Szczegóły publikacji
Opis bibliograficzny
Convolutional neural network and support vector machine for prediction of damage intensity to multi-storey prefabricated RC buildings / Adrian JĘDRZEJCZYK, Karol FIREK, Janusz RUSEK // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073. — 2022 — vol. 15 iss. 13 art. no. 4736, s. 1-16. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 14-16, Abstr. — Publikacja dostępna online od: 2022-06-28
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140960 |
|---|---|
| Data dodania do BaDAP | 2022-07-13 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en15134736 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
This paper presents the results of a comparative analysis of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) models created for the prediction of the extent and intensity of damage caused to multi-storey reinforced concrete (RC) buildings. The research was conducted on a group of residential buildings, which were subjected to mining impacts in the form of surface deformations and rock mass tremors during their technical life cycle. Damage to buildings poses a significant threat to the safety of the structure and the serviceability of the buildings. They are often the cause of breaks in thermal insulation, which leads to excessive consumption of thermal energy used for space heating, which in turn contributes to over-emissions of CO2 into the atmosphere. Therefore, this problem is important, not only from a technical dimension, but also includes social, economic, and environmental aspects, which allows it to be classified as an issue of sustainable development in the building industry. As a result of the conducted analysis, among the CNN models, the highest level of classification accuracy was the model obtained using the ADAM (derived from adaptive moment estimation) algorithm, which was also characterized by a very high level of generalization, obtaining 80.35% correctly classified patterns for the training set and 80.52% for the test set. However, its accuracy level was slightly lower than that of the SVM model (85.15% for the training set and 84.42% for the test set), in which Bayesian optimization was used to determine the parameters. The analysis confirmed the effectiveness of the adopted methodology for predicting the extent and intensity of damage. The developed tool can support the optimization of building maintenance management, resulting in reduced economic and environmental expenditures for renovations.
