Fiber reinforced self compacting concrete workability properties prediction and optimization of mix using machine learning modeling

ABSTRACT Self Compacting Concrete (SCC) is an engineered concrete manufactured in such a way that it can compact itself independently, without any external vibrations or equipment. The self-weight of the SCC is specifically higher than that of the Conventionally Vibrated Concrete (CVC) because of more fines in the SCC. The fines help to achieve self-compaction, but at the same time, it creates more shrinkage in the SCC. The fibers were used in the SCC to reduce these shrinkages. This investigation uses, various percentages of natural kenaf fibers such as 0.1%, 0.2%, 0.3% and 0.4%. Due to this variation in the fiber fractions, the workability properties are affected in the SCC. If the workability gets affected, the concrete does not have the self-compaction property and behaves as CVC. Hence the current research focuses on the SCC Workability Properties (WP) and optimization of SCC mix utilizing machine learning techniques. Considering the advantages of past research, a model was developed with a fusion approach that incorporates Principal Component Analysis (PCA) for SCCWP. Initially, the dataset is processed with the help of standardization using an SCC mix. The processed output is fed into principal component analysis for a dimensional shift from high to low. Then the low dimensional data is given as input to the effect of various workability properties of Fiber Reinforced Self Compacting Concrete (FRSCC) which was modeled using a Support Vector Machine (SVM) and Logistic Regression (LR). A comparison has been made, logistic regression produces a more reliable outcome compared to support vector machine in terms of all the evaluation metrics used.