Cement-based materials for self-sensing and structural damage advance warning alert by electrical resistivity
| Autor: | Sardasht S. Weli, Imad Shakir Abbood, Fkrat L. Hamid |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Cement Self sensing Materials science technology industry and agriculture 02 engineering and technology 021001 nanoscience & nanotechnology medicine.disease_cause Compression (physics) 01 natural sciences Electrical resistivity and conductivity Mold 0103 physical sciences medicine Fiber Composite material Mortar 0210 nano-technology Sensitivity (electronics) |
| Zdroj: | Materials Today: Proceedings. 46:615-620 |
| ISSN: | 2214-7853 |
| Popis: | Damage detection and advance warning method of steel fiber mortar are developed by using electrical resistivity. Unsized steel fiber is employed as sensors to generate flow paths of electrons. Four-probe method is used to measure electrical current and voltage. Specimens with different steel fiber ratios, namely 1.5% by weight of cement, 5% by weight of cement, 7.5% by weight of cement, 10% by weight of cement, 12.5% by weight of cement, and 15% by weight of cement, have been examined to explore their effects on the fractional resistance variation and advance warning alert duration. To evaluate the effect of distances between probes, two different mold sizes are employed. The feasibility of this method is the real time monitoring of mortar subjected to real-time monotonic compression loading. The fractional resistivity, which decreases by increasing compression stress up to failure. The results revealed that the higher the steel fiber content, the higher the sensitivity of fractional resistivity curve. Further advantage of real time monitoring of smart mortar is to determine the advance warning alert of complete failure of the smart concrete matrix. The effect of steel fiber ratios on the advance warning alert duration was also examined. The higher the steel fiber content, the longer the advance warning alert duration and the higher the accuracy of advance warning alert. |
| Databáze: | OpenAIRE |
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