Data-driven approximation algorithms for rapid performance evaluation and optimization of civil structures
Name
Tseranidis-Brown-Mueller_2016_Submitted-Version.pdf
Description
Submitted Manuscript
Size
1.28 MB
Format
Adobe PDF
Checksum (MD5)
e661192922e06d4bd32a55ccb14c1b99
Author(s)
Tseranidis, Stavros
Brown, Nathan Collin
Mueller, Caitlin T
Date Issued
December 2016
Journal
Automation in Construction
Publisher
Elsevier
Citation
Tseranidis, Stavros, Nathan C. Brown, and Caitlin T. Mueller. “Data-Driven Approximation Algorithms for Rapid Performance Evaluation and Optimization of Civil Structures.” Automation in Construction 72 (December 2016): 279–293.
Version
Author's final manuscript
Abstract
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. Key Words: surrogate modelling, machine learning, approximation, structural design
MIT Department
Massachusetts Institute of Technology. Department of Architecture
Massachusetts Institute of Technology. Computation for Design and Optimization Program
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.autcon.2016.02.002
