Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods

• Published in: Materials Vol. 15; no. 20; p. 7344
• Main Authors: Alkadhim, Hassan Ali, Amin, Muhammad Nasir, Ahmad, Waqas, Khan, Kaffayatullah, Nazar, Sohaib, Faraz, Muhammad Iftikhar, Imran, Muhammad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2022; MDPI
• Subjects: Analysis; Artificial intelligence; building material; Cement; Cement constituents; cement mortar; Coefficient of variation; Compressive strength; Evaluation; Experimental methods; Flexural strength; Ingredients; Landfill; Machine learning; Methods; Mortars (material); Natural resources; Powders; Raw materials; Recycling; Research methodology; Shear strength; waste glass powder; Waste materials
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma15207344

This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R2), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.