Role of Silica Fume in Compressive Strength of Cement Paste, Mortar, and Concrete

Controversy exists as to why silica fume increases the strength of concrete when it is used as a partial replacement for cement. Some evidence supports the view that the increase in strength is due to an increase in the strength of the cement paste constituent of concrete. However, contradictory evidence exists that shows no increase in the strength of cement paste, but substantial increases in concrete strength, when silica fume is used. The latter evidence is used to support the theory that silica fume strengthens concrete by strengthening the bond between cement paste and aggregate. This study is designed to explain the contradictory evidence and establish the role played by silica fume in controlling the strength of concrete and its constituent materials. These goals are accomplished using cement pastes, mortars, and concretes with water-cementitious material ratios ranging from 0.30 to 0.39. Mixtures incorporate no admixtures, a superplasticizer only, or silica fume and a superplasticizer. The research demonstrates that replacement of cement by silica fume and the addition of a superplasticizer increases the strength of cement paste. It also demonstrates that cement paste specimens, with or without silica fume, can exhibit reduced strength compared to other specimens with the same water-cementitious material ratio if the material segregates during fabrication, thus explaining some earlier experimental observations. The segregation of cement paste is caused by high superplasticizer dosages that do not cause segregation of concrete with the same water-cementitious material ratio. Concrete containing silica fume as a partial replacement for cement exhibits an increased compressive strength because of the improved strength of its cement paste constituent. Changes in the paste-aggregate interface caused by silica fume appear to have little effect on the uniaxial compressive strength of concrete

The development of generalized synchronization on complex networks

In this paper, we investigate the development of generalized synchronization (GS) on typical complex networks, such as scale-free networks, small-world networks, random networks and modular networks. By adopting the auxiliary-system approach to networks, we show that GS can take place in oscillator networks with both heterogeneous and homogeneous degree distribution, regardless of whether the coupled chaotic oscillators are identical or nonidentical. For coupled identical oscillators on networks, we find that there exists a general bifurcation path from initial non-synchronization to final global complete synchronization (CS) via GS as the coupling strength is increased. For coupled nonidentical oscillators on networks, we further reveal how network topology competes with the local dynamics to dominate the development of GS on networks. Especially, we analyze how different coupling strategies affect the development of GS on complex networks. Our findings provide a further understanding for the occurrence and development of collective behavior in complex networks.Comment: 10 pages, 13 figure

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification

The dynamic ensemble selection of classifiers is an effective approach for processing label-imbalanced data classifications. However, such a technique is prone to overfitting, owing to the lack of regularization methods and the dependence of the aforementioned technique on local geometry. In this study, focusing on binary imbalanced data classification, a novel dynamic ensemble method, namely adaptive ensemble of classifiers with regularization (AER), is proposed, to overcome the stated limitations. The method solves the overfitting problem through implicit regularization. Specifically, it leverages the properties of stochastic gradient descent to obtain the solution with the minimum norm, thereby achieving regularization; furthermore, it interpolates the ensemble weights by exploiting the global geometry of data to further prevent overfitting. According to our theoretical proofs, the seemingly complicated AER paradigm, in addition to its regularization capabilities, can actually reduce the asymptotic time and memory complexities of several other algorithms. We evaluate the proposed AER method on seven benchmark imbalanced datasets from the UCI machine learning repository and one artificially generated GMM-based dataset with five variations. The results show that the proposed algorithm outperforms the major existing algorithms based on multiple metrics in most cases, and two hypothesis tests (McNemar's and Wilcoxon tests) verify the statistical significance further. In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the research on the regularization for dynamic ensemble methods.Comment: Major revision; Change of authors due to contribution