An experimental study was conducted to investigate the effect ofnano-SiO2 and steel fiber content on the durability of concrete. Five different dosages of nano-SiO2 particles and five volume dosages of steel fiber were used. The durability of concretes includes permeability resistance, cracking resistance, carbonation resistance, and freezing-thawing resistance, and these were evaluated by the water permeation depth, number of cracks, total cracking area per unit area of the specimens, carbonation depth of the specimens, and the relative dynamic elastic modulus of the specimens after freezing-thawing cycles, respectively. The results indicate that the addition of nano-SiO2 particles significantly improves the durability of concrete when the content of nano-SiO2 is limited within a certain range. With the increase of nano-SiO2 content, the durability of concrete first increases and then decreases. An excessive number of nano-SiO2 particles could have an adverse effect on the durability of the concrete. The addition of the correct amount of steel fibers improves the carbonation resistance of concrete containing nano-particles, but excessive steel fiber reduces the carbonation resistance. Moreover, the addition of steel fibers reduces the permeability resistance of concrete containing nano-particles. The incorporation of steel fiber enhanced the freezing-thawing resistance and cracking resistance of concrete containing nano-particles. With increasing steel fiber content, the freezing-thawing resistance of the concrete containing nano-particles increases, and the cracking resistance of the concrete decreases gradually.

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Existing query engines for RDF graphs follow one of two design paradigms: relational or graph-based. We explore sparse matrix algebra as a third paradigm and propose MAGiQ: a framework for implementing SPARQL query engines that are portable on various hardware architectures, scalable over thousands of compute nodes, and efficient for very large RDF datasets. MAGiQ represents the RDF graph as a sparse matrix and defines a domain-specific language of algebraic operations. SPARQL queries are translated into matrix algebra programs that are oblivious to the underlying computing infrastructure. Existing matrix algebra libraries, optimized for each particular architecture, are called to execute the program and handle the performance issues. We present three case studies of matrix algebra back-end libraries: SuiteSparse, Matlab, and CombBLAS; we demonstrate how MAGiQ can effortlessly be ported on a variety of architectures such as Intel CPUs, NVIDIA GPUs, and Cray XC40 supercomputers. Our experiments on large-scale real and synthetic datasets show that MAGiQ performs comparably to or better than existing specialized SPARQL query engines for data-intensive queries, scales to very large computing infrastructures, and handles datasets with up to 512 billion triples.