This study focuses on the identification of collapsed buildings in satellite images after earthquakes through deep learning-based image segmentation models. The performance of four different architectures, namely U-Net, LinkNet, FPN, and PSPNet, was evaluated using various performance metrics, such as accuracy, precision, recall, F1 score, specificity, AUC, and IoU. The study used satellite images taken from the area located in the south and southeast of Türkiye covering the eleven provinces which are most affected by the Mw 7.7 Pazarcık (Kahramanmaraş) and Mw 7.6 Elbistan (Kahramanmaraş) earthquakes. The results indicated that FPN and U-Net were the best-performing models depending on the performance metric of interest. FPN achieved the highest accuracy and specificity scores, as well as the best precision score, while U-Net achieved the best recall and F1 score values, as well as the best AUC and IoU scores. The training and validation accuracy and loss curves were analyzed, and the results indicated that all four models achieved an accuracy value of over 96%. The FPN model outperformed the others in terms of accurately segmenting images while maintaining a low loss value. This study provides insights into the potential of deep learning-based image segmentation models in disaster management and can be useful for future research in this field.

The use of timber as a construction material is based on much older dates than concrete and steel. Therefore, the preservation of historical timber structures such as bridges, monuments, towers, mosques, etc. is very important for cultural heritage. The perspective of preservation for this kind of structures has been developed and has gained more importance in the last two decades. However, there are some issues that need to be addressed. Timber is an anisotropic material and is not fire resistant. Also, moisture causes significant swelling. So, structural behavior should be carefully evaluated by experimental techniques before and after restoration and verified numerically using finite element models. This article presents a detailed study on the structural condition assessment of a restored historical timber mosque: its finite element analysis (FEA), ambient vibration tests, model updating to minimize the differences and reflect the current situation, and dynamic analyses before and after updating procedure. The Kuşluca historical timber mosque located in the Sürmene District of Trabzon, Turkey was selected as an application. The mosque was built in the early 20th century and includes two floors. Significant timber decorative elements are available in the entrance door, minbar, and mihrab. The restoration projects began in 2008, and the restored mosque was opened in 2011. Finite element modeling of the mosque was accomplished using SAP2000 software (Computers and Structures, Inc., Walnut Creek, California), considering the restoration project drawings. Modal analysis was performed using orthotropic material properties, considering literature review, to determine the initial dynamic characteristics. Nondestructive experimental measurements were conducted after construction to validate the numerical results using in situ testing. Ambient vibration-based system identification was employed using the Enhanced Frequency Domain Decomposition method in the frequency domain and Stochastic Subspace Identification method in the time domain. The first three natural frequencies were obtained between 5.160 Hz to 7.153 Hz and 3.960 Hz to 5.873 Hz numerically and experimentally, respectively. There is close agreement between mode shapes, but 30.0 % differences in natural frequencies. To minimize the differences, the finite element model of the timber mosque is updated using the manual model updating procedures, with a changing of material properties to reflect the real structural behavior. The maximum differences are reduced to below the acceptable limits as 5 %. To evaluate the structural behavior and determine the model updating effect, linear dynamic-time history analyses are performed, and displacements with internal forces are compared to each other.