Predictive equation for Automated Damage Assessment of Reinforced Concrete Columns

Earthquakes have the potential to induce significant structural damage, particularly in reinforced concrete columns, resulting in an increased risk of collapse and substantial repair costs. Traditional methods of damage assessment primarily rely on subjective and time-consuming visual inspections. This study proposes an automated approach for assessing damage by utilizing structural features of reinforced concrete columns. These structural features include the compressive strength of concrete (fc'), the axial load demand-to-capacity ratio of the column (P⁄Agfc'), the longitudinal reinforcement ratio (ρc), and the longitudinal and transverse strength of rebars (Fyl, Fyv). Additionally, geometric properties such as the height-to-width ratio are extracted from a comprehensive database comprising ۱۰۰ images of damaged concrete columns subjected to cyclic quasi-static loading. By employing symbolic regression, a predictive equation for the peak experienced drift ratio is derived. The results demonstrate a strong correlation coefficient of ۰.۷۲, indicating a meaningful relationship between the damage state of the columns and the aforementioned structural properties. The proposed method offers a rapid and accurate means of assessing damage, reducing the reliance on manual inspections, and improving the safety and efficiency of damage assessment processes.

کلیدواژه ها:

Structural health monitoring ، symbolic regression ، reinforced concrete column ، predictive equation