Prediction of the Static Modulus of Elasticity Using Four non Destructive Testing
The static modulus of elasticity (Es) is an important parameter in the analysis of hydraulic concrete structures, changes have been made to the regulation of construction; these changes require minimum values for the Es, so now, in addition to concrete compressive strength (f´c) also Es values shoul...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | México |
| Institución: | Universidad Michoacana de San Nicolás de Hidalgo |
| Repositorio: | Redalyc-UMSNH |
| OAI Identifier: | oai:redalyc.org:127631777004 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=127631777004 |
| Access Level: | acceso abierto |
| Palabra clave: | Ingeniería Hydraulic Concrete Nondestructive Tests Compressive Strength Static Modulus of Elasticity Dynamic Modulus of Elasticity |
| Sumario: | The static modulus of elasticity (Es) is an important parameter in the analysis of hydraulic concrete structures, changes have been made to the regulation of construction; these changes require minimum values for the Es, so now, in addition to concrete compressive strength (f´c) also Es values should be ensured. A methodology to predict Es is proposed, specifically, the Es were modeled by testing: ultrasonic pulse velocity (UPV), electrical resistivity test (ERT), resonance frequency test (RFT), the Hammer Test Rebound (HTR) and f´c. In order to generate models multiple linear regression technique was used. Cylindrical specimens were prepared in two stages, in the first stage was simulated laboratory conditions in the second stage was simulated conditions of concrete made in situ. All cylinders were subjected to non-destructive and destructive tests at different ages. The research objective is to predict Es from the results of destructive tests (traditionally employed to obtain Es) and nondestructive testing. It was possible to obtain a model whose correlation coefficient indicates the good approximation in the generated predictions. |
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