The self-healing action of a permeable crystalline coating on the po rous mortar was investigated by two times impermeability test. Moreover, the sel f-healing mechanism of cement-based materials with the permeable cr...The self-healing action of a permeable crystalline coating on the po rous mortar was investigated by two times impermeability test. Moreover, the sel f-healing mechanism of cement-based materials with the permeable crystalline c oating was studied by SEM. The results indicate that the permeable crystalline c oating not only seals the pores and cracks in mortar during its curing process, but also heals the permeable pathway caused by first impermeability test or crac ks produced by freeze-thaw cycles. Therefore, cement-based materials can be im proved by the permeable crystalline coating for the self-healing function. SEM images prove that the self-healing function is realized by generating a great q uantity of non-soluble dendritic crystalline within the pores and cracks, which prevents the penetration of water and other liquids.展开更多
To investigate the durability, especially the long-term stability of cement-based materials with very low w/b, the air permeability test, carbonation test, capillary absorption rate test and dilation potential test we...To investigate the durability, especially the long-term stability of cement-based materials with very low w/b, the air permeability test, carbonation test, capillary absorption rate test and dilation potential test were adopted under long-term heat treatment condition. Microstructure of these materials is also analyzed by scanning electronic microscopy （SEM） and mercury intrusion porosimeter （MIP） in order to further unveil its mechanism and interrelation between microstructure and its properties. The results indicate that in the area investigated, cement-based material with w/b 0.17, like RPC, possesses low porosity and excellent durability. Moreover, its porosity will further decrease under long-term heat treatment compared with normal heat treatment. Its long-term durability is much superior to that of other cement-based materials with w/b 0.25 or 0.35 as high strength concrete（HSC）.展开更多
The electrical conductivity and piezoresistivity of carbon fiber graphite cement-matrix composites（CFGCC） with carbon fiber content（1% by the weight of cement）,graphite powder contents （0%-50% by the weight of ce...The electrical conductivity and piezoresistivity of carbon fiber graphite cement-matrix composites（CFGCC） with carbon fiber content（1% by the weight of cement）,graphite powder contents （0%-50% by the weight of cement） and CCCW（cementitious capillary crystalline waterproofing materials,4% by the weight of cement） were studied.The experimental results showed that the relationship between the resistivity of CFGCC and the concentration of graphite powders had typical features of percolation phenomena.The percolation threshold was about 20%.A clear piezoresistive effect was observed in CFGCC with 1wt% of carbon fibers,20wt% or 30wt% of graphite powders under uniaxial compressive tests,indicating that this type of smart composites was a promising candidate for strain sensing.The measured gage factor （defined as the fractional change in resistance per unit strain） of CFGCC with graphite content of 20wt% and 30wt% were 37 and 22,respectively.With the addition of CCCW,the mechanical properties of CFGCC were improved,which benefited CFGCC piezoresistivity of stability.展开更多
Water vapor permeability of building materials is a crucial parameter for analysing and optimizing the hygrothermal performance of building envelopes and built environments.Its measurement is accurate but time-consumi...Water vapor permeability of building materials is a crucial parameter for analysing and optimizing the hygrothermal performance of building envelopes and built environments.Its measurement is accurate but time-consuming,while data mining methods have the potential to predict water vapor permeability efficiently.In this study,six data mining methods—support vector regression(SVR),decision tree regression(DT),random forest regression(RF),K-nearest neighbor(KNN),multi-layer perceptron(MLP),and adaptive boosting regression(AdaBoost)—were compared to predict the water vapor permeability of cement-based materials.A total of 143 datasets of material properties were collected to build prediction models,and five materials were experimentally determined for model validation.The results show that RF has excellent generalization,stability,and precision.AdaBoost has great generalization and precision,only slightly inferior to the former,and its stability is excellent.DT has good precision and acceptable generalization,but its stability is poor.SVR and KNN have superior stability,but their generalization and precision are inadequate.MLP lacks generalization,and its stability and precision are unacceptable.In short,RF has the best comprehensive performance,demonstrated by a limited prediction deviation of 26.3%from the experimental results,better than AdaBoost(38.0%)and DT(38.3%)and far better than other remaining methods.It is also found that data mining methods provide better predictions when cement-based materials’water vapor permeability is high.展开更多
基金Funded by the Scientific and Technological Project of Hubei Province(2004BCS005)
文摘The self-healing action of a permeable crystalline coating on the po rous mortar was investigated by two times impermeability test. Moreover, the sel f-healing mechanism of cement-based materials with the permeable crystalline c oating was studied by SEM. The results indicate that the permeable crystalline c oating not only seals the pores and cracks in mortar during its curing process, but also heals the permeable pathway caused by first impermeability test or crac ks produced by freeze-thaw cycles. Therefore, cement-based materials can be im proved by the permeable crystalline coating for the self-healing function. SEM images prove that the self-healing function is realized by generating a great q uantity of non-soluble dendritic crystalline within the pores and cracks, which prevents the penetration of water and other liquids.
基金Funded by the National Natural Science Foundation of China(No.50708114)the Postgraduate Science Foundation of China(No.20060400883)
文摘To investigate the durability, especially the long-term stability of cement-based materials with very low w/b, the air permeability test, carbonation test, capillary absorption rate test and dilation potential test were adopted under long-term heat treatment condition. Microstructure of these materials is also analyzed by scanning electronic microscopy （SEM） and mercury intrusion porosimeter （MIP） in order to further unveil its mechanism and interrelation between microstructure and its properties. The results indicate that in the area investigated, cement-based material with w/b 0.17, like RPC, possesses low porosity and excellent durability. Moreover, its porosity will further decrease under long-term heat treatment compared with normal heat treatment. Its long-term durability is much superior to that of other cement-based materials with w/b 0.25 or 0.35 as high strength concrete（HSC）.
基金Funded by the National Natural Science Foundation of China(No.50878170 and No. 10672128)
文摘The electrical conductivity and piezoresistivity of carbon fiber graphite cement-matrix composites（CFGCC） with carbon fiber content（1% by the weight of cement）,graphite powder contents （0%-50% by the weight of cement） and CCCW（cementitious capillary crystalline waterproofing materials,4% by the weight of cement） were studied.The experimental results showed that the relationship between the resistivity of CFGCC and the concentration of graphite powders had typical features of percolation phenomena.The percolation threshold was about 20%.A clear piezoresistive effect was observed in CFGCC with 1wt% of carbon fibers,20wt% or 30wt% of graphite powders under uniaxial compressive tests,indicating that this type of smart composites was a promising candidate for strain sensing.The measured gage factor （defined as the fractional change in resistance per unit strain） of CFGCC with graphite content of 20wt% and 30wt% were 37 and 22,respectively.With the addition of CCCW,the mechanical properties of CFGCC were improved,which benefited CFGCC piezoresistivity of stability.
基金supported by the National Natural Science Foundation of China (No.52178065).
文摘Water vapor permeability of building materials is a crucial parameter for analysing and optimizing the hygrothermal performance of building envelopes and built environments.Its measurement is accurate but time-consuming,while data mining methods have the potential to predict water vapor permeability efficiently.In this study,six data mining methods—support vector regression(SVR),decision tree regression(DT),random forest regression(RF),K-nearest neighbor(KNN),multi-layer perceptron(MLP),and adaptive boosting regression(AdaBoost)—were compared to predict the water vapor permeability of cement-based materials.A total of 143 datasets of material properties were collected to build prediction models,and five materials were experimentally determined for model validation.The results show that RF has excellent generalization,stability,and precision.AdaBoost has great generalization and precision,only slightly inferior to the former,and its stability is excellent.DT has good precision and acceptable generalization,but its stability is poor.SVR and KNN have superior stability,but their generalization and precision are inadequate.MLP lacks generalization,and its stability and precision are unacceptable.In short,RF has the best comprehensive performance,demonstrated by a limited prediction deviation of 26.3%from the experimental results,better than AdaBoost(38.0%)and DT(38.3%)and far better than other remaining methods.It is also found that data mining methods provide better predictions when cement-based materials’water vapor permeability is high.
