Publications Repository - Gdańsk University of Technology

Two-stage concrete (TSC) is a sustainable mate- rial produced by incorporating coarse aggregates into formwork and filling the voids with a specially formulated grout mix. The significance of this study is to improve the predictive accuracy of TSC’s tensile strength, which is essential for optimizing its use in construction applications. To achieve this objective, novel and reliable predictive models were developed using advanced machine learning algorithms, including random forest (RF) and gene expres- sion programming (GEP). The performance of these models was evaluated using important evaluation metrics, including the coefficient of determination (R2), mean absolute error (MAE), mean squared error, and root mean square error (RMSE), after they were trained on a comprehensive dataset. The results suggest that the RF model outperforms the GEP model, as evi- denced by a higher R2 value of 0.94 relative to 0.91 for GEP and reduced MAE and RMSE error values. This suggests that the RF model has a superior predictive capability. Additionally, sensi- tivity analyses and SHapley Additive ExPlanation analysis revealed that the water-to-binder (W/B) ratio was the most influential input parameter, accounting for 51.01% of the pre- dictive outcomes presented in the model. This research emphasizes optimizing TSC design, enhancing material perfor- mance, and promoting sustainable, cost-effective construction.

Authors

• Muhammad Nasir Amin,
• Faizullah Jan link open in new tab ,
• Kaffayatullah Khan,
• Suleman Ayub Khan,
• Muhammad Tahir Qadir,
• dr hab. inż. Marcin Kujawa link open in new tab