Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques.

Jong Yil Park,Tae Keun Oh,Young Geun Yoon,Sung-Han Sim

doi:10.3390/ma13132886

Abstract

The static elastic modulus (Ec) and compressive strength (fc) are critical properties of concrete. When determining Ec and fc, concrete cores are collected and subjected to destructive tests. However, destructive tests require certain test permissions and large sample sizes. Hence, it is preferable to predict Ec using the dynamic elastic modulus (Ed), through nondestructive evaluations. A resonance frequency test performed according to ASTM C215-14 and a pressure wave (P-wave) measurement conducted according to ASTM C597M-16 are typically used to determine Ed. Recently, developments in transducers have enabled the measurement of a shear wave (S-wave) velocities in concrete. Although various equations have been proposed for estimating Ec and fc from Ed, their results deviate from experimental values. Thus, it is necessary to obtain a reliable Ed value for accurately predicting Ec and fc. In this study, Ed values were experimentally obtained from P-wave and S-wave velocities in the longitudinal and transverse modes; Ec and fc values were predicted using these Ed values through four machine learning (ML) methods: support vector machine, artificial neural networks, ensembles, and linear regression. Using ML, the prediction accuracy of Ec and fc was improved by 2.5–5% and 7–9%, respectively, compared with the accuracy obtained using classical or normal-regression equations. By combining ML methods, the accuracy of the predicted Ec and fc was improved by 0.5% and 1.5%, respectively, compared with the optimal single variable results.

Highlights

During the design, construction, and maintenance of concrete structures, static elastic modulus (Ec) and compressive strength are critical properties for analyzing structural stability parameters such as member force, stress, deflection, and displacement [1,2]
The concrete specimens were composed of Type I Portland cement, river sand, crushed granite with a size of up to 25 mm, and supplementary cementitious materials (SCMs), i.e., fly ash and slag cement
The coefficient of variation (COV), which is equal to the standard deviation (i.e., σ divided by the average values of the specimen set μ), was used to evaluate the experimental variability of the static and dynamic measurements of the concrete specimens

Summary

Introduction

Construction, and maintenance of concrete structures, static elastic modulus (Ec) and compressive strength (fc) are critical properties for analyzing structural stability parameters such as member force, stress, deflection, and displacement [1,2]. These properties are indicators of concrete deterioration. The specimen cores of existing concrete structures are typically extracted and tested to determine Ec and fc, according to the recommended ASTM. The standard testing methods for determining Ec and fc cannot be used to evaluate the entire

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