Data-driven approximation algorithms for rapid performance evaluation and optimization of civil structures

Stavros Tseranidis, Nathan C. Brown, Caitlin T. Mueller

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


This paper explores the use of data-driven approximation algorithms, often called surrogate modeling, in the early-stage design of structures. The use of surrogate models to rapidly evaluate design performance can lead to a more in-depth exploration of a design space and 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 modeling 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.

Original languageEnglish (US)
Pages (from-to)279-293
Number of pages15
JournalAutomation in Construction
StatePublished - Dec 1 2016

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


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