Prediction of Compressive Strength for Fly Ash-Based Concrete: Critical Comparison of Machine Learning Algorithms

Document Type : Regular Article

Authors

1 Graduate Students, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India

2 Ph.D. Student, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India

3 Assistant Professor, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India

Abstract

In the construction field, compressive strength is one of the most critical parameters of concrete. However, a significant amount of physical effort and natural raw materials are required to produce concrete. In addition, the curing period of concrete for at least 28 days is a must for attaining the required compressive strength. Various types of industrial and agricultural wastes have been used in concrete to reduce cement consumption and problems due to its production. Therefore, considering such constraints, the application of Artificial Intelligence (AI) has been widely used in the current scenarios to predict the desired output parameters. In the present study, 12 input parameters have been considered along with 455 data points and nine Machine Learning (ML) models to forecast the compressive strength of Fly Ash (FA) based concrete. The output from the models has been compared to find the best-fit model in terms of numerous analyses such as visual descriptive statistics, errors, R2, Taylor’s diagram, Feature Importance (FI), and scatter plots. Based on the analysis of the current study, Decision Tree (DT) and Gradient Boost (GB) were found to be the best-fit model because of the least errors and higher R2 values as compared to other models.

Keywords

Main Subjects

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  • Receive Date: 24 July 2022
  • Revise Date: 06 February 2023
  • Accept Date: 04 April 2023
  • First Publish Date: 04 April 2023

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