Simulation of Microbial Enhanced Recycled Aggregate Preparation System Based on Artificial Intelligence and Embedded Processor

Abstract Construction waste recycling is prevented by its high water absorption rate and the weak bond of the new cement paste. This problem is exacerbated by the fact that Requirements for Accreditation Scheme (RFAS) recycled porridge production. To explore the possibility of repeated use of the RFAS system, it is proposed in the integrated study of Microbial Carbonate Precipitation (MCP) derived from RFAS. Due to its rapid construction, the demand for concrete raw materials, especially coarse aggregates, increases the risk of premature erosion of natural resources. Alternative energy sources for raw materials delay the possibility of this initial exhaustion. Recycled Coarse Aggregate (RCA) played an important role as a ready-mixed concrete substitute. Natural Coarse Aggregate (NCA) changes. Previous studies have shown that RCA's special reinforcing concrete lags behind NCA. I hope that may be due to the microstructure of the calcium carbonate hailstone. Recycling aggregate has high water absorption and crushing indicators. In recent years, the integrated cycle has changed the new treatment model biopsy. Degradation-enhanced protocols have been implemented to change the nature of the motor in circulation. Famous for using the information protocol learning machine based on extracting and source information, it comes on a hardware platform provided that vendors compete between the FPGA and the GPU to run intensive computational learning algorithms quickly and efficiently. Deep learning, which is considered to be the key point by which drivers compare the most advanced machine learning applications.