Nuray Beyza Avcı

While there are evident advantages to deploying UAVs for structural inspection applications within reinforced-concrete structures where human access may be hazardous or restricted, UAV deployment is still inhibited within structures due to a lack of GPS, payload and power limitations, signal attenuation, limited transmission opportunities, and low bandwidth for communication. Within the confines of the present project, a low-cost two-layer six-rotor architecture was developed; a single Pixhawk PX4 manages stabilized flight, while a Raspberry Pi 4 manages mission-level operations. The complete prototype integrated a relative-pose/nearby-object estimator, hybrid 433 MHz and Wi-Fi/MQTT communications, avionics-side energy management, mission continuity via SQLite, and human-in-the-loop fail-safe support. Prototype experiments revealed reduced horizontal drift compared to both tested comparison configurations, continued operation under constrained communication conditions, and a 62.0% reduction in avionics-side power consumption, excluding propulsion. This was followed by a complementary PX4–Gazebo evaluation probing horizontal and vertical proximity responses, six-sector LiDAR processing, and stale-data watchdog functionality. Across 45 repeated simulation runs and 3000 retained sector-level observations, no simulated collisions occurred during the horizontal-approach, vertical-proximity, or watchdog tests. No false sector assignments were observed, and the overall mean absolute error was 0.0285 m. From this limited demonstration, the architecture appears satisfactory at the prototype and simulated-subsystem levels. Further physical testing will be needed prior to operational deployment.

Historical buildings are important cultural assets that reflect the identity of cities and preserve the collective memory of societies. However, these structures are increasingly exposed to environmental degradation and human-induced impacts, making their systematic documentation and condition assessment essential for effective conservation strategies. Recent advances in artificial intelligence have provided powerful tools for image-based analysis in the field of heritage preservation. In particular, transfer learning enables the adaptation of pre-trained deep learning models to domain-specific tasks with limited labeled data. In this study, a deep transfer learning-based framework is proposed for automatic damage detection and classification in historical buildings. A new near-balanced dataset of 20,000 images spanning six deterioration categories was developed and made publicly available. Ten convolutional neural network and transformer architectures pre-trained on ImageNet were systematically compared under a unified Bayesian optimization protocol. Experimental results on a held-out test set show that EfficientNetB3 achieves the highest classification accuracy (97.65%), while AlexNet obtains the lowest performance (83.89%); the validation set was used exclusively for hyperparameter tuning. The results demonstrate that transfer learning-based models can effectively identify visually observable deterioration patterns and provide reliable support for automated documentation processes. The proposed framework contributes to the development of data-driven decision-support tools for digital documentation and condition assessment in heritage conservation.

The recent seismic activity in southeastern Turkey in February 2023 again emphasized the critical need to promptly evaluate structural damage to assist in emergency response operations. This study introduces a comprehensive ensemble deep learning approach to structural damage classification following earthquake events, based on a dataset containing 13,270 high-resolution images with 15 different damage classes. Six different state-of-the-art convolutional neural network models (VGG16, ResNet50, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2) are combined using a weighted voting approach to handle extreme class imbalance using weighted categorical cross-entropy loss. An integrated explainability component is incorporated into the trained convolutional neural network models to highlight the image regions that contribute to the predicted damage class, thereby improving the interpretability of deep learning decisions in safety-critical post-disaster assessment scenarios. The performance evaluation results show that the ensemble model achieves a test accuracy of 93.77%, with an increase of 2.67% compared to the best performing model individually. Notably, the ensemble model improves performance in minority classes like collapsed buildings. The proposed framework can be used to provide a powerful approach to structural damage evaluation, balancing accuracy with interpretability, to assist structural engineers in post-earthquake evaluation procedures.