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

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.

A Review of the Piezoelectric Electromechanical Impedance Based Structural Health Monitoring Technique for Engineering Structures

“…The authors conclude by stating that the approach proposed in the work has the potential to be applied to in situ hydration monitoring of reinforced concrete structures. More research in the field of concrete strength prediction can be found in recent studies [ 69 , 70 , 71 , 72 , 73 , 74 , 75 ].…”

Piezoceramic-Based Damage Monitoring of Concrete Structure for Underwater Blasting

“…www.mdpi.com/journal/sensors associated damage index based on wavelet-packet analysis was developed to help interpret the data. The development of smart-aggregate technology accelerated research on piezoceramic transducers in concrete structures in many applications, such as hydration monitoring [49][50][51], impact detection [52][53][54][55], blast-damage monitoring [56], bond-slip or debonding detection [57][58][59], and concrete compactness monitoring [60,61].…”

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

“…In another study, Castro et al investigated the feasibility of using low-cost piezoelectric sensors to identify partial discharges in the mineral insulating oil of power transformers [ 18 ]. In a recent study, Kim and Park [ 19 ] proposed a novel method for estimation of the early age strength of concrete by introducing an artificial neural network algorithm to process the dynamic response measurements of concrete structures. They used both electromechanical impedances and guided ultrasonic waves signals, which were obtained from an embedded piezoelectric sensor module.…”

“…Methods based on Neural Networks (NN) have been widely proposed in the context of SHM applications [ 19 , 26 , 27 , 28 ]. Recently, new classes of artificial networks, such as Probabilistic Neural Network (PNN) and Fuzzy ARTMAP Network (FAN), have been proposed based on their considerable performances, such as improved accuracy rates and reduced time consumption.…”