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Structural reliability analysis is of critical importance for ensuring the safety of many engineering systems. This paper proposes an efficient structural reliability analysis method based on stochastic configuration networks (SCNs), which is constructed by learning the real performance function of the structure. In this article, two kinds of innovative surrogate models of performance function are constructed based on the trained SCNs. To improve the approximation accuracy and efficiency of SCNs for complex high-dimensional performance functions, the improved high-dimensional model representation is applied to decompose the multivariate function into a combination of several low-dimensional functions. An innovative SCN-based learning strategy is proposed to simultaneously approximate all the low-dimensional component functions. By performing Monte Carlo Simulation on the established SCN-based surrogate models, we can successfully evaluate the failure probability of many complex multi-dimensional engineering structures. The proposed SCN-based reliability analysis method provides a powerful tool for estimating the failure probability of complex multi-dimensional engineering structures. Effectiveness of the proposed method in terms of accuracy and efficiency as well as stability is demonstrated through several numerical experiments. [ABSTRACT FROM AUTHOR]

Abstract View factor plays an important role in evaluation of radiative heat transfer. This paper gives a detailed account of setting up the Monte Carlo calculation models of view factors between some complex surfaces. Main purpose is to calculate view factor between cold-rolled strip and W-shaped radiant tube in continuous annealing furnace. Given advantages of Monte Carlo method, it has been used to calculate view factor between the strip and radiant tube. Firstly, the Monte Carlo calculation models for view factor between rectangular plane and parallel cylinder and for view factor between rectangular plane and torus are established and verified, respectively. Then, according to reciprocity and superposition rules of view factors, the Monte Carlo calculation model for view factor between the strip and radiant tube is established by the above models. By comparing with analytical expression, or checking reciprocity after performing the Monte Carlo analysis in each direction between surface pairs, the calculation errors of above Monte Carlo models are analyzed and the results show that the Monte Carlo method is effective and reliable in calculating view factors between some complex surfaces. [ABSTRACT FROM AUTHOR]

The ultra-early age strength of shotcrete with Portland cement-based materials in various supporting constructions is crucial for safety and engineering efficiency. However, concerns exist about the low strength and uncertain mechanism when using aluminum sulfate, the main component of most-used alkali-free accelerators. This study addresses these concerns by introducing ettringite seeds into the Portland cement system with aluminum sulfate. Significant improvement in ultra-early age compressive strength of mortar, i.e., 242 % at 6 h and 201 % at 8 h, was achieved by mere 1 % seed addition. Analyses of hydration heat, composition and microstructure demonstrate that the ettringite seeds mainly affect the mechanical performance by forming a more prolonged and coarser ettringite skeleton, rather than directly accelerating cement hydration. Such an enhanced skeleton was proved to establish stronger interactions between particles in the Monte Carlo simulations. Additionally, the synergistic effect of the ettringite skeleton and C–S–H gel on ultra-early age strength was also explored. These proposed strengthening mechanisms were verified by the C 3 S and equivalent CaCO 3 systems. [Display omitted] [ABSTRACT FROM AUTHOR]