Synthetic limestone sand has advantages, such as stable quality and adjustable particle size distribution, and has gradually substituted high-quality natural sand as a fine aggregate in concrete production. The project team has prepared Magnesium Potassium Phosphate Cement (MKPC) mortar by replacing part of the river sand with machine-made limestone sand in equal amounts, which proves that its physical and mechanical properties are obviously better than mortar prepared by whole river sand. However, the research on the impact of machine-made limestone sand on the durability of MKPC mortar has not been carried out. As the repairing material of concrete structures, the frost resistance of MKPC mortar must be evaluated. In this study, the effect of synthetic limestone sand on the frost resistance of Magnesium Potassium Phosphate Cement (MKPC) mortar was investigated by characterizing the strength, mass loss rate, and water absorption of specimens subjected to freeze–thaw cycling. MKPC mortars prepared using solely river sand (M0) or limestone sand (M1) were completely degraded after 225 freezing–thawing cycles in water, whereas the flexural and compressive strengths of MKPC mortar (M2) prepared using both river and synthetic limestone sands was 29.3 and 22.0% of the initial strengths, respectively. The water freeze–thaw resistance of M2 specimens were significantly higher than that of M0 and M1 specimens, and the sulfate freeze–thaw resistance of M1 and M2 were significantly higher than that of M0. The mass loss of MKPC mortar is not more than 0.4% when it is frozen and thawed 225 times in water and 5% Na2SO4 solution, which is far lower than the damage standard of 5%. Based on the favorable composition of the two aggregates, the initial open porosity of M2 was relatively low, owing to the lower water–cement ratio of the mortar at the same flow rate.
As a new type of riser connecting offshore platforms and submarine pipelines, steel catenary risers (SCRs) are generally subject to waves and currents for a long time, thus it is significant to fully evaluate the SCR structure’s safety. Aiming at the damage identification of the SCR, the acceleration time series signals at multiple locations are taken as the damage characteristics. The damage characteristics include spatial information of the measurement point location and time information of the acquisition signal. Therefore, a convolutional neural network (CNN) is employed to obtain spatial information. Considering the variable period characteristics of the acceleration time series of the SCR, a gated recurrent unit (GRU) neural network is utilized to study these characteristics. However, neither a single CNN nor GRU model can simultaneously obtain temporal and spatial data information. Therefore, by combining a CNN with a GRU, the CNN-GRU model is established. Moreover, the hyperparameters of deep learning models have a significant influence on their performance. Therefore, particle swarm optimization (PSO) is applied to solve the hyperparameter optimization problem of the CNN-GRU. Thus, the PSO-CNN-GRU (PCG) model is established. Subsequently, an SCR damage identification method based on the PCG model is presented to predict the damage location and degree by SCR acceleration time series. By analyzing the SCR acceleration data, the prediction performances of the PCG model and the PSO optimization capacity are verified. The experimental results indicate that the identification result of the proposed PCG model is better than that of several existing models (CNN, GRU, and CNN-GRU).
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