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Comparison of NDT Data Fusion for Concrete Strength using Decision Tree and Artificial Neural Network

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Title Comparison of NDT Data Fusion for Concrete Strength using Decision Tree and Artificial Neural Network
 
Creator Dauji, Saha
 
Subject Design life
Multiple performance measures
Non-destructive testing
Rebound number
Ultrasonic pulse velocity
 
Description 831-840
Fusion of Non-Destructive Test (NDT) data results in more accurate estimation of concrete strength when compared to any
single NDT data. Estimation of concrete strength from NDT results assumes importance for health assessment and evaluation of
existing concrete buildings, particularly those near the end of their design life. Application of machine learning tools and response
surface method has found popularity in recent years for this purpose. In this study, universally popular Artificial Neural Network
(ANN) and relatively un-explored Decision Tree (DT) are applied to estimate concrete strength from rebound number and
ultrasonic pulse velocity data collected from literature, in single and combined forms. A ranking system based on ratios of multiple
performance measures was demonstrated for cases where different models are adjudged better considering different performance
measures. From the results, it was concluded that fusion of NDT data resulted in better accuracy, for both ANN and DT.
Comparing the selected performance measures as well as the ranks of the two machine learning tools, ANN models were found to
perform better as compared to the DT models. The narrow range of multiple performance metrics obtained for three different data
divisions (into modelling and evaluation sets) in all cases imparted confidence in the robustness of the approach of model
development adopted in this study.
 
Date 2023-08-09T04:33:31Z
2023-08-09T04:33:31Z
2023-08
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/62413
https://doi.org/10.56042/jsir.v82i08.3048
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(08) [August 2023]