Title:
Estimating Drift Capacity of Reinforced Concrete Columns Using Machine Learning
Author(s):
Liam Pledger, Santiago Pujol, and Reagan Chandramohan
Publication:
Structural Journal
Volume:
123
Issue:
2
Appears on pages(s):
73-86
Keywords:
columns; drift capacity (DC); machine learning (ML); reinforced concrete (RC)
DOI:
10.14359/51749374
Date:
3/1/2026
Abstract:
A machine learning (ML) model is developed using a gradient-boosted decision-tree algorithm to estimate the drift capacity (DC) of reinforced concrete (RC) columns. A reliable estimate of the DC of a structure is critical to both its design and assessment. The DC of a structure is also broadly interpreted as a measure of its seismic vulnerability. The estimated DC from the ML model is compared against that of existing methods using test results from a data set of 341 RC columns subjected to cyclic loading. The mean of the ratio of measured to estimated DC for the developed ML model was 1.0 with a coefficient of variation (CV) of 0.31. In comparison, the regression equation currently adopted in New Zealand and the United States to estimate the DC of RC columns has a mean of 3.13 and a CV of 1.07. Other empirical methods assessed in this study also led to large scatter and no discernible correlation between estimated and measured DC. The developed ML model provides more accurate results than existing methods and can estimate the DC for a broad range of RC columns. The developed model is published under an open-source license and is freely available to practitioners and researchers.
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