Multi-objective design optimization for graphite-based nanomaterials reinforced cementitious composites: A data-driven method with machine learning and NSGA-II

Graphite-based nanomaterials (GNs) are promising conductive fillers for producing highly effective electrically conductive cementitious composites (ECCC) and promoting non-destructive structural health monitoring (SHM) methods. Since acceptable mechanical strength and electrical resistivity are both required, the design of GN-reinforced cementitious composites (GNRCC) is a complicated multi-objective optimization problem (MOOP). The present study proposes a comprehensive data-driven method to address this multi-objective design optimization (MODO) issue for GNRCC using machine learning (ML) techniques and non-dominated sorting genetic algorithm (NSGA-II). First, prediction models of uniaxial compressive strength (UCS) and electrical resistivity (ER) of GNRCC are established by Bayesian-tuned XGBoost with prepared experimental datasets. The results show that they have excellent performance in predicting both properties with high R-2 (0.95 and 0.92, 0.99 and 0.98) and low mean absolute error (MAE) scores (1.24 and 3.44, 0.15 and 0.22). The influence of critical features on GNRCC's properties are quantified by ML theories. This helps determine the variables to be optimized and define their constraints for the MODO. Finally, the MODO program is developed on the basis of NSGA-II. It optimizes GNRCC's properties of UCS and ER simultaneously with the proposed prediction models as objective functions. It successfully achieves a set of Pareto solutions, which can facilitate appropriate parameters selections for the GNRCC design.

Automated summary

This study proposes a comprehensive data-driven method for the multi-objective design optimization of GN-reinforced cementitious composites (GNRCC) using machine learning techniques and a non-dominated sorting genetic algorithm. It establishes prediction models for the properties of GNRCC and quantifies the influence of critical features. The proposed method successfully achieves a set of Pareto solutions for the optimization of GNRCC properties.