Numerical Simulation of Basic Properties of Full-Steel Slag Aggregate Concrete

In this study, the bit-level table of orthogonal test is adopted as the coding form of Genetic Algorithm (GA), and a Back Propagation (BP) neural network prediction model of the basic properties of full steel slag aggregate concrete (FSSAC) is established, and the experiment data are validated with good agreement. The impacts of several parameters including the sand ratio, water-cement ratio, content of steel slag sand, replacement particle size of steel slag sand, content of coarse steel slag, and replacement particle size of coarse steel slag on the compressive strength and expansion rate of the FSSAC are numerically investigated. The results show that the compressive strength of the FSSAC declines with the increase of the sand ratio, water-cement ratio, content of the steel slag sand, or coarse steel slag while it first increases and then decreases as the replacement particle size of steel slag sand or replacement particle size of coarse steel slag increases. The expansion rate of the FSSAC increases as the sand ratio or content of coarse steel slag increases. With a gradual increase of the water-cement ratio, content of steel slag sand, replacement particle size of steel slag sand, or replacement particle size of coarse steel slag, the expansion rate of the FSSAC first increases and then decreases. In addition, the impacts of the three most important parameters (i.e., water-cement ratio, content of steel slag sand, and replacement particle size of steel slag sand) on the stress-strain relationship of the FSSAC stub columns is further numerically studied.

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