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- MakaleYalnız künye2025A deep neural network combined with a two-stage ensemble model for detecting cracks in concrete structures
Reis, Hatice Catal; Turk, Veysel; Kaya Yildiz, Cagla Melisa; Bozkurt, Muhammet Furkan; Karagoz, Seray Nur; Ustuner, Mustafa
Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes. Traditional inspection techniques are costly, time-consuming, and inefficient regarding human resources. Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency. Deep learning models (DLMs) stand out as an effective solution in crack detection due to their features such as end-to-end learning capability, model adaptation, and automatic learning processes. However, providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic. In this article, three different methods are proposed for detecting cracks in concrete structures. In the first method, a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network (SCAMEFNet) deep neural network, which has a deep architecture and can provide a balance between the depth of DLMs and model parameters, has been developed. This model was designed using a convolutional neural network, multi-head attention, and various fusion techniques. The second method proposes a modified vision transformer (ViT) model. A two-stage ensemble learning model, deep feature-based two-stage ensemble model (DFTSEM), is proposed in the third method. In this method, deep features and machine learning methods are used. The proposed approaches are evaluated using the Concrete Cracks Image Data set, which the authors collected and contains concrete cracks on building surfaces. The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%, the ViT model 97.33%, and the DFTSEM model 99.00%. These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to real-world problems. In addition, the developed methods can contribute as a tool for BIM platforms in smart cities for building health.
- MakaleYalnız künye2018Earthquake hazard potential in the Eastern Anatolian Region of Turkey: seismotectonic b and Dc-values and precursory quiescence Z-value
Öztürk, S.
The Eastern Anatolian Region of Turkey is one of the most seismically and tectonically active regions due to the frequent occurrence of earthquakes. Thus, the main goal of this study is to analyze the regional and temporal characteristics of seismicity in the Eastern Anatolia in terms of the seismotectonic b-value, fractal dimension Dc-value, precursory seismic quiescence Z-value, and their interrelationships. This study also seeks to obtain a reliable empirical relation between b and Dc-values and to evaluate the temporal changes of these parameters as they relate to the earthquake potential of the region. A more up-to-date relation of Dc = 2:55–0:39*b is found with a very strong negative correlation coefficient (r =–0.95) by using the orthogonal regression method. The b-values less than 1.0 and the Dc-values greater than 2.2 are observed in the Northeast Anatolian Fault Zone, Aşkale, Erzurum, Iğdır and Çaldıran Faults, Doğubeyazıt Fault Zone, around the Genç Fault, the western part of the Bitlis-Zagros Thrust Zone, Pülümür and Karakoçan Faults, and the Sancak- Uzunpınar Fault Zone. In addition, the regions having small b-values and large Z-values are calculated around the Genç, Pülümür and Karakoçan Faults as well as the Sancak-Uzunpınar Fault Zone. Remarkably, the combinations of these seismotectonic parameters could reveal the earthquake hazard potential in the Eastern Anatolian Region of Turkey, thus creating an increased interest in these anomaly regions.
- DergiFrontiers of Structural and Civil Engineering
- DergiFrontiers of Earth Science