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

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dc.authorid0000-0003-2696-2446
dc.authorid0000-0003-1250-0590
dc.authorid0000-0003-0553-2682
dc.contributor.authorÇatal Reis, Hatice
dc.contributor.authorTürk, Veysel
dc.contributor.authorÜstüner, Mustafa
dc.contributor.authorKaya Yıldız, Çağla Melisa
dc.contributor.authorTatlı, Ramazan
dc.date.accessioned2025-03-14T12:16:06Z
dc.date.available2025-03-14T12:16:06Z
dc.date.issued2025
dc.departmentAÇÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümü
dc.description.abstractEarthquakes 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.
dc.identifier.doi10.1007/s12145-024-01574-2
dc.identifier.endpage18
dc.identifier.issn1865-0473
dc.identifier.issue1
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11494/5445
dc.identifier.volume18
dc.identifier.wosWOS:001389179100004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.institutionauthorÜstüner, Mustafa
dc.institutionauthorid0000-0003-0553-2682
dc.language.isoen
dc.publisherSPRINGER
dc.relation.ispartofEARTH SCIENCE INFORMATICS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBuilding crack detection
dc.subjectCNN-based deep learning algorithm
dc.subjectDeep feature extraction
dc.subjectFeature scaling
dc.subjectKahramanmaras earthquake
dc.subjectMachine learning algorithms
dc.titlePost‑seismic structural assessment: advanced crack detection through complex feature extraction using pre‑trained deep learning and machine learning integration
dc.typeArticle

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