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Materials 2015, 8(10), 7169-7178; doi:10.3390/ma8105368

Improving Non-Destructive Concrete Strength Tests Using Support Vector Machines

1
Department of Civil Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan
2
Faculty of Civil and Ecological Engineering Department, I-Shou University, Kaohsiung 84001, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Teen-Hang Meen
Received: 27 August 2015 / Revised: 16 September 2015 / Accepted: 13 October 2015 / Published: 22 October 2015
(This article belongs to the Special Issue Selected Papers from ICASI 2015)
View Full-Text   |   Download PDF [1812 KB, uploaded 22 October 2015]   |  

Abstract

Non-destructive testing (NDT) methods are important alternatives when destructive tests are not feasible to examine the in situ concrete properties without damaging the structure. The rebound hammer test and the ultrasonic pulse velocity test are two popular NDT methods to examine the properties of concrete. The rebound of the hammer depends on the hardness of the test specimen and ultrasonic pulse travelling speed is related to density, uniformity, and homogeneity of the specimen. Both of these two methods have been adopted to estimate the concrete compressive strength. Statistical analysis has been implemented to establish the relationship between hammer rebound values/ultrasonic pulse velocities and concrete compressive strength. However, the estimated results can be unreliable. As a result, this research proposes an Artificial Intelligence model using support vector machines (SVMs) for the estimation. Data from 95 cylinder concrete samples are collected to develop and validate the model. The results show that combined NDT methods (also known as SonReb method) yield better estimations than single NDT methods. The results also show that the SVM model is more accurate than the statistical regression model. View Full-Text
Keywords: concrete compressive strength; non-destructive test; rebound hammer test; ultrasonic pulse velocity test; artificial intelligence; support vector machines concrete compressive strength; non-destructive test; rebound hammer test; ultrasonic pulse velocity test; artificial intelligence; support vector machines
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shih, Y.-F.; Wang, Y.-R.; Lin, K.-L.; Chen, C.-W. Improving Non-Destructive Concrete Strength Tests Using Support Vector Machines. Materials 2015, 8, 7169-7178.

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