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Defect detection in reinforced concrete using random neural architectures
J.B. Butcher
, C.R. Day
, J.C. Austin
, P.W. Haycock
, D. Verstraeten
, B. Schrauwen
Aston Pharmacy School
College of Health and Life Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
108
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Citations (SciVal)
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Keyphrases
Neural Network Method
100%
Neural Architecture
100%
Defect Detection
100%
Reinforced Concrete
100%
Echo State Network
100%
Extreme Learning Machine
100%
Concrete Structures
100%
First Use
50%
Fast Training
50%
Training Procedure
50%
Efficient Training
50%
Non-invasive Method
50%
Network Architecture
50%
Networked Learning
50%
Electromagnetic Properties
50%
Recurrent Neural Network
50%
Simple Threshold
50%
Anomaly Detection
50%
Traditional Neural Network
50%
Reinforcing Steel
50%
Electromagnetic Anomalies
50%
Built Infrastructure
50%
Engineering
Neural Network Approach
100%
Network State
100%
Defect Detection
100%
Reinforced Concrete
100%
Concrete Structure
100%
Extreme Learning Machine
100%
Collected Data
50%
Electromagnetic Property
50%
Anomaly Detection
50%
Recurrent Neural Network
50%
Steel Rebar
50%
Computer Science
Neural Network Approach
100%
Extreme Learning Machine
100%
Echo State Network
100%
Network Architecture
50%
Collected Data
50%
Recurrent Neural Network
50%
Anomaly Detection
50%
Chemical Engineering
Neural Network
100%
Learning System
100%
Recurrent Neural Network
50%
Anomaly Detection
50%
Material Science
Reinforced Concrete
100%
Steel Rebar
50%