in cement concrete included in the ACI （American Concrete Institute） guide, Eurocode 2 andfib （International Federation for Structural Concrete） Model Code 2010. For the presentation and comparison of the creep models, creep coefficients are developed. The main factors affecting the prediction of creep in concrete are outlined, comparing the influence of concrete grade, environmental conditions, member size and loading conditions. Most of the conventional models currently used for creep in cement concrete develop the code-type procedures and are calibrated for normal- and high-strength concretes. They enable a more accurate analysis and better assessment of the time-dependent deformation of concrete structures at the design stage. Their complexity is significantly reduced and a range of influencing parameters are excluded from the models for simplicity and easy adaptation. The comparison of the models shows that thefib Model Code 2010 model is more consistent and calibrated to avoid shortcomings in the previous models.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Performances of belite-rich Portland cement, or HBC （high belite cement）, and the resultant concrete are introduced by comparing with that of alite based PC （Portland cement） and concrete. The comparison study of cement properties indicates that HBC possesses the properties of less water demand for normal consistency, better compatibility with water reducer, higher later age strength after 28-day under standard curing temperature of 20 ℃, unique strength gain under elevated curing temperatures of 38-70 ℃, lower hydration heat evolution and temperature rise, lower drying shrinkage and excellent resistance to sulphate attack. These results have been demonstrated by the comparison performance evaluation of concretes prepared by HBC and PC in terms of workability, physical mechanical properties and durability when making high performance high strength concrete and massive concrete.
文摘The paper presents and compares the conventional models for creep in cement concrete included in the ACI （American Concrete Institute） guide, Eurocode 2 andfib （International Federation for Structural Concrete） Model Code 2010. For the presentation and comparison of the creep models, creep coefficients are developed. The main factors affecting the prediction of creep in concrete are outlined, comparing the influence of concrete grade, environmental conditions, member size and loading conditions. Most of the conventional models currently used for creep in cement concrete develop the code-type procedures and are calibrated for normal- and high-strength concretes. They enable a more accurate analysis and better assessment of the time-dependent deformation of concrete structures at the design stage. Their complexity is significantly reduced and a range of influencing parameters are excluded from the models for simplicity and easy adaptation. The comparison of the models shows that thefib Model Code 2010 model is more consistent and calibrated to avoid shortcomings in the previous models.
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.
