A Novel Life Prediction Model Based on Monitoring Electrical Properties of Self-Sensing Cement-Based Materials
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
3.1. Fatigue Tests
3.2. Sensing Capability under Fatigue Test Conditions
3.3. G-Value Concept and Its Relationship with Damage
3.4. Fatigue Life Model Based on G-Value
3.5. Remaining Life Model Based on G-Value
4. Conclusions
- −
- The concept of the G-value was defined as the slope of the electrical response baseline of the self-sensing concrete as a function of the number of cycles. It reflects the rate of the damage created in the concrete per each cycle at the specific stress level in the fatigue test.
- −
- Higher absolute values of the G-value indicate that more severe damage growth conditions occur, and more destructive external effects affect the concrete member.
- −
- A novel damage curve, log (G)–log (N), was developed as an alternative to traditional fatigue curves, S-log (N), based on the electrical response of self-sensing concrete estimated through a linear regression approach.
- −
- The log (G)–log (N) diagram can be used to determine the level of damage in concrete infrastructure members based on the continuous monitoring of the electrical response in time. This result can be useful to monitor the infrastructure behavior under different loading conditions and any other destructive effects. This highlights the potential of using this type of material as an embedded sensor for estimating the remaining life of concrete infrastructure and helping to decide the proper structural maintenance action.
Author Contributions
Funding
Institutional Review Board Statement
Conflicts of Interest
References
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Type of Additive | Label | Diameter (nm) | Length | Tensile Strength (GPa) | Purity | Specific Gravity (gr/cm3) | Electrical Conductivity (Ohm·cm) |
---|---|---|---|---|---|---|---|
Multi-walled carbon nanotubes | MWCNT | 10–20 | About 100 | About 1.5 | < |
Cement | MWCNTs | Fine Sand | Coarse Sand | Fine Gravel | Coarse Gravel | Water | |
---|---|---|---|---|---|---|---|
kg/m3 | 380 | 0.57 | 980.397 | 172.4 | 574.416 | 191.224 | 178.592 |
Percentage | 15.34 | 0.023 | 39.58 | 6.96 | 23.19 | 7.72 | 7.21 |
No. | Width (mm) | Span (mm) | Height (mm) | Failure Force (kN) | Bending Stress (MPa) |
---|---|---|---|---|---|
1 | 71.0 | 250 | 75.5 | 5.20 | 4.82 |
2 | 70.3 | 250 | 75.9 | 4.69 | 4.34 |
3 | 70.6 | 250 | 75.4 | 4.85 | 4.53 |
4 | 70.1 | 250 | 75.2 | 4.96 | 4.69 |
5 | 70.8 | 250 | 74.9 | 4.72 | 4.46 |
6 | 71.6 | 250 | 75.8 | 5.07 | 4.62 |
Average bending stress (MPa) | 4.58 | ||||
Standard deviation of bending stress (MPa) | 0.029 |
Fatigue Model | SSE | Adjusted R-Square | RMSE |
Log (G) = 12.77 − 4.881 × log (N) | 1.576 | 0.9585 | 0.3138 |
G = 160.5 − 64.25 × log (N) | 2843 | 0.6839 | 13.33 |
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Adresi, M.; Tulliani, J.-M.; Lacidogna, G.; Antonaci, P. A Novel Life Prediction Model Based on Monitoring Electrical Properties of Self-Sensing Cement-Based Materials. Appl. Sci. 2021, 11, 5080. https://doi.org/10.3390/app11115080
Adresi M, Tulliani J-M, Lacidogna G, Antonaci P. A Novel Life Prediction Model Based on Monitoring Electrical Properties of Self-Sensing Cement-Based Materials. Applied Sciences. 2021; 11(11):5080. https://doi.org/10.3390/app11115080
Chicago/Turabian StyleAdresi, Mostafa, Jean-Marc Tulliani, Giuseppe Lacidogna, and Paola Antonaci. 2021. "A Novel Life Prediction Model Based on Monitoring Electrical Properties of Self-Sensing Cement-Based Materials" Applied Sciences 11, no. 11: 5080. https://doi.org/10.3390/app11115080