Author: Elia Mercedes Alonso Guzmán
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.
Quality tests applied to hydraulic concrete such as compressive, tension, and bending strength are used to guarantee proper
characteristics ofmaterials. All these assessments are performed by destructive tests (DTs). The trend is to carry out quality analysis
using nondestructive tests (NDTs) as has been widely used for decades.This paper proposes a framework for predicting concrete
compressive strength and modulus of rupture by combining data from four NDTs: electrical resistivity, ultrasonic pulse velocity,
resonant frequency, and hammer test rebound withDTs data.Themodel, determined fromthemultiple linear regression technique,
produces accurate indicators predictions and categorizes the importance of each NDT estimate. However, the model is identified
fromall the possible linear combinations of the available NDT, and it was selected using a cross-validation technique. Furthermore,
the generality of the model was assessed by comparing results from additional specimens fabricated afterwards.