Non-destructive measurement of the air content of hardened concrete

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Volume Title

Insinööritieteiden korkeakoulu | Master's thesis

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Date

2018-09-24

Department

Major/Subject

Mcode

ENG27

Degree programme

Master's Programme in Building Technology (CIV)

Language

en

Pages

92+18

Series

Abstract

Concrete is the most widely used construction material in the world. Its mechanical and durability performance as well as relatively low cost make it unique among other materials. However, these performance properties of hardened concrete, as with all scientific research, may reveal uncertainties when going into depth with investigations. One of these uncertainties involves the air content of concrete. The input of air in concrete is necessary to improve the frost resistance. Typically, frost resistant concretes have a design air content target value of 4-6% by volume. However, recent investigations have revealed that higher air content levels were in frost resistant concrete than expected. This thesis is part of the problem-solving mechanism that addresses this situation. The thesis focuses on the non-destructive measurement of the air content of hardened concrete. The challenge in this field is that there is no such method. The aim of this study is to evaluate if the current technologies in concrete or other fields of research are feasible for the estimation of the air void content of concrete. A thorough research is carried out in the literature study on prior and current technologies in the fields of Computer Tomography, Ground Penetrating Radar, Rebound Hammer, Ultrasound Pulse Velocity, and Machine Learning. The laboratory investigation comprises of the preparation of 9 different concretes in three groups of w/c ratio and differing air contents ranging from 2% to 10% by volume. The concretes are measured by a GPR equipment for the determination of the permittivity and the Rebound Hammer and the Ultrasound Pulse Velocity equipment. Finally, several images were taken from the concrete surfaces for Machine Learning classification. Based on the results, the SonReb experiment has the potential to provide a model for the estimation for the air void content if the model is further investigated on a wide range of concrete mix designs. The Machine Learning experiment has the potential to classify a concrete surface based on the surface texture with the right approach. The thesis proves that the method can be improved and suggests the direction for further investigations in this field. The success of estimation of air content from the permittivity remains impractical. The use of GPR to measure the permittivity is proven to be successful, yet the sensitivity of the dielectric value makes this approach currently impractical.

Description

Supervisor

Punkki, Jouni

Thesis advisor

Al-Neshawy, Fahim

Keywords

concrete air content, non-destructive measurements, machine learning, ground penetrating radar, rebound hammar, ultrasound pulse velocity

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https://urn.fi/URN:NBN:fi:aalto-201810055308

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[dipl] Insinööritieteiden korkeakoulu / ENG