Skip to main navigation
Skip to search
Skip to main content
Penn State Home
Help & FAQ
Link opens in a new tab
Search content at Penn State
Home
Researchers
Research output
Research units
Equipment
Grants & Projects
Prizes
Activities
Truck weigh-in-motion using reverse modeling and genetic algorithms
G. Vala
, I. Flood
,
E. Obonyo
School of Engineering Design and Innovation
Research output
:
Contribution to conference
›
Paper
›
peer-review
1
Link opens in a new tab
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Truck weigh-in-motion using reverse modeling and genetic algorithms'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Genetic Algorithm
100%
Reverse Genetics
100%
Modeling Algorithm
100%
Reverse Modeling
100%
In-motion
100%
Weigh-in
100%
Neural Networks Solution
37%
Neural Network Method
25%
Axle
25%
Bridge Response
25%
Order of Magnitude
12%
Noise Level
12%
Structural Health Monitoring
12%
Inverse Problem
12%
Computation Time
12%
Optimization Techniques
12%
Optimization Problem
12%
Efficient Search
12%
Identification Method
12%
Simply Supported
12%
Minimizing Errors
12%
Stress Model
12%
Bending Stress
12%
Algorithmic Solution
12%
Concrete Deck
12%
Axle Spacing
12%
Rapid Search
12%
Time-varying Load
12%
Axle Load
12%
Measurement Parameters
12%
Bridge Girder
12%
Steel Girder Bridge
12%
Search Optimization
12%
Load Estimate
12%
Real-coded
12%
Engineering
Genetic Algorithm
100%
Solution Network
37%
Bridge Girder
25%
Structural Health Monitoring
12%
Computational Time
12%
Optimisation Problem
12%
Misclassification
12%
Optimization Technique
12%
Optimality
12%
Sampling Frequency
12%
Neural Network Approach
12%
Bending Stress
12%
Stress Model
12%
Imposed Load
12%