Fundamentals of Controlled Demolition in Structures: Real-Life Applications, Discrete Element Methods, Monitoring, and Artificial Intelligence-Based Research Directions

YÜZBAŞI J.

Buildings, cilt.15, sa.19, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 19
  • Basım Tarihi: 2025
  • Doi Numarası: 10.3390/buildings15193501
  • Dergi Adı: Buildings
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: artificial intelligence (AI), controlled demolition, convolutional neural network (CNN), discrete element methods (DEM), Extreme Gradient Boosting (XGBoost), solid mechanics, structural engineering, vision-language models (VLMs)
  • Çukurova Üniversitesi Adresli: Evet

Özet

Controlled demolition is a critical engineering practice that enables the safe and efficient dismantling of structures while minimizing risks to the surrounding environment. This study presents, for the first time, a detailed, structured framework for understanding the fundamental principles of controlled demolition by outlining key procedures, methodologies, and directions for future research. Through original, carefully designed charts and full-scale numerical simulations, including two 23-story building scenarios with different delay and blasting sequences, this paper provides real-life insights into the effects of floor-to-floor versus axis-by-axis delays on structural collapse behavior, debris spread, and toppling control. Beyond traditional techniques, this study explores how emerging technologies, such as real-time structural monitoring via object tracking, LiDAR scanning, and Unmanned Aerial Vehicle (UAV)-based inspections, can be further advanced through the integration of artificial intelligence (AI). The potential Deep learning (DL) and Machine learning (ML)-based applications of tools like Convolutional Neural Network (CNN)-based digital twins, YOLO object detection, and XGBoost classifiers are highlighted as promising avenues for future research. These technologies could support real-time decision-making, automation, and risk assessment in demolition scenarios. Furthermore, vision-language models such as SAM and Grounding DINO are discussed as enabling technologies for real-time risk assessment, anomaly detection, and adaptive control. By sharing insights from full-scale observations and proposing a forward-looking analytical framework, this work lays a foundation for intelligent and resilient demolition practices.