Machine Learning-Based Backbone Curve Model of Reinforced Concrete Columns Subjected to Cyclic Loading Reversals

2018 American Society of Civil Engineers. Backbone curves constructed from experimentally derived hysteresis envelopes are often used to evaluate the force-deformation behavior and, thus, seismic residual collapse capacity of structural components under cyclic loading. This paper proposes a novel machine learning-based backbone curve model (ML-BCV) for rapidly predicting these curves for flexure- and shear-critical columns. The model integrates a multioutput least-squares support vector machine to discover the mapping between input and output variables and a grid search optimization algorithm to facilitate the training process. A database including 262 test columns is utilized to train, test, and validate the ML-BCV model by (1) direct comparison with experimental results, (2) a 10-fold cross-validation procedure, and (3) direct comparison with traditional modeling approaches for three columns. The ML-BCV model reduced the root-mean-square error for the four values governing the shape of the backbone curve by 80% (drift ratio at yield shear), 61% (yield shear force), 58% (drift ratio at maximum shear), and 67% (maximum shear force), demonstrating that the ML-BCV is increasingly robust and accurate compared to traditional modeling approaches.

Machine Learning-Based Backbone Curve Model of Reinforced Concrete Columns Subjected to Cyclic Loading Reversals Academic Article