This paper presents test results of fifteen reinforced engineered cementitious composite (ECC)-concrete beams. The main parameters investigated were the amount and type of reinforcement, and ECC thickness. All reinforced ECC-concrete composite beams tested were classified into four groups according to the amount and type of main longitudinal reinforcement used; three groups were reinforced with FRP, steel and hybrid FRP/steel bars, respectively, having similar tensile capacity, whereas the fourth group had a larger amount of only FRP reinforcement. In each group, four height replacement ratios of ECC to concrete were studied. The test results showed that the moment capacity and stiffness of concrete beams are improved and the crack width can be well controlled when a concrete layer in the tension zone is replaced with an ECC layer of the same thickness. However, the improvement level of ECC-concrete composite beams was controlled by the type and amount of reinforcement used. Based on the simplified constitutive relationships of materials and plane section assumption, three failure modes and their discriminate formulas are developed. Furthermore, simplified formulas for moment capacity calculations are proposed, predicting good agreement with experimental results.

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Sentence semantic matching is the cornerstone of many natural language processing tasks, including Chinese language processing. It is well known that Chinese sentences with different polysemous words or word order may have totally different semantic meanings. Thus, to represent and match the sentence semantic meaning accurately, one challenge that must be solved is how to capture the semantic features from the multi-granularity perspective, e.g., characters and words. To address the above challenge, we propose a novel sentence semantic matching model which is based on the fusion of semantic features from charactergranularity and word-granularity, respectively. Particularly, the multigranularity fusion intends to extract more semantic features to better optimize the downstream sentence semantic matching. In addition, we propose the equilibrium cross-entropy, a novel loss function, by setting mean square error (MSE) as an equilibrium factor of cross-entropy. The experimental results conducted on Chinese open data set demonstrate that our proposed model combined with binary equilibrium cross-entropy loss function is superior to the existing state-of-the-art sentence semantic matching models.

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