It remains a major issue to assess health condition and degree of vibration damage of flood discharge structure by working features in recent years. In the process of acquisition and transmission, because vibration signals are susceptible to interference from high-frequency white noise and low-frequency water flow noise, they are usually shown in the form of nonstationary random signals with low signal to noise ratio. Modal information is hard to be precisely recognized as the character of structural vibration is drowned into the strong noise. In order to remove the noise and preserve structural characteristic information, a new characteristic information extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) entropy (CEEMDAN-PE) is proposed. Firstly, the vibration signal is decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN, and then low-frequency water flow noise can be filtered out through spectrum analysis of each IMF component. Secondly, the noise degree of each IMF is determined by permutation entropy and high-frequency noise in IMFs is filtered out by singular value decomposition. Finally, the noise elimination IMFs are reconstructed to obtain the operating characteristic information of flood discharge structure. The effectiveness of the proposed method on characteristic information extraction is validated by a simulation experiment. Furthermore, the proposed method was applied to the 5th overflow section of Three Gorges Dam and the analysis results show that the CEEMDAN-PE method can effectively remove the noise and extract dominant frequencies of flood discharge structure, which provides foundation for health monitoring and damage identification of flood discharge structure with a strong engineering practicability.

In order to clarify the effect of the MgO–silica fume (SF) ratio on the reaction process of the MgO–SiO₂–H₂O system, the reaction products and degree of reaction were characterized. Furthermore, the parameters of the reaction thermodynamics were calculated and the reaction kinetics were deduced. The results indicate that a large amount of Mg(OH)₂ and small quantities of magnesium silicate hydrate (M–S–H) gels were generated upon dissolution of MgO. However, the M–S–H gels were continuously generated until the SF or Mg(OH)₂ was consumed completely. For a MgO dosage less than 50% of the total MgO–SiO₂–H₂O system, the main product was M–S–H gel, while for a MgO dosage greater than 50%, the main product was Mg(OH)₂. The results indicate that M–S–H gels have greater stability than Mg(OH)₂, and the final reaction product was prone to be M–S–H gels. Based on the experimental values, an equation is proposed for the reaction kinetics of MgO.

Operation feature extraction of flood discharge structures under ambient excitation has attracted increasing attention in recent years. However, the vibration signal of flood discharge structures is a nonstationary random signal with low signal-to-noise ratio, which is mixed with lots of low-frequency water flow noise and high-frequency white noise. It is difficult to excavate the hidden vibration characteristic information accurately. To solve the problem, we propose a novel denoising method called improved variational mode decomposition. As an improved method of variational mode decomposition, improved variational mode decomposition can effectively determine the decomposition mode number of variational mode decomposition by using the mutual information method. Furthermore, improved variational mode decomposition is combined with a variance dedication rate to extract the overall operation characteristic information of the structure. In order to evaluate the applicability and effectiveness of the proposed improved variational mode decomposition–variance dedication rate method, we compare the denoising results of simulation signals produced by an improved variational mode decomposition–variance dedication rate with those produced by digital filter, wavelet threshold, empirical mode decomposition, empirical wavelet transform, complete ensemble empirical mode decomposition with adaptive noise, and improved variational mode decomposition methods and find a better performance of the improved variational mode decomposition–variance dedication rate method. In addition, the proposed method is applied to the Three Gorges Dam, and the results show that the improved variational mode decomposition–variance dedication rate method can effectively remove heavy background noises and extract the operation characteristic information of the flood discharge structure, which contributes to health monitoring and damage identification of the flood discharge structure.

In this study, albumin egg (AE) induced layered rectorite (REC) to exfoliate by electrostatic forces between the molecular chains of AE and the interlayer of REC, which was confirmed by small angle X-ray diffraction. The composite nanofibrous mats with the exfoliated layered rectorite were successfully fabricated via electrospinning poly(vinyl alcohol) (PVA)/AE/REC solutions with different mass ratios. The morphology of the nanofibrous membranes was examined by field emission scanning electron microscopy. Energy-dispersive X-ray spectroscopy and Fourier transform infrared spectra showed the existence of REC in the composite nanofibrous mats. Besides, the wide angle X-ray diffraction and Fourier transform infrared spectra results demonstrated the crystalline structure of PVA was changed by formic acid. The differential scanning calorimetry and the thermo-gravimetric analysis results suggested that REC could slightly improve the thermal stability of the composite nanofibrous mats. MTT assay showed the nanofibrous mats were nontoxic and beneficial to the proliferation of L929 cells.