Prediction of compressive strength of cementitious grouts for semi-flexible pavement application using machine learning approach

Abstract

This study involves the preparation of cement grout samples by varying proportions of superplasticizer (SP) and water-cement (w/c) ratio (0.25–0.45) which were tested for computing flow value and 1-day, 7-day, and 28-day compressive strength. With 75 data points a neural network model was developed to predict the 28-day compressive strength based on w/c ratio, superplasticizer percentage, flow value, and compressive strengths of 1-day and 7-day as input variables. Due to the better performance of Artificial Neural Networks (ANN) over other applied models, this study predicts a confident pattern of relationship between w/c ratio (indirect), superplasticizer (direct up to 3%), and the corresponding strength characteristics of the study samples. The prediction power of the developed model has been improved from R2 = 0.959 to R2 = 0.984 by optimizing the number of neurons, activation function, and optimizer. The best performance was observed in the case of Model45-R_A. The same model was used for assessing the effect of the percentage of superplasticizer and water cement ratio on 28 days compressive strength where the compressive strength of 73.941 MPa was recorded in the case of 3% superplasticizer and w/c of 0.25.

Keywords

• Compressive strength
• Flow value
• Cement grouts
• Semi-flexible pavement
• Machine learning
• Artificial neural network