Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO<sub>2</sub> Emissions Trade-Offs
Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability,...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/15/2640 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849770696925773824 |
|---|---|
| author | Yuzhuo Zhang Jiale Peng Zi Wang Meng Xi Jinlong Liu Lei Xu |
| author_facet | Yuzhuo Zhang Jiale Peng Zi Wang Meng Xi Jinlong Liu Lei Xu |
| author_sort | Yuzhuo Zhang |
| collection | DOAJ |
| description | Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this gap, this study proposes an AI-assisted framework integrating machine learning (ML) and Multi-Objective Optimization (MOO) to achieve a sustainable GPC design. A robust database of 1154 experimental records was developed, focusing on five key predictors: cement content, water-to-binder ratio, aggregate composition, glass powder content, and curing age. Seven ML models were optimized via Bayesian tuning, with the Ensemble Tree model achieving superior accuracy (R<sup>2</sup> = 0.959 on test data). SHapley Additive exPlanations (SHAP) analysis further elucidated the contribution mechanisms and underlying interactions of material components on GPC compressive strength. Subsequently, a MOO framework minimized unit cost and CO<sub>2</sub> emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. The Pareto-optimal solutions provide actionable guidelines for engineers to align GPC design with circular economy principles and low-carbon policies. This work advances sustainable construction practices by bridging AI-driven innovation with building materials, directly supporting global goals for waste valorization and carbon neutrality. |
| format | Article |
| id | doaj-art-5c6db2e6f9194a7ba9a839aa87ff853e |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-5c6db2e6f9194a7ba9a839aa87ff853e2025-08-20T03:02:55ZengMDPI AGBuildings2075-53092025-07-011515264010.3390/buildings15152640Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO<sub>2</sub> Emissions Trade-OffsYuzhuo Zhang0Jiale Peng1Zi Wang2Meng Xi3Jinlong Liu4Lei Xu5School of Management, Shenyang Jianzhu University, Shenyang 110168, ChinaSchool of Management, Shenyang Jianzhu University, Shenyang 110168, ChinaSchool of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, ChinaSchool of Management, Shenyang Jianzhu University, Shenyang 110168, ChinaSchool of Civil Engineering, Southeast University, Nanjing 211189, ChinaLaboratory of Construction Materials, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandGlass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this gap, this study proposes an AI-assisted framework integrating machine learning (ML) and Multi-Objective Optimization (MOO) to achieve a sustainable GPC design. A robust database of 1154 experimental records was developed, focusing on five key predictors: cement content, water-to-binder ratio, aggregate composition, glass powder content, and curing age. Seven ML models were optimized via Bayesian tuning, with the Ensemble Tree model achieving superior accuracy (R<sup>2</sup> = 0.959 on test data). SHapley Additive exPlanations (SHAP) analysis further elucidated the contribution mechanisms and underlying interactions of material components on GPC compressive strength. Subsequently, a MOO framework minimized unit cost and CO<sub>2</sub> emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. The Pareto-optimal solutions provide actionable guidelines for engineers to align GPC design with circular economy principles and low-carbon policies. This work advances sustainable construction practices by bridging AI-driven innovation with building materials, directly supporting global goals for waste valorization and carbon neutrality.https://www.mdpi.com/2075-5309/15/15/2640sustainable construction materialswaste glasscarbon footprint reductionmulti-objective optimizationmachine learning |
| spellingShingle | Yuzhuo Zhang Jiale Peng Zi Wang Meng Xi Jinlong Liu Lei Xu Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO<sub>2</sub> Emissions Trade-Offs Buildings sustainable construction materials waste glass carbon footprint reduction multi-objective optimization machine learning |
| title | Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO<sub>2</sub> Emissions Trade-Offs |
| title_full | Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO<sub>2</sub> Emissions Trade-Offs |
| title_fullStr | Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO<sub>2</sub> Emissions Trade-Offs |
| title_full_unstemmed | Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO<sub>2</sub> Emissions Trade-Offs |
| title_short | Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO<sub>2</sub> Emissions Trade-Offs |
| title_sort | machine learning assisted sustainable mix design of waste glass powder concrete with strength cost co sub 2 sub emissions trade offs |
| topic | sustainable construction materials waste glass carbon footprint reduction multi-objective optimization machine learning |
| url | https://www.mdpi.com/2075-5309/15/15/2640 |
| work_keys_str_mv | AT yuzhuozhang machinelearningassistedsustainablemixdesignofwasteglasspowderconcretewithstrengthcostcosub2subemissionstradeoffs AT jialepeng machinelearningassistedsustainablemixdesignofwasteglasspowderconcretewithstrengthcostcosub2subemissionstradeoffs AT ziwang machinelearningassistedsustainablemixdesignofwasteglasspowderconcretewithstrengthcostcosub2subemissionstradeoffs AT mengxi machinelearningassistedsustainablemixdesignofwasteglasspowderconcretewithstrengthcostcosub2subemissionstradeoffs AT jinlongliu machinelearningassistedsustainablemixdesignofwasteglasspowderconcretewithstrengthcostcosub2subemissionstradeoffs AT leixu machinelearningassistedsustainablemixdesignofwasteglasspowderconcretewithstrengthcostcosub2subemissionstradeoffs |