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Predictive Models with Applicable Graphical User Interface (GUI) for the Compressive Performance of Quaternary Blended Plastic-Derived Sustainable Mortar

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Publicado en:Buildings vol. 15, no. 11 (2025), p. 1932
Autor principal: Aïssa, Rezzoug
Otros Autores: Elabbasy Ahmed A. Abdou, Alqurashi Muwaffaq, AlAteah, Ali H
Publicado:
MDPI AG
Materias:
Green development
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
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2075-5309
DOI:10.3390/buildings15111932
Fuente:Engineering Database