Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved with reservations]
Background Cement-zeolite improved sand can be used in diverse civil engineering applications. However, earlier research has not duly optimized its production process to attain best mechanical strength, lowest cost, and least environmental impact. This study proposes a multi-objective optimization a...
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2024-04-01
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| author | Hossein MolaAbasi Satinder Kaur Brar Amin Tanhadoust Sepideh Nasrollahpour Omolbanin Ataee Moncef L. Nehdi |
| author_facet | Hossein MolaAbasi Satinder Kaur Brar Amin Tanhadoust Sepideh Nasrollahpour Omolbanin Ataee Moncef L. Nehdi |
| author_sort | Hossein MolaAbasi |
| collection | DOAJ |
| description | Background Cement-zeolite improved sand can be used in diverse civil engineering applications. However, earlier research has not duly optimized its production process to attain best mechanical strength, lowest cost, and least environmental impact. This study proposes a multi-objective optimization approach using back-propagation neural network (BPNN) to predict the mechanical strength, along with an adaptive geometry estimation-based multi-objective evolutionary algorithm (AGE-MOEA) to identify the best parameters for cement-zeolite-improved sand, filling a long-lasting research gap. Methods A collection of unconfined compression tests was used to evaluate cemented sand specimens treated with stabilizers including portland cement (at dosages of 2, 4, 6, 8, and 10%) and six dosages of natural zeolite as partial replacement for cement (0, 10, 30, 50, 70, and 90%) at different curing times of 7, 28, and 90 days. The study further conducts a detailed analysis of life cycle assessment (LCA) to show how partial zeolite replacement for cement impacts the environment. Through a tuning process, the BPNN model found the optimal architecture and accurately predicted the unconfined compressive strength of cement-zeolite improved sand systems. This allowed the AGE-MOEA to optimize zeolite and cement dosages, density, curing time, and environmental impact. Results The results of this study reveal that the optimal range of zeolite was between 30-45%, which not only increased cemented sand strength, but also reduced the cost and environmental impact. It is also shown that increasing the zeolite replacement to 25-30% can increase the ultimate strength of cemented sand, yet exceeding this limit can cause the strength to decrease. Conclusions Zeolite has the potential to serve as an alternative for cement in applications that involve cemented sand, while still achieving mechanical strength performance, which is comparable or even superior. From an LCA standpoint, using zeolite as partial cement replacement in soil improvement projects is a promising alternative. |
| format | Article |
| id | doaj-art-deb0f09272bd43448276d43991ca2318 |
| institution | Kabale University |
| issn | 2046-1402 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj-art-deb0f09272bd43448276d43991ca23182025-08-24T01:00:00ZengF1000 Research LtdF1000Research2046-14022024-04-011310.12688/f1000research.148275.1162564Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved with reservations]Hossein MolaAbasi0Satinder Kaur Brar1Amin Tanhadoust2Sepideh Nasrollahpour3Omolbanin Ataee4https://orcid.org/0000-0001-9695-0585Moncef L. Nehdi5https://orcid.org/0000-0002-2561-993XDepartment of Civil Engineering, Gonbad Kavous University, Gonbad Kavous, Golestan, IranDepartment of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Ontario, CanadaDepartment of Civil Engineering, Isfahan University of Technology, Isfahan, IranDepartment of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Ontario, CanadaDepartment of Geography and Urban Planning, University of Mazandaran, Babolsar, Mazandaran, IranDepartment of Civil Engineering, McMaster University, Hamilton, Ontario, CanadaBackground Cement-zeolite improved sand can be used in diverse civil engineering applications. However, earlier research has not duly optimized its production process to attain best mechanical strength, lowest cost, and least environmental impact. This study proposes a multi-objective optimization approach using back-propagation neural network (BPNN) to predict the mechanical strength, along with an adaptive geometry estimation-based multi-objective evolutionary algorithm (AGE-MOEA) to identify the best parameters for cement-zeolite-improved sand, filling a long-lasting research gap. Methods A collection of unconfined compression tests was used to evaluate cemented sand specimens treated with stabilizers including portland cement (at dosages of 2, 4, 6, 8, and 10%) and six dosages of natural zeolite as partial replacement for cement (0, 10, 30, 50, 70, and 90%) at different curing times of 7, 28, and 90 days. The study further conducts a detailed analysis of life cycle assessment (LCA) to show how partial zeolite replacement for cement impacts the environment. Through a tuning process, the BPNN model found the optimal architecture and accurately predicted the unconfined compressive strength of cement-zeolite improved sand systems. This allowed the AGE-MOEA to optimize zeolite and cement dosages, density, curing time, and environmental impact. Results The results of this study reveal that the optimal range of zeolite was between 30-45%, which not only increased cemented sand strength, but also reduced the cost and environmental impact. It is also shown that increasing the zeolite replacement to 25-30% can increase the ultimate strength of cemented sand, yet exceeding this limit can cause the strength to decrease. Conclusions Zeolite has the potential to serve as an alternative for cement in applications that involve cemented sand, while still achieving mechanical strength performance, which is comparable or even superior. From an LCA standpoint, using zeolite as partial cement replacement in soil improvement projects is a promising alternative.https://f1000research.com/articles/13-257/v1Zeolite; Cemented Sand; Life Cycle Assessment; Artificial Neural Network; Multi-Objective Optimization.eng |
| spellingShingle | Hossein MolaAbasi Satinder Kaur Brar Amin Tanhadoust Sepideh Nasrollahpour Omolbanin Ataee Moncef L. Nehdi Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved with reservations] F1000Research Zeolite; Cemented Sand; Life Cycle Assessment; Artificial Neural Network; Multi-Objective Optimization. eng |
| title | Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_full | Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_fullStr | Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_full_unstemmed | Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_short | Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved with reservations] |
| title_sort | multi objective optimization of sustainable cement zeolite improved sand based on life cycle assessment and artificial intelligence version 1 peer review 1 approved 2 approved with reservations |
| topic | Zeolite; Cemented Sand; Life Cycle Assessment; Artificial Neural Network; Multi-Objective Optimization. eng |
| url | https://f1000research.com/articles/13-257/v1 |
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