Kurt, Berces; Soylak, Mustafa; Köse, Oğuz

In aircraft maintenance operations, the early detection of defects such as cracks, corrosion, impact damage and paint damage on aircraft external surfaces is of critical importance. Traditional visual inspection methods have significant limitations, including lengthy inspection times, high operational costs and results that vary depending on the operator. In this study, an approach combining Unmanned Aerial Vehicle (UAV)-based imaging with deep learning-based image processing techniques is proposed to address these limitations. As part of the study, a dataset comprising high-resolution images of aircraft external surfaces was created, with the aim of automatically detecting paint damage. YOLO-based object detection models (YOLOv11 and YOLO26) were used for defect detection, and model performance was evaluated comparatively through training conducted on the same dataset using the Roboflow and Google Colab platforms. The results obtained demonstrate that the proposed approach provides high accuracy and reliability in the detection of paint damage on aircraft surfaces. Furthermore, it was observed that the choice of training platform influences the model’s temporal consistency in video-based analyses. Tests conducted on different hardware platforms demonstrate that the method is applicable across various systems; however, hardware capacity is a critical factor affecting real-time performance. In conclusion, the proposed UAV-supported deep learning approach offers a faster, non-contact and repeatable inspection method for aircraft maintenance processes, demonstrating significant potential for digitalisation and automation in Maintenance, Repair and Overhaul (MRO) operations.

Ihsan Turan, Ahmet; Celik, Alper; Kumbasaroglu, Atila; Yalciner, Hakan

Earthquakes of magnitude Mw = 7.7 and Mw = 7.6 hit the city of Kahramanmaraş (Turkey) on 6 February 2023. These earthquakes caused serious damage in 11 provinces in Turkey resulting in over 50,000 deaths and huge economic losses with the collapse of thousands of buildings. Two consecutive devastating earthquakes have severely damaged the reinforced concrete building stock located in Malatya. There are many reasons reinforced concrete structures suffer damage under the influence of seismic loads. These reasons can be listed as material and section properties that do not comply with earthquake codes, workmanship defects, ground parameters that are not considered and design errors, as seen in field investigations after earthquakes in Turkey in the past. In this research, a field study was conducted on reinforced concrete buildings damaged after the earthquakes in Malatya province. The causes of structural damages in reinforced concrete buildings between 6–26 years of age were assessed in terms of design and material defects. The structural damages were mainly caused due to insufficient stirrups in structural members, short column, strong beam–weak column, failures of infill walls, soft story type collapse, poor concrete quality, and reinforcement corrosion. In addition to the field investigation, the spectrum relationships created using raw acceleration data obtained from DEMP (Disaster and Emergency Management Presidency) stations in Malatya province were compared with the design response spectra.

Gemici, Zafer; Dogan, Ahmet

In spaces with considerable volumes that are sporadically used and typically not operating at full capacity, existing thermal comfort models face limitations. This research aims to reveal disparities between these models and survey-based comfort assessments. Comfort calculations were conducted using conventional thermal comfort models based on measured parameters and compared with survey data. Notable differences prompted the formulation of a more robust correlation, accounting for 83.4 % of the variations in Predicted Mean Votes (PMV). A regression model derived from survey data shows a high coefficient of determination (R2 = 0.84), supporting its applicability for large intermittently used spaces like mosques. Addressing limitations of existing models, this research introduces a practical and simplified approach to predicting thermal comfort, enhancing assessments in these distinctive spaces. The study also examines mosque indoor air quality dynamics. Despite sporadic ventilation, CO2 levels rose from 1500 ppm to 2500 ppm during Friday prayers, posing potential health risks. Before prayers, indoor CO2 and humidity met ASHRAE Standard 62, ISO 7730, and EN15231 values. To mitigate health concerns from elevated CO2 levels, improved ventilation systems are necessary. Comfort temperatures were only achieved during prayer periods.