Developing data-driven surrogate models for holistic performance-based assessment of mid-rise RC frame buildings at early design

Abstract

This paper presents a framework to develop generalizable surrogate models to predict seismic vulnerability and environmental impacts of a class of buildings at a particular location. To this end, surrogate models are trained on a performance inventory, here simulation-based seismic and environmental assessments of 720 mid-rise concrete office buildings of variable topology in Charleston, South Carolina. Five surrogate models of multiple regression, random forest, extreme gradient boosting, support vector machine and k-nearest neighbors were trained in a machine-learning pipeline including hyperparameter tuning and cross-validation. Variance-based sensitivity and accumulated local effect analysis were performed on the most accurate model to identify the most influential parameters and interpret the trained surrogate model. Support vector machines achieved the highest accuracy for total annual loss with an average 10-fold adjusted R2 of 0.96, whereas simpler linear regression was adequate to estimate the initial and seismic-induced embodied carbon emission. Floor area, building height, lateral-resisting frame weight, and average beam section sizes were found to be the most influential features. As these features may be approximated by an experienced structural engineer the results indicate that, with suitable performance inventories available, it should be possible to employ surrogate models in early design to narrow the initial design space to highly resilient and sustainable configurations.