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Machine learning user preferences for structural design
Deborah L. Thurston
, Ruofei Sun
Industrial and Enterprise Systems Engineering
Research output
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peer-review
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Dive into the research topics of 'Machine learning user preferences for structural design'. Together they form a unique fingerprint.
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Keyphrases
Structural Design
100%
Design Optimization
100%
Machine Learning
100%
User Preference
100%
Computer-aided
66%
Design Evaluation
66%
Structural Optimization
66%
Design Analysis
66%
Multiple Attributes
66%
Process Optimization
33%
Design Process
33%
Numerical Methods
33%
Process Evaluation
33%
Design Objectives
33%
Optimization System
33%
Optimization Algorithm
33%
Multi-attribute
33%
Multi-attribute Utility Theory
33%
Conventional Design
33%
Performance Attributes
33%
Evaluation System
33%
Seismic Design
33%
Damage Index
33%
Configuration Analysis
33%
Design Configuration
33%
Evaluation Analysis
33%
Overall Design
33%
Steel Frame Structure
33%
User-specific
33%
Decision Theoretic
33%
User-interactive
33%
Cost Index
33%
Evaluation Optimization
33%
Specific Considerations
33%
User Willingness
33%
Computer Science
Machine Learning
100%
User Preference
100%
Structural Design
100%
Design Optimization
75%
Multiple Attribute
50%
Analysis Technique
25%
Optimization Algorithm
25%
Numerical Methods
25%
Design Objective
25%
Process Optimization
25%
Interactive User
25%
Performance Attribute
25%
Engineering
Structural Design
100%
Design Optimization
75%
Numerical Methods
25%
Redesign
25%
Design Process
25%
Design Objective
25%
Design Analysis
25%
Seismic Design
25%
Frame Structure
25%
Steel Frame
25%
Computer Aid
25%