A novel lattice model to predict chloride diffusion coefficient of unsaturated cementitious materials based on multi-typed pore structure characteristics

This paper develops a novel lattice diffusive model to quantitatively study the chloride diffusion coefficient in unsaturated cementitious materials, in which the pore voxels are redistributed to make a better representation of a real microstructure of hardened cement paste. Considering the hierarchical microstructure and different drying...

Degradation of alkali-activated slag subjected to water immersion

In this study, the impacts of tap water immersion on the pore solution, phase assemblages, gel chemistry and structure, and pore structure of alkali-activated slag (AAS) pastes were studied. AAS degrades under such condition and the potential mechanisms can be concluded as lower reaction rates, gel decomposition and carbonation. The leaching...

Simplex-based multinomial logistic regression with diverging numbers of categories and covariate

Multinomial logistic regression models are popular in multicategory classification analysis, but existing models suffer several intrinsic drawbacks. In particular, the parameters cannot be determined uniquely because of the over-specification. Although additional constraints have been imposed to refine the model, such modifications can be...

A facile method to prepare oriented boron nitride-based polymer composite with enhanced thermal conductivity and mechanical properties

Hexagonal boron nitride (BN) is often used as filler to improve the thermal conductivity of polymer matrix due to its high thermal conductivity. However, previously reported BN-based composites always have a high in-plane thermal conductivity, which is not beneficial for vertical heat dissipation. In addition, high BN content results in the...

Evaluation of short-term streamflow prediction methods in Urban river basins

Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally...

SlimML: Removing Non- critical Input Data in Large-scale Iterative Machine Learning

The core of many large-scale machine learning (ML) applications, such as neural networks (NN), support vector machine (SVM), and convolutional neural network (CNN), is the training algorithm that iteratively updates model parameters by processing massive datasets. From a plethora of studies aiming at accelerating ML, being data...

Quantification the filling of microcracks due to Autogenous self-healing in cement paste

Microcracks play vital roles in the prediction of the service life of concrete structure. Because microcracks in concrete structure are the preferential ingression channels for aggressive ions, e.g., chloride, sulphate, etc. However, microcracks have potentials to self-heal autogenously due to the continuous hydration of unhydrated cement,...