Akademik Veri Yönetim Sistemi
Journal of Structural Engineering & Applied Mechanics (Online), cilt.7, sa.2, ss.123-135, 2024 (Hakemli Dergi)
Efflorescence is a common problem in concrete and masonry structures. It occurs
when soluble salts in the material are dissolved by water, and then carried to the
surface where the water evaporates, leaving behind the salt deposits. In recent years,
the construction industry has seen the development of deep learning methods to
detect defects and damages such as efflorescence during the service life of structures.
These advanced techniques offer more efficient and accurate ways to identify and
address issues, improving the overall quality and durability of structures. This study
aims to identify efflorescence damage using an innovative deep-learning model. In
this scope, 617 different images were taken showing efflorescence defects on
concrete and plaster surfaces. A custom dataset was created by labeling the
efflorescence regions from the images taken and this dataset was trained using the
YOLOv8x model. The accuracy, recall, precision, mAP50, and mAP50-95
performance measures obtained from the YOLOv8x model are 0.962, 0.902,0.991,
0.851, and 0.6334 respectively. According to these performance metrics,
efflorescence damage was considerably detected. Finally, the model was tested by
taking efflorescence damage images from different concrete and plaster surfaces.
The study indicated that detecting efflorescence defects on light-colored surfaces in
the model was difficult, with a low detectability rate. Algorithms can be developed
to detect efflorescence damage more effectively on light-colored surfaces.