AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass Mortar
Waste glass powder (WGP) faces challenges in recycling and regeneration, which is used as a partial substitute for concrete components, with its macro-mechanical properties being investigated. This study aims to elucidate the extent to which various variables affect the unconfined compressive streng...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/11/1866 |
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| author | Fei Wu Xin Zhang Yanan Zhang Dong Wang Hua Tian Jing Xu Wei Luo Yuzhuo Zhang |
| author_facet | Fei Wu Xin Zhang Yanan Zhang Dong Wang Hua Tian Jing Xu Wei Luo Yuzhuo Zhang |
| author_sort | Fei Wu |
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| description | Waste glass powder (WGP) faces challenges in recycling and regeneration, which is used as a partial substitute for concrete components, with its macro-mechanical properties being investigated. This study aims to elucidate the extent to which various variables affect the unconfined compressive strength (UCS) and alkali–silica reactivity (ASR) of waste glass incorporated concrete. Initially, in the experimental procedure, 291 data points for the UCS and 485 data points for the ASR were obtained from laboratory tests. Subsequently, four machine learning models were introduced, including Gradient Boosting Regressor, Random Forest, Hist Gradient Boosting Regressor, and XGBoost. Their performance was analyzed and compared based on evaluation indexes. The findings reveal that Gradient Boosting Regressor accurately models the actual data distribution, generating reliable synthetic data. Partial dependence plots (PDPs) were used to understand the impact of individual features on glass concrete UCS and ASR, and Shapley additive explanation (SHAP) values were used to analyze the predictive output influenced by the contribution of each feature. The feature interaction effects analyzed through PDP indicate that UCS is highest when WGP is 202.5 kg/m<sup>3</sup>, and ASR is maximized when WGP is 708.75 kg/m<sup>3</sup>. The SHAP value analysis results reveal that the “alkali” feature exerts the most pronounced influence on the UCS model predictions. Conversely, in the case of the ASR model, the “curing duration” feature emerges as the primary driver of its predictions. |
| format | Article |
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| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-e1a2e819711b4db2991f5f9efc6624892025-08-20T02:23:44ZengMDPI AGBuildings2075-53092025-05-011511186610.3390/buildings15111866AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass MortarFei Wu0Xin Zhang1Yanan Zhang2Dong Wang3Hua Tian4Jing Xu5Wei Luo6Yuzhuo Zhang7Jiangxi Traffic Engineering Assembly Manufacturing Co., Ltd., Nanchang 330000, ChinaChina Testing & Certification International Group Shanghai Co., Ltd., Shanghai 201203, ChinaSchool of Management, Shenyang Jianzhu University, Shenyang 110168, ChinaLiyang Market Comprehensive Inspection and Testing Center, Liyang 213300, ChinaChina Construction Fifth Engineering Bureau Co., Ltd., Changsha 410004, ChinaTongji University Library, Shanghai 200092, ChinaJiangxi Traffic Engineering Assembly Manufacturing Co., Ltd., Nanchang 330000, ChinaChina Testing & Certification International Group Shanghai Co., Ltd., Shanghai 201203, ChinaWaste glass powder (WGP) faces challenges in recycling and regeneration, which is used as a partial substitute for concrete components, with its macro-mechanical properties being investigated. This study aims to elucidate the extent to which various variables affect the unconfined compressive strength (UCS) and alkali–silica reactivity (ASR) of waste glass incorporated concrete. Initially, in the experimental procedure, 291 data points for the UCS and 485 data points for the ASR were obtained from laboratory tests. Subsequently, four machine learning models were introduced, including Gradient Boosting Regressor, Random Forest, Hist Gradient Boosting Regressor, and XGBoost. Their performance was analyzed and compared based on evaluation indexes. The findings reveal that Gradient Boosting Regressor accurately models the actual data distribution, generating reliable synthetic data. Partial dependence plots (PDPs) were used to understand the impact of individual features on glass concrete UCS and ASR, and Shapley additive explanation (SHAP) values were used to analyze the predictive output influenced by the contribution of each feature. The feature interaction effects analyzed through PDP indicate that UCS is highest when WGP is 202.5 kg/m<sup>3</sup>, and ASR is maximized when WGP is 708.75 kg/m<sup>3</sup>. The SHAP value analysis results reveal that the “alkali” feature exerts the most pronounced influence on the UCS model predictions. Conversely, in the case of the ASR model, the “curing duration” feature emerges as the primary driver of its predictions.https://www.mdpi.com/2075-5309/15/11/1866discarded glassmachine learningunconfined compressive strengthalkali–silica reactionpartial dependence plotsSHAP |
| spellingShingle | Fei Wu Xin Zhang Yanan Zhang Dong Wang Hua Tian Jing Xu Wei Luo Yuzhuo Zhang AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass Mortar Buildings discarded glass machine learning unconfined compressive strength alkali–silica reaction partial dependence plots SHAP |
| title | AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass Mortar |
| title_full | AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass Mortar |
| title_fullStr | AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass Mortar |
| title_full_unstemmed | AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass Mortar |
| title_short | AI-Based Variable Importance Analysis of Mechanical and ASR Properties in Activated Waste Glass Mortar |
| title_sort | ai based variable importance analysis of mechanical and asr properties in activated waste glass mortar |
| topic | discarded glass machine learning unconfined compressive strength alkali–silica reaction partial dependence plots SHAP |
| url | https://www.mdpi.com/2075-5309/15/11/1866 |
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