Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods
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...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/20/7344 |
| _version_ | 1797406926178353152 |
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| author | Hassan Ali Alkadhim Muhammad Nasir Amin Waqas Ahmad Kaffayatullah Khan Sohaib Nazar Muhammad Iftikhar Faraz Muhammad Imran |
| author_facet | Hassan Ali Alkadhim Muhammad Nasir Amin Waqas Ahmad Kaffayatullah Khan Sohaib Nazar Muhammad Iftikhar Faraz Muhammad Imran |
| author_sort | Hassan Ali Alkadhim |
| collection | DOAJ |
| description | 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 (R<sup>2</sup>), 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. |
| first_indexed | 2024-03-09T03:33:49Z |
| format | Article |
| id | doaj.art-b6032008737c4e0fa20ebc30bff0232b |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T03:33:49Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-b6032008737c4e0fa20ebc30bff0232b2023-12-03T14:51:40ZengMDPI AGMaterials1996-19442022-10-011520734410.3390/ma15207344Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) MethodsHassan Ali Alkadhim0Muhammad Nasir Amin1Waqas Ahmad2Kaffayatullah Khan3Sohaib Nazar4Muhammad Iftikhar Faraz5Muhammad Imran6Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi ArabiaSchool of Civil and Environmental Engineering (SCEE), National University of Sciences & Technology (NUST), Islamabad 44000, PakistanThis 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 (R<sup>2</sup>), 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.https://www.mdpi.com/1996-1944/15/20/7344cement mortarwaste glass powderbuilding materialcompressive strengthflexural strength |
| spellingShingle | Hassan Ali Alkadhim Muhammad Nasir Amin Waqas Ahmad Kaffayatullah Khan Sohaib Nazar Muhammad Iftikhar Faraz Muhammad Imran Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods Materials cement mortar waste glass powder building material compressive strength flexural strength |
| title | Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods |
| title_full | Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods |
| title_fullStr | Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods |
| title_full_unstemmed | Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods |
| title_short | Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods |
| title_sort | evaluating the strength and impact of raw ingredients of cement mortar incorporating waste glass powder using machine learning and shapley additive explanations shap methods |
| topic | cement mortar waste glass powder building material compressive strength flexural strength |
| url | https://www.mdpi.com/1996-1944/15/20/7344 |
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