Machine Learning Model to Predict Slump, VEBE and Compaction Factor of M Sand and Shredded Pet Bottles Concrete

Abstract A measure of how easy it is to transport, cast, compact and surface finish fresh concrete without any segregation directly proportional workability concrete. The consistency indirectly measures its workability. VeBe test, slump test compaction factor are some the methods that can be used study quick determination strength properties an important element on large construction sites for mass concreting save time cost. It develop a machine learning model predict with help available data. This paper aims experimental data such as slump, incorporated shredded Polyethylene Terephthalate (PET) bottles, Manufactured Sand (M-sand) River sand fine aggregates replacement in mixtures. developed by using different techniques Multiple Linear Regression (MLR) Decision Tree (DTR). As per results obtained DTR performed well than MLR model.