Impedance spectroscopy analytical method for concrete using machine learning, recording medium and device for performing the method
Provided is an impedance spectroscopy analytical method for concrete using machine learning. The method comprises identifying electrical flow through moisture and a conductive ion present in concrete using a node that measures electricity based on electrochemical impedance spectroscopy (EIS); generating a theoretical equivalent circuit model comprising a conductive path reflecting the electrical flow; normalizing an equivalent circuit reflecting a concrete microstructure based on an impedance experiment using the theoretical equivalent circuit model; and generating a predictive model for estimating a water and cement ratio from a parameter value of the equivalent circuit through machine learning. Accordingly, the accuracy and reliability of estimating the microstructure and mixing ratio of the cement-based material can be increased.
1 . An impedance spectroscopy analytical method for concrete using machine learning comprising:
identifying electrical flow through moisture and a conductive ion present in concrete using a node that measures electricity based on Electrochemical Impedance Spectroscopy (EIS);
generating a theoretical equivalent circuit model comprising a conductive path reflecting the electrical flow;
normalizing an equivalent circuit reflecting a concrete microstructure based on an impedance experiment using the theoretical equivalent circuit model; and
generating a predictive model for estimating a water and cement ratio from a parameter value of the equivalent circuit through machine learning,
wherein identifying the electrical flow applies a three-electrode method using a working electrode (WE), a counter electrode (CE), and a reference electrode.
2 . The method of claim 1 , wherein the concrete microstructure includes a cement matrix and an internal void.
3 . The method of claim 1 , wherein the machine learning uses at least one model of square exponential function Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree.
4 . The method of claim 1 , wherein the parameter value includes resistance and capacitance of the equivalent circuit.
5 . A non-transitory computer-readable storage medium, on which a computer program for performing the impedance spectroscopy analytical method for concrete using machine learning according to claim 1 is recorded.
6 . An impedance spectroscopy analytical apparatus for concrete using machine learning comprising:
an Electrochemical Impedance Spectroscopy (EIS) unit for identifying electrical flow through moisture and a conductive ion present in concrete using a node that measures electricity based on EIS;
an equivalent circuit unit for generating a theoretical equivalent circuit model comprising a conductive path reflecting the electrical flow;
a circuit normalization unit for normalizing an equivalent circuit reflecting a concrete microstructure based on an impedance experiment using the theoretical equivalent circuit model; and
a predictive model unit for generating a predictive model for estimating a water and cement ratio from a parameter value of the equivalent circuit through machine learning,
wherein the EIS unit applies a three-electrode method using a working electrode (WE), a counter electrode (CE), and a reference electrode.
7 . The apparatus of claim 6 , wherein the concrete microstructure includes a cement matrix and an internal void.
8 . The apparatus of claim 6 , wherein the machine learning uses at least one model of square exponential function Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree.
9 . The apparatus of claim 6 , wherein the parameter value includes resistance and capacitance of the equivalent circuit.