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Machine learning user preferences for structural design
Deborah L. Thurston
, Ruofei Sun
Industrial and Enterprise Systems Engineering
Civil and Environmental Engineering
Grainger College of Engineering
Research output
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peer-review
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Mathematics
Attribute
43%
Design
28%
Trade-offs
20%
Optimization
14%
Evaluation
14%
Costs
14%
Frame Structure
10%
Process Optimization
8%
Narrative
7%
Steel
7%
Damage
7%
Optimization Algorithm
6%
Integrate
6%
Learning
5%
Optimal Solution
5%
Configuration
5%
Engineering & Materials Science
Structural design
50%
Machine learning
38%
Costs
7%
Numerical methods
5%
Computer aided design
5%
Steel
3%
Earth & Environmental Sciences
machine learning
52%
attribute
24%
cost
7%
seismic design
6%
index
4%
learning
3%
analysis
3%
damage
3%
decision
3%