15 research outputs found
Kuvvetli Yer Hareketi Deprem Kayıtlarında P-Dalgası Tespiti İçin Varyasyonel Otomatik Kodlayıcılar
No full textEarthquake early warning systems rely on accurate detection of Primary waves before the destructive Secondary waves arrive. However, identifying P-wave onsets in strongmotion accelerograms is challenging due to high noise, limited labeled data, and complex waveforms. This paper proposes a Variational Autoencoder framework for self-supervised P-wave detection in strong-motion data. A Convolutional VAE is trained to reconstruct P-wave segments while rejecting noise and non-P-wave inputs. We employ a sliding window method, combining reconstruction loss and normalized cross-correlation, to locate P-wave arrivals. Experimental results on 1, 2, and 3 second segments show robust performance with area-under-the-curve up to 0.97, demonstrating improved accuracy for longer segments and reduced computational cost for shorter segments
BERT İle Harmonik Zaman Serisi Modelleme: Çok Amaçlı İnce Ayar Yaklaşımı
No full textThis study demonstrates the potential of a BERT-based transformer model in harmonic signal modeling using synthetic sinusoidal data. The model was trained through a three-stage fine-tuning process (reconstruction, linear analysis, full tuning) with a masked language modeling approach. In the first stage, the model successfully filled in missing data and learned the basic features, while in subsequent stages, its ability to capture temporal dependencies and sequential patterns was enhanced. Additionally, patch, time, and station embedding strategies effectively represented the harmonic structure of the signal. The results indicate that pre-training with synthetic data can overcome the limited access to real-world data, allowing transformer models to be efficiently used in these types of problems
Sismik İstasyon Verilerinin Analizi İçin Değişimsel Otomatik Kodlayıcı Eğitimi ve Değerlendirilmesi
No full textIn this study, a variational autoencoder model is proposed to encode the geological, geophysical, and geographical data of seismic stations in Turkey. The model encodes various station-specific data, such as site frequency, surface lithology, and latitude-longitude, into a low-dimensional latent space, aiming to disentangle the generative factors of these data and improve their representation. This approach effectively represents the regional characteristics surrounding the station, enabling the disentanglement of station-related effects. Evaluations based on disentanglement and completeness scores indicate that the model successfully distinguishes station characteristics, such as the average shear wave velocity in the top 30 meters of the surface. The resulting station data encoder can provide additional information to deep learning models processing acceleration records, contributing to a better understanding and modeling of station effects in seismic analysis
Elevators as a Repeatable Excitation Source for Structural Health Monitoring in Buildings
No full textDetermination of Soil-Structure Interaction Effects for a Model Test Structure Using Parametric System Identification Procedures
No full textKuvvetli Yer Hareketi Sinyallerinin Difüzyon Modelleri Yoluyla Yaratımı ve Analizi
No full textThis study examines the potential of Denoising Diffusion Probabilistic Models in earthquake engineering to generate seismic signals and learn deep representations. The complex nature of seismic data and its noise are major obstacles that hinder the extraction of meaningful features. Traditional supervised learning methods are limited in their generalization capacity due to their dependence on labeled data. Diffusion models, however, promise to overcome these limitations by generating conditional seismic signals and enhancing the reliability of early warning systems. This study aims to demonstrate how diffusion-based methods contribute to earthquake engineering and propose an approach for seismic data analysis and detection of the P-wave arrival time. The obtained results show that the model can grasp certain patterns; however, larger-scale datasets are needed for more realistic signal generation and a deeper understanding of seismic features
Dynamic Stiffness and Damping of a Shallow Foundation from Forced Vibration of a Field Test Structure
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