Compressive Sterngth Analysis of Glass Fiber Reinforced Concrete with Recycle Aggregates Using Long Short Term Memory

The construction resources of scarcity like labour, money, land, material and technique are developed a high chaos and that nations are faced issues of less Gross Domestic Product (GDP). This study concentrates on reutilize the laboratory waste utilized aggregate and analysed a mechanic property of concrete with glass fiber and differing a percent of recycle aggregates. The study concentrates on utilize of huge percent of recycle aggregate and analysed the optimal mixture which can utilized to structure application through updating glass fibers without compressing a parameter of strength. It is created for utilizing a Recycled Coarse Aggregate (RCA) and Recycled Crushed Glass (RCG) for generating Mixtures of Self-Compacting Concrete (SCC). The Natural Coarse Aggregate (NCA) is replaced through RCA from 0 -100 percent and Natural Fine Aggregate (NFA) is exchanged through RCG from 0 - 20 percent. The mechanic properties strength of compressive, flexural and split tensile in 28 and 56 days are analysed. That represents a mixture of CR80G0.25 is utilized for compression members such as columns and mixture of CR70G0.25 is utilized in building of slabs and beams. Based on these data, a deep learning based prediction model is proposed in this research to evaluate the compressive strength of recycled aggregated concrete. Experimental results of proposed Spatial Attention Mechanism based Long Short Term Memory (SAM-LSTM) model used for compressive strength prediction of Recycled aggregate concrete achieved Root Mean Squared Error (RMSE) OF 4.236 which is less than existing methods such as gradient boosting.