INCOOPERATING MACHINE LEARNING FOR RAPID BLAST RESILENCE ASSESSMENT
Document Type
Conference Proceeding
Publication Date
7-21-2022
Abstract
Numerous resilience frameworks have been introduced to assess the post-blast functionality and the expected recovery time for critical infrastructures. As such, several researchers have developed blast assessment diagrams. However, usually the blast assessment diagrams are computationally expensive and require prolonged processing time. In this context, this paper introduces a novel concept of developing blast assessment diagrams using different machine learning models. As for demonstration, the developed models will be focused on concrete masonry walls as considered the front defense lines in critical infrastructures. Throughout the different machine learning tested models, the developed ANN model showed the best performance. The proposed approach opens the gate for similar applications considering different infrastructure components.
Recommended Citation
Salem, Shady and Torky, Islam Mr, "INCOOPERATING MACHINE LEARNING FOR RAPID BLAST RESILENCE ASSESSMENT" (2022). Civil Engineering. 145.
https://buescholar.bue.edu.eg/civil_eng/145