Content area
Machine learning (ML) models in material science and construction engineering have significantly improved predictive accuracy and decision making. However, the practical implementation of these models often requires technical expertise, limiting their accessibility for engineers and practitioners. A user-friendly graphical user interface (GUI) can be an essential tool to bridge this gap. In this study, a sustainable approach to improve the compressive strength (C.S) of plastic-based mortar mixes (PMMs) by replacing cement with industrial waste materials was investigated using ML models such as support vector machine, AdaBoost regressor, and extreme gradient boosting. The significance of key mix parameters was further analyzed using SHapley Additive exPlanations (SHAPs) to interpret the influence of input variables on model predictions. To enhance the usability and real-world application of these ML models, a GUI was developed to provide an accessible platform for predicting the C.S of PMMs based on input material proportions. The ML models demonstrated strong correlations with experimental results, and the insights from SHAP analysis further support data-driven mix design strategies. The developed GUI serves as a practical and scalable decision support system, encouraging the adoption of ML-based approaches in sustainable construction engineering.
Details
Accessibility;
Cement hydration;
Datasets;
Graphical user interface;
Sustainability;
Industrial wastes;
Machine learning;
Landfill;
Prediction models;
User interface;
Decision support systems;
Construction engineering;
Recycling;
Artificial intelligence;
Support vector machines;
Waste materials;
Databases;
Variables;
Mortars (material);
Data collection;
Algorithms;
Reinforced concrete;
Waste management;
Morphology;
Decision making;
Compressive strength
; Elabbasy Ahmed A. Abdou 2 ; Alqurashi Muwaffaq 3
; AlAteah, Ali H 4
1 College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
2 Civil and Architectural Engineering Department, College of Engineering and Computer Sciences, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia
3 Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; [email protected]
4 Department of Civil Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia; [email protected]