Post-seismic structural assessment: advanced crack detection through complex feature extraction using pre-trained deep learning and machine learning integration

• Published in: Earth science informatics 2025, Vol.18 (1), p.133, Article 133
• Main Authors: Reis, Hatice Catal, Turk, Veysel, Ustuner, Mustafa, Kaya Yildiz, Cagla Melisa, Tatli, Ramazan
• Format: Article
• Language: English
• Subjects: Algorithms; Architecture; Artificial intelligence; Building damage; Buildings; Complexity; Cracks; Damage assessment; Damage detection; Datasets; Decision trees; Deep learning; Dimensional analysis; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Earthquake damage; Earthquakes; Feature extraction; Information Systems Applications (incl.Internet); Machine learning; Ontology; Principal components analysis; Seismic activity; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Support vector machines
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-024-01574-2

Earthquakes can often cause significant damage to buildings. After an earthquake, experts/managers need to make quick and accurate damage assessments. Traditionally, manual analysis processes have been widely used in damage assessment studies. The fact that these methods are time-consuming and based on human observation leads to certain limitations in damage assessment studies. In recent years, artificial intelligence techniques such as deep learning and machine learning have frequently been preferred in damage detection studies, and significant success has been achieved. This study aimed to automatically detect cracks/damages in the buildings in Diyarbakir city after the February 6, 2023 Kahramanmaras, Turkey earthquake. Our experimental dataset was collected by the researchers and named Kahramanmaras-Diyarbakir Earthquake Building Crack Dataset (KDBECD-2023). The data set consists of four categories in terms of damage level: undamaged, slightly damaged, moderately damaged, and heavily damaged buildings. DenseNet201 deep learning architecture and popular machine learning algorithms, Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) were used to classify cracks at different damage levels. In the experimental phase, feature extraction was performed with the DenseNet201 architecture. In addition, dimensional reduction was applied with the Principal Component Analysis method to reduce the computational complexity of the proposed hybrid study. According to the experimental results, the DenseNet201-KNN hybrid model gave the most successful result with an accuracy value of 94.62%. The results of this study can make important contributions to decision makers and experts in detecting cracks and damages in buildings after an earthquake.