Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County
This study introduces a novel approach to building condition assessment (BCA) by combining traditional manual grading with machine learning models—artificial neural networks (ANNs) and random forests (RFs). Individual building components (e.g., windows, roofs, and floors) were assessed based on thei...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/9/1511 |
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| author | Hana Begić Juričić Hrvoje Krstić |
| author_facet | Hana Begić Juričić Hrvoje Krstić |
| author_sort | Hana Begić Juričić |
| collection | DOAJ |
| description | This study introduces a novel approach to building condition assessment (BCA) by combining traditional manual grading with machine learning models—artificial neural networks (ANNs) and random forests (RFs). Individual building components (e.g., windows, roofs, and floors) were assessed based on their remaining useful life using an Excel-based system. The resulting total building grades were used to train and validate ANN and RF models. Performance was evaluated using <i>R</i><sup>2</sup>, mean squared error (<i>MSE</i>), root mean squared error (<i>RMSE</i>), coefficient of variation of <i>RMSE</i> (<i>CVRMSE</i>), and mean absolute percentage error (<i>MAPE</i>). The ANN model outperformed RF in the training set (<i>R</i><sup>2</sup> = 0.987, <i>MAPE</i> = 0.50%) and showed high accuracy in validation (<i>R</i><sup>2</sup> = 0.940, <i>MAPE</i> = 2.55%). The RF model also performed well (<i>R</i><sup>2</sup> = 0.942, <i>MAPE</i> = 2.66%), confirming its viability. External validation on data from outside Osijek-Baranja County confirmed model robustness, with ANN again achieving better performance (<i>R</i><sup>2</sup> = 0.799, <i>MAPE</i> = 7.71%) than RF (<i>R</i><sup>2</sup> = 0.747, <i>MAPE</i> = 9.17%). |
| format | Article |
| id | doaj-art-ab0ed528df7142989e84e942d6f05faa |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-ab0ed528df7142989e84e942d6f05faa2025-08-20T02:59:14ZengMDPI AGBuildings2075-53092025-04-01159151110.3390/buildings15091511Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja CountyHana Begić Juričić0Hrvoje Krstić1Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, CroatiaFaculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, CroatiaThis study introduces a novel approach to building condition assessment (BCA) by combining traditional manual grading with machine learning models—artificial neural networks (ANNs) and random forests (RFs). Individual building components (e.g., windows, roofs, and floors) were assessed based on their remaining useful life using an Excel-based system. The resulting total building grades were used to train and validate ANN and RF models. Performance was evaluated using <i>R</i><sup>2</sup>, mean squared error (<i>MSE</i>), root mean squared error (<i>RMSE</i>), coefficient of variation of <i>RMSE</i> (<i>CVRMSE</i>), and mean absolute percentage error (<i>MAPE</i>). The ANN model outperformed RF in the training set (<i>R</i><sup>2</sup> = 0.987, <i>MAPE</i> = 0.50%) and showed high accuracy in validation (<i>R</i><sup>2</sup> = 0.940, <i>MAPE</i> = 2.55%). The RF model also performed well (<i>R</i><sup>2</sup> = 0.942, <i>MAPE</i> = 2.66%), confirming its viability. External validation on data from outside Osijek-Baranja County confirmed model robustness, with ANN again achieving better performance (<i>R</i><sup>2</sup> = 0.799, <i>MAPE</i> = 7.71%) than RF (<i>R</i><sup>2</sup> = 0.747, <i>MAPE</i> = 9.17%).https://www.mdpi.com/2075-5309/15/9/1511building condition assessmentfacilities managementschool buildingsbuilt environment |
| spellingShingle | Hana Begić Juričić Hrvoje Krstić Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County Buildings building condition assessment facilities management school buildings built environment |
| title | Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County |
| title_full | Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County |
| title_fullStr | Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County |
| title_full_unstemmed | Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County |
| title_short | Developing a Framework for Building Condition Assessment of Schools in Osijek-Baranja County |
| title_sort | developing a framework for building condition assessment of schools in osijek baranja county |
| topic | building condition assessment facilities management school buildings built environment |
| url | https://www.mdpi.com/2075-5309/15/9/1511 |
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