Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification
In the field of building inspection and diagnosis, uncertainty is common and surveyors are aware of it, although it is not easily measured. This research proposes a model to quantify uncertainty based on the inspection of rendered façades. A Bayesian network is developed, considering three levels of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/6/9/124 |
| _version_ | 1797518832342925312 |
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| author | Clara Pereira Ana Silva Cláudia Ferreira Jorge de Brito Inês Flores-Colen José D. Silvestre |
| author_facet | Clara Pereira Ana Silva Cláudia Ferreira Jorge de Brito Inês Flores-Colen José D. Silvestre |
| author_sort | Clara Pereira |
| collection | DOAJ |
| description | In the field of building inspection and diagnosis, uncertainty is common and surveyors are aware of it, although it is not easily measured. This research proposes a model to quantify uncertainty based on the inspection of rendered façades. A Bayesian network is developed, considering three levels of variables: characteristics of the building, façade and exposure conditions; causes of defects; and defects. To compute conditional probabilities, the results of an inspection campaign from the literature are used. Then, the proposed model is validated and verified using inspection results from another sample, the combination of a strength-of-influence diagram and sensitivity analysis and the application of the model to a case study. Results show that the probabilities computed by the model are a reasonable representation of the hesitancy in decision making during the diagnosis process based only on visual observation. For instance, design and execution errors show lower probabilities due to not being verifiable a posteriori without detailed documentation. The proposed model may be extended and replicated for other building materials in the future, as it may be a useful tool to improve the perception of uncertainty in a key stage of building maintenance or rehabilitation. |
| first_indexed | 2024-03-10T07:34:59Z |
| format | Article |
| id | doaj.art-8e9c7061b880433f9579f6ced94f3c95 |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-10T07:34:59Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-8e9c7061b880433f9579f6ced94f3c952023-11-22T13:35:48ZengMDPI AGInfrastructures2412-38112021-09-016912410.3390/infrastructures6090124Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model QuantificationClara Pereira0Ana Silva1Cláudia Ferreira2Jorge de Brito3Inês Flores-Colen4José D. Silvestre5CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, PortugalCERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, PortugalCERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, PortugalCERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, PortugalCERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, PortugalCERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, PortugalIn the field of building inspection and diagnosis, uncertainty is common and surveyors are aware of it, although it is not easily measured. This research proposes a model to quantify uncertainty based on the inspection of rendered façades. A Bayesian network is developed, considering three levels of variables: characteristics of the building, façade and exposure conditions; causes of defects; and defects. To compute conditional probabilities, the results of an inspection campaign from the literature are used. Then, the proposed model is validated and verified using inspection results from another sample, the combination of a strength-of-influence diagram and sensitivity analysis and the application of the model to a case study. Results show that the probabilities computed by the model are a reasonable representation of the hesitancy in decision making during the diagnosis process based only on visual observation. For instance, design and execution errors show lower probabilities due to not being verifiable a posteriori without detailed documentation. The proposed model may be extended and replicated for other building materials in the future, as it may be a useful tool to improve the perception of uncertainty in a key stage of building maintenance or rehabilitation.https://www.mdpi.com/2412-3811/6/9/124Bayesian networksbuilding inspection and diagnosisfaçade rendersuncertainty |
| spellingShingle | Clara Pereira Ana Silva Cláudia Ferreira Jorge de Brito Inês Flores-Colen José D. Silvestre Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification Infrastructures Bayesian networks building inspection and diagnosis façade renders uncertainty |
| title | Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification |
| title_full | Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification |
| title_fullStr | Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification |
| title_full_unstemmed | Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification |
| title_short | Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification |
| title_sort | uncertainty in building inspection and diagnosis a probabilistic model quantification |
| topic | Bayesian networks building inspection and diagnosis façade renders uncertainty |
| url | https://www.mdpi.com/2412-3811/6/9/124 |
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