Exploring the fresh and rheology properties of 3D printed concrete with fiber reinforced composites (3DP-FRC): a novel approach using machine learning techniques

作者：Risul Islam Rasel, Md Minaz Hossain, Md Hasib Zubayer, Chaoqun Zhang · 发表于：Materials Research Express · 年份：2024 · DOI：10.1088/2053-1591/ad9890 · 被引用次数：5 · 研究领域：Innovations in Concrete and Construction Materials、Innovative concrete reinforcement materials、Additive Manufacturing and 3D Printing Technologies

Abstract This study focuses on the prediction models for four parameters related to the fresh and rheological properties of 3DP-FRC: spreading diameters (S PD ), dynamic yield stress (DYs), static yield stress (SYs) and plastic viscosity (PV), respectively. Five machine learning (ML) algorithms were employed, namely artificial neural network (ANN), random forest (RF), decision tree (DT), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost). An extensive dataset was compiled including 373 (S PD ) and 219 (SYs, DYs, PV) from various literature comprising experimental results. Fifteen input parameters were identified as the most influential factors affecting the fresh and rheological properties. These parameters include OPC, W/B, W/S, FA, LP, SF, SP, VMA, W, h f , R i , AR, t sf , F t , and S time /R time . This study found strong correlations between the developed ML models and the experimental outcomes from both the training and testing datasets. The models demonstrated exceptional accuracy and provided precise predictions for S PD , SYs, DYs, and PV. The correlation coefficients (R 2 ) ranged from 0.94 to 0.99 for S PD , 0.93 to 0.99 for SYs, 0.98 to 0.99 for DYs, and 0.98 to 1.00 for PV, with consistent results observed across both the training and testing datasets. Moreover, the model’s precision was assessed using different error metrics, including root mean square error (RMSE), mean square error (MSE), coefficient of variation in root-mean-square error...