Nondestructive Concrete Strength Estimation based on Electro-Mechanical Impedance with Artificial Neural Network
Source abstract
Abstract
Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since con-crete is susceptible to fractures, it is essential to confirm the strength development of concrete during the curing process, in order to prevent unexpected collapse. To address this issue, this study proposes an artificial neural network (ANN)-based strength estimation technique using several kinds of strength related factors of concrete materials. In particular, the variations in mechanical properties of concrete were measured through electro-mechanical impedance (EMI) change using an embedded piezoelectric sensor. The ANN was trained to estimate the strength of concrete by using watercement ratio, curing time and temperature, maturity from internal temperature, and 1-CC of the EMI signals. The trained ANN was verified with conventional strength estimation models throughout a series of experimental studies. According to the comparison results, it is noted that the proposed technique could be very effectively applied to estimate the strength of concrete.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Structural Health Monitoring Techniques · Smart Materials for Construction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Safety Engineering, Incheon National University, Incheon, Republic of Korea.
Department of Civil & Environmental System Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
School of Civil & Architectural Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
References (34)
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