Comparative analysis of conventional and ensemble machine learning models for predicting split tensile strength in thermal stressed SCM-blended lightweight concrete

Abstract

This study aimedto predict the split tensile strength (STS) of lightweight concrete having silica fume (SF) as supplementary cementitious material (SCM) under thermal stresses. Machine learning (ML) models including a conventional machine learning (CML) model as Artificial Neural Networks (ANN) and ensemble machine learning (EML) models, particularly Random Forest (RF) and Gradient Boosting (GB) were developed to predict the STS. Dataset comprising of 96 instances was splitted into 80% for training and 20% for testing phase respectively. With the help of the 80% training data and subsequent hyperparameter tuning, various CML and EML models were developed. ML models were analysed statistically for both the training and testing phases, under the model performanceparameters, line diagrams, and Taylor diagrams. After the thorough statistical analysis, it was concluded that the GB as an EML model outperformed all other ML models. This was demonstrated by the fact that it had constantly higher model performance parameter values of RMSE, MAE and R2 as 0.151, 0.115 and 0.977 for training and 0.23, 0.196 and 0.908 for testing respectively. Also, from the line diagrams which shows a good correlation with the actual and predicted STS values and from the Taylor diagram of the various ML models for both the phases, it was concluded that the GB model is found superior model for this specific study.