Title:
A Framework to Set Performance Requirements for Structural Component Models: Application to Reinforced Concrete Wall Shear Strength
Author(s):
Matias Rojas-Leon, Saman A. Abdullah, Kristijan Kolozvari, and John W. Wallace
Publication:
Structural Journal
Volume:
121
Issue:
1
Appears on pages(s):
75-88
Keywords:
machine learning; model performance; statistics; structural wall; wall shear
DOI:
10.14359/51739186
Date:
1/1/2024
Abstract:
Numerous models to predict the shear strength of reinforced
concrete structural walls have been proposed in the literature.
Evaluation of the predictive performance of new models relative
to existing models is often challenging because the models were
created with different levels of complexity and calibrated using
different databases. More complex models are expected to have
less variance than simpler models, and target performance metrics
for models of different complexity do not exist. In addition, a
common, comprehensive database should be used to enable direct
comparisons between different models. To address these issues, the
present study applies statistical and machine-learning approaches
to propose a five-step framework to establish target performance
metrics for models with different levels of complexity. Application
of the framework is demonstrated by addressing the problem of
estimating wall shear strength using a comprehensive database of
340 shear-controlled wall tests.