Estimation of Humidity Variation and Electric Resistivity in Hardened Concrete by Means of a Stainless Steel Voltammetric Sensor
Abstract
:1. Introduction
- (1)
- detecting and verifying if any damage is, or aggressive agents are, present in the structure;
- (2)
- locating any risk zones;
- (3)
- estimating/quantifying harm or the presence of aggressive agents;
- (4)
- making a prognosis of, or predicting, the structure’s service life under these conditions.
2. Experimental
2.1. Sensor System
2.1.1. Electro-Analytical Techniques
2.1.2. Electrodes
2.2. Methodology
- The estimation model of humidity variation: this was obtained with the correlation of the electric resistance (Rs) data obtained with the weight variation of samples after submitting them to different humidity conditions.
- The estimation model of concrete’s electric resistivity: this was obtained with the correlation of the empirical electric resistivity (ρ) and electric resistance (Rs) data obtained with the sensor system.
2.3. Samples and Materials
- the humidity variations in the environment would quickly affect the zone where sensors were;
- sensors were not affected by any uncontrolled defects on concrete surfaces (hollows, etc.).
2.4. Preparing Samples
2.5. Tests
2.5.1. Electric Resistivity Measurement
2.5.2. Concrete Characterization Tests
- Hardened concrete tests. To determine resistance to compression (UNE 12390-3:2009). Resistance to compression was determined at 28 days (fc). Two cylindrical samples were prepared (10 cm diameter, 20 cm high) for each mass at each dose. Total number of samples: 36.
- Determining water absorption, density and accessible porosity for water (UNE 83980:2014). Testing was conducted at 28 days. Two cylindrical samples were prepared (10 cm diameter, 5 cm high) for each mass at each dose. Total number of samples: 36.
- Determining air permeability (UNE 83981:2008). Testing was conducted at 28 days. Two cylindrical samples were prepared (15 cm diameter, 5 cm high) for each mass at each dose. To run the test, sample sides were covered with sealing paint. The air permeability coefficient (k) was obtained. Total number of samples: 36.
- Determining electrical resistivity (ρ): direct (reference) method (UNE 83988-1:2008). Progression over time. Two prismatic samples were prepared (4 × 4 × 16 cm3) for each mass at each dose. Total number of samples: 36.
- Determining the water cover depth under pressure (UNE 83-309-90). A test was conducted at 28 days. For this test, two samples were prepared (15 cm diameter and 30 cm high for each mass at each dose. Total number of samples: 36.
- Accelerated chloride diffusion test according to Standard NT-BUILD 492. A test was run at 28 days. Two samples were manufactured (5 cm diameter, 10 cm high) for each mass at each dose. Total number of samples: 36.
3. Results and Discussion
3.1. Concrete Characterization Tests
- Very low-durability concretes: w/c = 0.9 and w/c = 0.8. These concretes are not covered by Spanish Standard EHE-08 for their structural use. Their use is justified when the sensor needs to be characterized within a wide range of porosities.
- Low-durability concrete: w/c = 0.6.
- Medium-durability concrete: w/c = 0.5.
- High-durability concrete: w/c = 0.4 and w/c = 0.3.
3.2. Humidity and Electric Resistivity Models
3.2.1. Estimation Model of Humidity Variation
Model Validation
3.2.2. Estimation Model of Concrete’s Electric Resistivity
Model Validation
4. Conclusions
- The developed sensor system allows a reliable estimation model of concrete’s humidity variation to be established. It accounts for 88.9% of data variance.
- As Δm/m0 = f(ρ) and Δm/m0 = f(Rs) functions have the same curve morphology, their empirical data fit the same function typology.
- This study demonstrates how the electric field generated in the sensor system cannot be considered uniform because no uniform linear correlation exists between electric resistivity and electric resistance. This means that uniform field simplifications cannot be used to achieve electric resistivity by means of the sensor system.
- With the correlation between functions Δm/m0 = f(ρ) and Δm/m0 = f(Rs), the estimation model of electric resistivity by the parameter is obtained with the sensor system’s Rs. This model offers good reproducibility of reality by accounting for 94% of data variance.
- Working with two electrodes implies there are no limitations to the developed models’ reliability and reduces the energy needed for the system.
- •
- The sensor system presented with the developed models can be coupled to a multi-sensor system, developed according to the smart sensor network concept. Depending on data requirements about the state, this sensor network is capable of collecting interesting data at the necessary points to optimize the system’s data resources, and its economic and energy uses.
- •
- It is possible to collect further information from the electric resistivity estimation to characterize the degree of corrosion with these relations:
- ✓
- The correlation between the electric resistivity value and the presence of chlorides. Initial tests can be conducted to check if the model slightly varies when chloride anions are present in the concrete’s porous solution.
- ✓
- The correlation between concrete’s electric resistivity and the corrosion electric current, which are correlated by Ampère’s Law.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Materials | kg/m3concrete | |||||
---|---|---|---|---|---|---|
w/c = 0.9 | w/c = 0.8 | w/c = 0.6 | w/c = 0.5 | w/c = 0.4 | w/c = 0.3 | |
I 42.5 R-SR5 cement | 225 | 250 | 315 | 385 | 490 | 650 |
Water | 203 | 200 | 189 | 193 | 196 | 195 |
Superplasticizer | 1.6 | 1.8 | 2.2 | 2.7 | 3.4 | 4.6 |
Silica sand | 1433 | 1431 | 1212 | 1179 | 1115 | 1108 |
Gravel | 478 | 477 | 653 | 635 | 601 | 475 |
fc,28days (MPa) | % Abs. Water | % P.A.W | P.W.P (mm) | k (×10−18 m2) | ρ (Ωm) | Dnssm (×10−12 m2/s) | |
---|---|---|---|---|---|---|---|
w/c = 0.9 | 15.46 | 8.65% | 18.38% | 150 | 996.44 | 135.87 | 72.71 |
w/c = 0.8 | 18.98 | 8.38% | 18.24% | 103 | 432.15 | 170.36 | 50.04 |
w/c = 0.6 | 30.74 | 7.82% | 17.00% | 18 | 258.84 | 178.89 | 45.22 |
w/c = 0.5 | 40.50 | 7.49% | 16.42% | 18 | 50.37 | 195.69 | 25.35 |
w/c = 0.4 | 60.94 | 6.58% | 14.63% | 8 | 5.68 | 213.22 | 9.70 |
w/c = 0.3 | 73.52 | 5.65% | 12.80% | 0 | 0.00 | 281.69 | 3.73 |
Avrg Coef V. | 4.24% | 4.45% | 3.86% | 6.99% | 17.33% | 7.87% | 4.24% |
RMSE | Line Slope Real vs. Estimated | R2 | |
---|---|---|---|
Stam mod. | 0.0046 | 0.906 | 0.889 |
Ln mod. | 0.0057 | 0.855 | 0.826 |
Log mod. | 0.0050 | 0.891 | 0.870 |
RMSE | Line Slope Real vs. Estimated | R2 | |
---|---|---|---|
Stam mod. | 0.0053 | 0.818 | 0.835 |
RMSE | Line Slope Real vs. Estimated | R2 | |
---|---|---|---|
Stam mod. | 0.0045 | 0.944 | 0.941 |
Ln mod. | 0.0075 | 0.842 | 0.832 |
Log mod. | 0.0059 | 0.932 | 0.939 |
RMSE | Line Slope Real vs. Estimated | R2 | |
---|---|---|---|
Stam mod. | 36.444 | 0.905 | 0.940 |
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Martínez Ibernón, A.; Lliso Ferrando, J.; Gasch, I.; Valcuende, M. Estimation of Humidity Variation and Electric Resistivity in Hardened Concrete by Means of a Stainless Steel Voltammetric Sensor. Sensors 2022, 22, 7279. https://doi.org/10.3390/s22197279
Martínez Ibernón A, Lliso Ferrando J, Gasch I, Valcuende M. Estimation of Humidity Variation and Electric Resistivity in Hardened Concrete by Means of a Stainless Steel Voltammetric Sensor. Sensors. 2022; 22(19):7279. https://doi.org/10.3390/s22197279
Chicago/Turabian StyleMartínez Ibernón, Ana, Josep Lliso Ferrando, Isabel Gasch, and Manuel Valcuende. 2022. "Estimation of Humidity Variation and Electric Resistivity in Hardened Concrete by Means of a Stainless Steel Voltammetric Sensor" Sensors 22, no. 19: 7279. https://doi.org/10.3390/s22197279
APA StyleMartínez Ibernón, A., Lliso Ferrando, J., Gasch, I., & Valcuende, M. (2022). Estimation of Humidity Variation and Electric Resistivity in Hardened Concrete by Means of a Stainless Steel Voltammetric Sensor. Sensors, 22(19), 7279. https://doi.org/10.3390/s22197279