Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches

Muhammad Faisal Javed; Muhammad Fawad; Rida Lodhi; Taoufik Najeh; Yaser Gamil

doi:10.1038/s41598-024-57896-0

Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches

Sci Rep. 2024 Apr 10;14(1):8381. doi: 10.1038/s41598-024-57896-0.

Authors

Muhammad Faisal Javed¹, Muhammad Fawad^{2

3}, Rida Lodhi⁴, Taoufik Najeh⁵, Yaser Gamil⁶

Affiliations

¹ Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, 23640, Pakistan. arbabfaisal@cuiatd.edu.pk.
² Silesian University of Technology Poland, Gliwice, Poland.
³ Budapest University of Technology and Economics Hungary, Budapest, Hungary.
⁴ Department of Urban and Regional Planning, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
⁵ Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden. taoufik.najeh@ltu.se.
⁶ Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.

Abstract

Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R²) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.

Keywords: Compressive strength prediction; Construction engineering; Machine learning models; Preplaced aggregate concrete; Two-stage concrete.