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Maximum flow and minimum cost flow theory to solve the evacuation planning
- 1 The George Washington University
- 2 The George Washington University
* Author to whom correspondence should be addressed.
Abstract
TODO machine learning has made significant advancements in the field of structural health monitoring, offering flexible and efficient solutions for detecting both local and global damage in various infrastructures. Local damage detection focuses on identifying cracks and spalling in specific areas of concrete structures such as bridges, highways, and tunnels. Techniques such as artificial neural networks (ANNs) and deep neural networks (DNNs) have been successfully employed for surface defect recognition, demonstrating their applicability across different structural contexts. Additionally, low-cost methods using devices like smartphones have been explored for quick road integrity assessments, proving to be both practical and affordable. Global damage detection encompasses the classification of structural collapse modes and damage types, utilizing feature extraction and deep learning models to enhance accuracy in identifying large-scale structural failures. These studies underscore the growing role of machine learning and computer vision in improving the resilience and monitoring of infrastructure systems.
Keywords
TODO Machine learning, Structural health monitoring, Local damage detection, Computer vision, Crack detection
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Cite this article
Che,C.;Tian,J. (2024). Maximum flow and minimum cost flow theory to solve the evacuation planning. Advances in Engineering Innovation,12,60-64.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Journal：Advances in Engineering Innovation
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