Szczegóły publikacji
Opis bibliograficzny
Prediction of damage intensity to masonry residential buildings with convolutional neural network and support vector machine / Adrian JĘDRZEJCZYK, Karol FIREK, Janusz RUSEK, Umberto Alibrandi // Scientific Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2045-2322. — 2024 — vol. 14 art. no. 16256, s. 1-13. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 11-13, Abstr. — Publikacja dostępna online od: 2024-07-15
Autorzy (4)
- AGHJędrzejczyk Adrian
- AGHFirek Karol
- AGHRusek Janusz
- Alibrandi Umberto
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 154669 |
|---|---|
| Data dodania do BaDAP | 2024-07-26 |
| Tekst źródłowy | URL |
| DOI | 10.1038/s41598-024-66466-3 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Scientific Reports |
Abstract
During their life cycle, buildings are subjected to damage that reduces their performance and can pose a significant threat to structural safety. This paper presents the results of research into the creation of a model for predicting damage intensity of buildings located in mining terrains. The basis for the research was a database of technical and mining impact data for 185 masonry residential buildings. The intensity of damage to buildings was negligible and ranged from 0 to 6%. The Convolutional Neural Network (CNN) methodology was used to create the model. The Support Vector Machine (SVM) methodology, which is commonly used for analysis of this type of issue, was used for comparisons. The resulting models were evaluated by comparing parameters such as accuracy, precision, recall, and F1 score. The comparisons revealed only minor differences between the models. Despite the small range of damage intensity, the models created were able to achieve prediction results of around 80%. The SVM model had better results for training set accuracy, while the CNN model achieved higher values for F1 score and average precision for the test set. The results obtained justify the adoption of the CNN methodology as effective in the context of predicting the damage intensity of masonry residential buildings located in mining terrains.
