TY - JOUR ID - 169218 TI - Prediction of Compressive Strength for Fly Ash-Based Concrete: Critical Comparison of Machine Learning Algorithms JO - Journal of Soft Computing in Civil Engineering JA - SCCE LA - en SN - AU - Wadhawan, Sifti AU - Bassi, Akshita AU - Singh, Rajwinder AU - Patel, Mahesh AD - Graduate Students, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India AD - Ph.D. Student, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India AD - Assistant Professor, Department of Civil Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India Y1 - 2023 PY - 2023 VL - 7 IS - 3 SP - 68 EP - 110 KW - Compressive strength KW - Fly ash KW - Machine Learning KW - prediction DO - 10.22115/scce.2023.353183.1493 N2 - 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. UR - https://www.jsoftcivil.com/article_169218.html L1 - https://www.jsoftcivil.com/article_169218_72e6f1b2b45196a5e0aa6dd1ea8b0006.pdf ER -