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Sensors 2017, 17(6), 1319; doi:10.3390/s17061319

Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals

1
Department of Civil & Environmental System Engineering, Sungkyunkwan University 2066, Seobu-ro, Jangan-gu, Suwon-si, 16419 Gyonggi-do, Korea
2
School of Civil & Architectural Engineering, Sungkyunkwan University 2066, Seobu-ro, Jangan-gu, Suwon-si, 16419 Gyonggi-do, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: M. H. Ferri Aliabadi and Zahra Sharif Khodaei
Received: 15 March 2017 / Revised: 2 June 2017 / Accepted: 5 June 2017 / Published: 7 June 2017
(This article belongs to the Special Issue Sensor Technologies for Health Monitoring of Composite Structures)
View Full-Text   |   Download PDF [5936 KB, uploaded 7 June 2017]   |  

Abstract

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process. View Full-Text
Keywords: early-age concrete strength estimation; artificial neural network; electromechanical impedance; harmonic wave; embedded piezoelectric sensor early-age concrete strength estimation; artificial neural network; electromechanical impedance; harmonic wave; embedded piezoelectric sensor
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Kim, J.; Lee, C.; Park, S. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals. Sensors 2017, 17, 1319.

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