Ultra-high-performance concrete (UHPC) is an advanced concrete with superior mechanical strength, ductility and durability properties. However, the influence of steel fiber on its constitutive laws and the specimen geometric dimension effect on its strength had not been paid enough attention. To investigate the effect of steel fibers on the properties of UHPC, specimens with different fiber volume contents and fiber types were tested. Meanwhile, the mechanical properties of UHPC at different ages from 3 days to 28 days were conducted. Moreover, specimens with various geometric dimensions were also prepared to study the effect of specimen geometric dimensions (dog-bone-shaped, prism and cylinder specimens) on the properties of UHPC. The results indicated that elastic modulus, tensile peak stress and the corresponding strain increased as the fiber volume content and curing age increased. Specimens with hooked-end fibers exhibited better tensile performance than those with straight fibers. Furthermore, different geometric dimensions of specimens significantly influenced the tensile properties of UHPC. Based on the experimental results, conversion factors were suggested for the transformation of strength obtained from specimens with different geometric dimensions to reference specimens. In addition, both compressive and tensile constitutive laws were proposed to generate the stress–strain relationship of UHPC.

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To solve the problem that the limited time-frequency features cannot fully represent the deep-seated state information of rolling bearing, the time-frequency analysis method, whale optimization algorithm (WOA) and support matrix machine (SMM) are combined, and a fault diagnosis model based on multisynchrosqueezing transform (MSST) and WOA-SMM is proposed. First, the time-frequency trait of the original signal is extracted by MSST. Then, using the time-frequency spectrum processed by MSST as the input of SMM, MSST solves the problem of state information loss when constructing a characteristic matrix. Finally, the parameters of the SMM are optimized by WOA and the ideal parameters can be obtained adaptively, and the problem of setting parameters subjectively is solved. The experimental analysis of the two datasets shows that WOA-SMM is superior not only to other classifiers in classification performance, but also has higher in convergence accuracy and speed for rolling bearing fault diagnosis. INDEX TERMS WOA-SMM; Whale optimization algorithm; Fault diagnosis; Rolling bearing; Multisynchrosqueezing transform I. INTRODUCTION