This paper explores the use of data-driven approximation algorithms, often called surrogate modelling, in the earlystage design of structures. The use of surrogate models to rapidly evaluate design performance can lead to a more indepth exploration of a design space reduce computational time of optimization algorithms. While this approach has been widely developed and used in related disciplines such as aerospace engineering, there are few examples of its application in civil engineering. This paper focuses on the general use of surrogate modelling in the design of civil structures, and examines six model types that span a wide range of characteristics. Original contributions include novel metrics and visualization techniques for understanding model error, and a new robustness framework that accounts for variability in model comparison. These concepts are applied to a multi-objective case study of an airport terminal design that considers both structural material volume and operational energy consumption.