A Visual Data-Informed Fiber Beam-Column Model for the Analysis of Residual Hysteretic Behavior of Post-Earthquake Damaged RC Columns

ABSTRACT

Seismic damage to components and structures may cause extensive degradation of the seismic capacities of the damaged buildings. The analysis of the residual hysteretic behavior of post-earthquake damaged components and structures is a critical step in the assessment of their residual seismic capacities. Whereas, the difficulty in quantifying the damage distribution of materials in post-earthquake damaged components leads to the challenge of quantitative analysis of their residual behavior (i.e. residual stiffness, strength, energy dissipation). In this study, a visual data-informed fiber beam-column model was developed to analyze the residual hysteretic behavior of post-earthquake damaged RC columns under cyclic loads. To begin with, the method for quantifying the damage strain fields of the columns according to their inspected visual data was proposed. Then, the stress-strain relations of the materials considering the effect of damage strain fields were constructed, and the visual data-informed fiber beam-column model was proposed for the analysis of the residual hysteretic behavior of post-earthquake damaged RC columns subjected to cyclic lateral loads. At last, the proposed model was validated by comparing model results with existing test data. The comparison shows that the proposed visual data-informed fiber beam-column model can accurately analyze the concrete crushing surfaces, the residual load-displacement responses, and the energy dissipation capacities of post-earthquake damaged RC columns, facilitating the evaluation of the residual seismic capacities of post-earthquake damaged RC columns.

KEYWORDS:

• Post-earthquake
• damage distribution
• RC columns
• visual data
• residual behavior
• fiber beam-column

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study is sponsored by the National Natural Science Foundation of China [Grant Number: 52278531] and the Independent Research and Development project of the State Key Laboratory of Green Building in Western China [Project Number: LSZZ202226].