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In order to develop a calculation method for the ultimate flexural capacity of high strain-hardening ultra-high performance concrete (UHPC) T-beams, four-point loading tests on five specimens were carried out. The design parameters were the ratio and the strength of reinforcement. Based on the assumption of plane section, a linear regression analysis was carried out and the ultimate tensile strain of high strain-hardening UHPC was obtained. The analysis results proved that the bond between reinforcement and UHPC is reliable and these two materials can maintain strain consistency before the reinforcement reaches its yield strain. A finite element model of the specimens was established using ANSYS and distributed reinforcement was added to simulate the behavior of UHPC after cracking. Considering the tensile contribution of the high strain-hardening UHPC after cracking, the calculation method for flexural capacity of high strain-hardening UHPC T-beams was derived. The finite element simulation method and the theoretical formula calculation method proposed in this paper are in good agreement with the experimental values, and they can be applied for the theoretical analysis and the design of high strain-hardening UHPC beams. The theoretical calculation method proposed in this paper is compared with calculation methods proposed by standards. The study shows that the proposed calculation method is accurate and generally applicable. K E Y W O R D Sfinite element method, flexural loading capacity, high strain-hardening, theoretical calculation formula, UHPC

The rapid development of neural networks has led to tremendous applications in image segmentation, speech recognition, and medical image diagnosis, etc. Among various hardware implementations of neural networks, silicon photonics is considered one of the most promising approaches due to its CMOS compatibility, accessible integration platforms, mature fabrication techniques, and abundant optical components. In addition, neuromorphic computing based on silicon photonics can provide massively parallel processing and high-speed operations with low power consumption, thus enabling further exploration of neural networks. Here, we focused on the development of neuromorphic computing based on silicon photonics, introducing this field from the perspective of electronic–photonic co-design and presenting the architecture and algorithm theory. Finally, we discussed the prospects and challenges of neuromorphic silicon photonics.

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