Polymer-modified concrete and fiber concrete are two excellent paving materials that improve the performance of some concrete, but the performance of single application material is still limited. In this paper, polymer-modified concrete with strong deformation and fiber concrete with obvious crack resistance and reinforcement effect were compounded by using the idea of composite material design so as to obtain a high-performance pavement material. The basic mechanical properties of high-content hybrid fiber–polymer-modified concrete, such as workability, compression, flexural resistance, and environmental durability (such as sulfate resistance) were studied by using the test regulations of cement concrete in China. The main results were as follows. (1) The hybrid fiber–polymer concrete displayed reliable working performance, high stiffness, and a modulus of elasticity as high as 35.93 GPa. (2) The hybrid fiber–polymer concrete had a compressive strength of 52.82 MPa, which was 31.2% higher than that of the plain C40 concrete (40.25 MPa); the strength of bending of the hybrid concrete was 11.51 MPa, 191.4% higher than that of the plain concrete (3.95 MPa). (3) The corrosion resistance value of the hybrid fiber–polymer concrete was 81.31%, indicating its adjustability to sulfate attack environments. (4) According to cross-sectional scanning electron microscope (SEM) images, the hybrid fiber–polymer concrete was seemingly more integrated with a dense layer of cementing substance on its surface along with fewer microholes and microcracks as when compared to the ordinary concrete. The research showed that hybrid fiber–polymer concrete had superior strength and environmental erosion resistance and was a pavement material with superior mechanical properties.
The K-means algorithm has been extensively investigated in the field of text clustering because of its linear time complexity and adaptation to sparse matrix data. However, it has two main problems, namely, the determination of the number of clusters and the location of the initial cluster centres. In this study, we propose an improved K-means++ algorithm based on the Davies-Bouldin index (DBI) and the largest sum of distance called the SDK-means++ algorithm. Firstly, we use the term frequency-inverse document frequency to represent the data set. Secondly, we measure the distance between objects by cosine similarity. Thirdly, the initial cluster centres are selected by comparing the distance to existing initial cluster centres and the maximum density. Fourthly, clustering results are obtained using the K-means++ method. Lastly, DBI is used to obtain optimal clustering results automatically. Experimental results on real bank transaction volume data sets show that the SDK-means++ algorithm is more effective and efficient than two other algorithms in organising large financial text data sets. The F-measure value of the proposed algorithm is 0.97. The running time of the SDK-means++ algorithm is reduced by 42.9% and 22.4% compared with that for K-means and K-means++ algorithms, respectively.
Crack line analysis is an effective way to solve elastic-plastic crack problems. Application of the method does not need the traditional small-scale yielding conditions and can obtain sufficiently accurate solutions near the crack line. To address mode-III crack problems under the perfect elastic-plastic condition, matching procedures of the crack line analysis method are summarized and refined to give general forms and formulation steps of plastic field, elastic-plastic boundary, and elastic-plastic matching equations near the crack line. The research unifies mode-III crack problems under different conditions into a problem of determining four integral constants with four matching equations. An example is given to verify correctness, conciseness, and generality of the procedure.
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