Ensemble learning based sustainable approach to rebuilding metal structures prediction
Abstract The effective implementation of the European Green Deal is based on closing cycles by means of reusing products and extending their durability, especially for steel products in the construction industry. The Life Cycle Assessment gives an opportunity to determine the potential impact caused...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84996-8 |
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| author | Tetiana Vlasenko Taras Hutsol Vitaliy Vlasovets Szymon Glowacki Tomasz Nurek Iryna Horetska Savelii Kukharets Yuriy Firman Olexandra Bilovod |
| author_facet | Tetiana Vlasenko Taras Hutsol Vitaliy Vlasovets Szymon Glowacki Tomasz Nurek Iryna Horetska Savelii Kukharets Yuriy Firman Olexandra Bilovod |
| author_sort | Tetiana Vlasenko |
| collection | DOAJ |
| description | Abstract The effective implementation of the European Green Deal is based on closing cycles by means of reusing products and extending their durability, especially for steel products in the construction industry. The Life Cycle Assessment gives an opportunity to determine the potential impact caused on the environment by building structures and it is used mainly at the early design stage. At the same time, there are significant gaps when it comes to predicting properties of steel products at the last stage of the life cycle of existing buildings in the End of Life Stage (C1-C4) phases and especially D—Benefits and Loads Beyond the System Boundary. This paper uses machine learning (ML) in order to solve the problem of predicting the reusability of construction steel based on the determination of its yield strength by a non-destructive magnetic method. This will give an opportunity to make informed decisions when using this steel again. The research uses machine learning approaches that include regression problems. However, the use of ensemble learning significantly improves quality and accuracy of the results, while demonstrating its advantage in combining multiple models for obtaining more accurate predictions. The research results show that the WeightedEnsemble ensemble method (which includes 8 models) has the best prediction accuracy (MSE = 441 MPa and RMSE = 21 MPa). This method has high accuracy and low delay of conclusion (IL = 0.119 s) when predicting the tensile strength limit (MPa) based on the data of non-destructive testing of structural steel products. . The innovation of the development lies in the ability to provide an automated tool to support informed decision-making for the reuse of building steel for construction site professionals. The accuracy of the ensemble model and its potential for integration with existing practices indicate significant progress in managing steel reuse processes at the final stage of the building life cycle – stage D. |
| format | Article |
| id | doaj-art-e3c5d4e45e8c47369b047a6903527346 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e3c5d4e45e8c47369b047a69035273462025-01-12T12:21:31ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-84996-8Ensemble learning based sustainable approach to rebuilding metal structures predictionTetiana Vlasenko0Taras Hutsol1Vitaliy Vlasovets2Szymon Glowacki3Tomasz Nurek4Iryna Horetska5Savelii Kukharets6Yuriy Firman7Olexandra Bilovod8Department of Management, Business and Administration, State Biotechnology UniversityDepartment of Mechanics and Agroecosystems Engineering, Polissia National UniversityDepartment of Mechanical Engineering, Lviv National Environmental UniversityDepartment of Fundamentals of Engineering and Power Engineering, Institute of Mechanical Engineering, Warsaw University of Life Sciences (SGGW)Department of Biosystem Engineering, Institute of Mechanical Engineering, Warsaw University of Life Sciences (SGGW)Ukrainian University in Europe – FoundationDepartment of Mechanical, Energy and Biotechnology Engineering, Agriculture Academy, Vytautas Magnus UniversityHigher Educational Institution “Podillia State University”Department of Industry Engineering, Poltava State Agrarian UniversityAbstract The effective implementation of the European Green Deal is based on closing cycles by means of reusing products and extending their durability, especially for steel products in the construction industry. The Life Cycle Assessment gives an opportunity to determine the potential impact caused on the environment by building structures and it is used mainly at the early design stage. At the same time, there are significant gaps when it comes to predicting properties of steel products at the last stage of the life cycle of existing buildings in the End of Life Stage (C1-C4) phases and especially D—Benefits and Loads Beyond the System Boundary. This paper uses machine learning (ML) in order to solve the problem of predicting the reusability of construction steel based on the determination of its yield strength by a non-destructive magnetic method. This will give an opportunity to make informed decisions when using this steel again. The research uses machine learning approaches that include regression problems. However, the use of ensemble learning significantly improves quality and accuracy of the results, while demonstrating its advantage in combining multiple models for obtaining more accurate predictions. The research results show that the WeightedEnsemble ensemble method (which includes 8 models) has the best prediction accuracy (MSE = 441 MPa and RMSE = 21 MPa). This method has high accuracy and low delay of conclusion (IL = 0.119 s) when predicting the tensile strength limit (MPa) based on the data of non-destructive testing of structural steel products. . The innovation of the development lies in the ability to provide an automated tool to support informed decision-making for the reuse of building steel for construction site professionals. The accuracy of the ensemble model and its potential for integration with existing practices indicate significant progress in managing steel reuse processes at the final stage of the building life cycle – stage D.https://doi.org/10.1038/s41598-024-84996-8European Green DealMachine learningPredictionMetal structuresSustainable approach |
| spellingShingle | Tetiana Vlasenko Taras Hutsol Vitaliy Vlasovets Szymon Glowacki Tomasz Nurek Iryna Horetska Savelii Kukharets Yuriy Firman Olexandra Bilovod Ensemble learning based sustainable approach to rebuilding metal structures prediction Scientific Reports European Green Deal Machine learning Prediction Metal structures Sustainable approach |
| title | Ensemble learning based sustainable approach to rebuilding metal structures prediction |
| title_full | Ensemble learning based sustainable approach to rebuilding metal structures prediction |
| title_fullStr | Ensemble learning based sustainable approach to rebuilding metal structures prediction |
| title_full_unstemmed | Ensemble learning based sustainable approach to rebuilding metal structures prediction |
| title_short | Ensemble learning based sustainable approach to rebuilding metal structures prediction |
| title_sort | ensemble learning based sustainable approach to rebuilding metal structures prediction |
| topic | European Green Deal Machine learning Prediction Metal structures Sustainable approach |
| url | https://doi.org/10.1038/s41598-024-84996-8 |
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