Efficient deep-learning model for predicting mechanical strength of carbon nanotube-reinforced cementitious composites: database acquisition, framework development and implementation

Abstract

In response to the growing demand for accurate models predicting the mechanical strength of carbon nanotube (CNT) reinforced cementitious composites, this study introduces a precise deep-learning model. The model, built upon a comprehensive dataset of 219 entries, incorporates 14 input variables considering various composites, cement types, and CNT characteristics. Notably, analysis of the data demonstrates a nuanced understanding of material properties, covering cementitious composites with compressive strength (CS) and flexural strength (FS) ranging from 15 to 140MPa and 3 to 17MPa, respectively. Employing a regularized deep learning neural network architecture, the model exhibits promising performance metrics, with the training dataset showcasing the lowest mean absolute error (MAE) of 2.036. The training and validation datasets demonstrate a high correlation coefficient (0.991 and 0.917, respectively). However, higher MAE values are observed for the testing and validation datasets, indicating potential limitations in generalization. Furthermore, the model's robustness is highlighted by its ability to surpass earlier studies in accuracy for validation datasets. The study elucidates a nonlinear trend in FS and the influence of CNT size on CS, with implications for material design and construction practices.