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

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

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

S. A. Masjuki | S. A. Saad | A.M Nadimalla | M. Ali

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