Do all roads lead to Rome? A comparison of knowledge-based, data-driven, and physics-based surrogate models for performance-based early design

A performance-based early design must assess the life cycle performance of a sizable design space at low computational cost and limited data. This paper evaluates the relative capabilities of three surrogate modeling approaches to estimate seismic loss under complete and incomplete design information scenarios. Three surrogate models of knowledge-based (i.e., from prior published assessments), data-driven (i.e., support vector machine trained on a simulation-based building inventory), and physics-based (i.e., equivalent single-degree-of-freedom systems) are systematically used to estimate bounds on direct seismic loss for four hypothetical concrete office construction projects in Charleston, South Carolina. Subsequently, a framework is presented that implements these surrogate models as a sequence to explore design alternatives consistent with divergence-convergence cycles of early design exploration. The results show that all different surrogate models provide reasonable accuracy for the complete design information case, whereas data-driven models provided higher accuracy than the other models. For incomplete design information, the data-driven models demarcated the performance space and estimated the same median loss values as detailed loss analysis. In contrast, physics-based surrogate models were more accurate in capturing the relationship between loss and design parameters for smaller sets of design alternatives. Nevertheless, all different surrogate modeling techniques were inadequate to capture loss variability between different designs of the same geometry.