Abstract
To truly reflect the durability characteristics of concrete subjected to multiple factors under complex environmental conditions, it is necessary to discuss the prediction of its durability. In response to the problem of durability prediction of traditional concrete structures, there is a low prediction accuracy, and the predicted time is long, and a concrete structural durability prediction method based on the deep belief network is proposed. The influencing factors of the concrete structural durability parameters are analyzed by two major categories of concrete material and external environmental conditions, and the transmission of chloride ions in the concrete structure is described. According to the disconnection of the steel bars, the durability of the concrete structure is started, and the determination is determined. The concrete structural antiflexural strength, using a deep belief network training concrete structural antiflexural strength judgment data, constructs a concrete structural durability predictive model and completes the durability prediction of the concrete structure based on the deep belief network. The proposed prediction method based on the deep belief network has a high prediction accuracy of 98% for the durability of concrete column structures. The simulation results show that the concrete structural durability’s prediction accuracy is high and the prediction time is short. The prediction of concrete durability discussed here has important guiding significance for the improvement of concrete durability test methods and the improvement of concrete durability evaluation standards in China.
Highlights
When the concrete is in an unsaturated state, or when the saturation is uneven, the chloride ion enters the concrete, which is mainly capillary absorption, which is caused by the surface tension in the capillary. e capillary effect is the main transmission method of the chloride ion in the concrete structure in the tidal region and the amorphic region
The lower portion of the rib is immersed in the concrete structure in the water, and there is a chloride ion penetration and diffusion, and the concrete structure of the amorphic region, while the chloride ion diffusion and capillary effects are present
The transmission of chloride ions in the concrete structure is described, and the transmission mode is generally simplified into diffusion transmission, and the diffusion process is used to describe the FICK second law: zC z2C zt D zx2, (1)
Summary
When the axial force reaches the critical value of concrete without crack compression, the calculation formula of the uniform external pressure p is as follows: By substituting the relevant parameters of the two constraint regions into formula (5), the value of uniform external pressure can be determined, the uneven lateral restraint can be provided along the periphery of the concrete, the interference of the strength improvement coefficient in the compression state can be eliminated, the maximum constraint stress of the concrete core region can be determined, and the constraint stress of the two regions can be added [13,14,15]. Erefore, the output data of the depth belief network are input into the BP neural network for the data learning training of the concrete structure buckling strength judgment, and the model parameter θ (W, b, c) and the learning rate ε are obtained after the pretraining process. Where C is the state of the deep belief network memory module and H is the output of the memory module
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