Real-Time Post-Earthquake Structural Crack Segmentation Using a Quadrupedal Robotic Inspection Platform
APPLIED SCIENCES, cilt.16, sa.4, ss.1-20, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/app16146922
- Dergi Adı: APPLIED SCIENCES
- Derginin Tarandığı İndeksler: Applied Science & Technology Source, Scopus, Science Citation Index Expanded (SCI-EXPANDED), Compendex, INSPEC, Directory of Open Access Journals
- Sayfa Sayıları: ss.1-20
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Karadeniz Teknik Üniversitesi Adresli: Evet
Özet
Post-earthquake structural inspections are critical for public safety and recovery, yet traditional manual assessments are slow, hazardous, and resource-intensive. This paper proposes a novel system that integrates a quadrupedal robot with a deep learning (DL) vision model to rapidly detect structural cracks and damage in the aftermath of earthquakes. A Unitree Go2 quadruped robot, equipped with cameras and sensors, is paired with a YOLOv8 instance segmentation network for near-real-time crack detection and localization. The approach addresses key limitations of manual post-disaster inspections by enabling operator-supervised, near-real-time visual crack screening in hazardous or hard-to-reach areas. The YOLOv8 model is trained on a curated dataset of crack and damage images to support crack detection and segmentation performance, and its advanced segmentation capabilities allow precise delineation of damaged regions. The integrated system is validated on a laboratory-scale concrete–steel frame with simulated damage. Preliminary results demonstrate that the Unitree Go2 quadruped robot can navigate and inspect structural elements while the AI model identifies and segments cracks and surface damage under near-real-time laboratory operating conditions. This work highlights the potential of combining advanced legged robotics and state-of-the-art DL for structural health monitoring (SHM), offering a preliminary visual screening tool that can support operator awareness and help prioritize areas requiring expert structural inspection.