- Tytuł:
- Convolutional neural networks in the SSI analysis for mine-induced vibrations
- Autorzy:
-
Zając, Maciej
Kuźniar, Krystyna - Powiązania:
- https://bibliotekanauki.pl/articles/38707462.pdf
- Data publikacji:
- 2024
- Wydawca:
- Instytut Podstawowych Problemów Techniki PAN
- Tematy:
-
deep learning
convolutional neural network
shallow neural network
small data set
soil-structure interaction
mine-induced vibrations
głęboka nauka
splotowa sieć neuronowa
płytka sieć neuronowa
mały zestaw danych
interakcja gleba-struktura
wibracje wywołane minami - Opis:
- Deep neural networks (DNNs) have recently become one of the most often used softcomputational tools for numerical analysis. The huge success of DNNs in the field of imageprocessing is associated with the use of convolutional neural networks (CNNs). CNNs,thanks to their characteristic structure, allow for the effective extraction of multi-layerfeatures. In this paper, the application of CNNs to one of the important soil-structureinteraction (SSI) problems, i.e., the analysis of vibrations transmission from the free-field next to a building to the building foundation, is presented in the case of mine-induced vibrations. To achieve this, the dataset from in-situ experimental measurements,containing 1D ground acceleration records, was converted into 2D spectrogram imagesusing either Fourier transform or continuous wavelet transform. Next, these images wereused as input for a pre-trained CNN. The output is a ratio of maximal vibration valuesrecorded simultaneously on the building foundation and on the ground. Therefore, the lastlayer of the CNN had to be changed from a classification to a regression one. The obtainedresults indicate the suitability of CNN for the analyzed problem.
- Źródło:
-
Computer Assisted Methods in Engineering and Science; 2024, 31, 1; 3-28
2299-3649 - Pojawia się w:
- Computer Assisted Methods in Engineering and Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł