A virtual-reality framework for graph-based damage evaluation of reinforced concrete structures

Pedram Bazrafshan; Arvin Ebrahimkhanlou

doi:10.1117/12.2657736

18 April 2023 A virtual-reality framework for graph-based damage evaluation of reinforced concrete structures

Proceedings Volume 12487, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII; 1248707 (2023) https://doi.org/10.1117/12.2657736
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States

Abstract

Artificial intelligence (AI) and virtual/augmented reality (VR/AR) are facilitating objective and fast assessment of infrastructures. Computer vision advancements are also transforming traditional methods into automated information modeling and decision support systems. These advancements offer new opportunities to combat growing challenges that threaten infrastructure systems. In particular, climate change, aging structures, and population growth have intensified threats to infrastructure, requiring methods for evaluating infrastructure quickly after a disaster. VR uses cameras and sensors to provide images of the current state of a structural system, including the pattern of concrete cracks. A novel framework for analyzing the degree of damage in cracked reinforced concrete shear walls (RCSWs) is presented in this paper by leveraging virtual reality (VR) technology. An automated and unbiased approach is enabled by converting images of crack patterns on the surface of concrete structures into graphs. It is possible to extract relevant information from graphs, including graph features, to quantify the extent of damage using graph theory. A machine learning algorithm is then trained using these features to predict the extent of the damage. To validate the approach, the framework was applied to data collected from three RCSWs that were subjected to quasi-static cyclic loading. ML was used to predict the secant stiffness and its descending trend during each load cycle in the experimental test. The VR-based approach achieves high R² scores of 0.90, 0.91, and 0.99 in a machine learning regression, indicating the framework's success.

Conference Presentation

Citation Download Citation

Pedram Bazrafshan and Arvin Ebrahimkhanlou "A virtual-reality framework for graph-based damage evaluation of reinforced concrete structures", Proc. SPIE 12487, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII, 1248707 (18 April 2023); https://doi.org/10.1117/12.2657736

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