Network-driven evaluation of recovery mechanisms in seismic performance of frame structures

Framed structures recovering efficiently after earthquakes is essential, especially in areas that regularly experience seismic disturbances. This study introduces a network-driven approach to evaluating post-earthquake recovery pathways, moving beyond conventional resilience assessments that rely on predefined recovery functions. By using complex network methodologies, the research models the interactions between structural components and repair sequences, enabling a dynamic assessment of how localized damage impacts overall recovery efficiency. The methodology integrates advanced network theory with structural engineering principles to identify critical nodes and links that influence post-earthquake functionality systematically. By constructing a network representation of the structural system, the study captures cascading failure effects and examines the influence of repair sequences on resilience. Unlike traditional models that often neglect repair order effects, this method explicitly incorporates the sequence of repairs, optimizing recovery paths based on network efficiency metrics. Key parameters such as repair prioritization, resource allocation, and downtime are analyzed to quantify resilience. Case studies on Reinforced Concrete (RC) frames subjected to seismic events highlight the importance of network centrality measures in identifying vulnerabilities and optimizing recovery strategies. The study considers realistic constraints to enhance practical applicability, including resource limitations and varying damage states. While the analysis focuses on planar frames, the methodology can be extended to Three-Dimensional (3D) structures with additional considerations for torsional effects. The findings offer understandings into structural design and post-earthquake decision-making, particularly for engineers and urban planners developing resilience-focused repair strategies. Future work aims to refine the approach by incorporating uncertainties in repair resources and comparing network-driven repair strategies with conventional methodologies. By establishing a structured framework for integrating complex network modeling into resilience assessment, this study advances the understanding of recovery mechanisms in seismic engineering.