Prediction of Impact Resistance Properties of Concrete Using Radial Basis Function Networks

• Authors: S. Yazici , G. Inan Sezer , A. Sezer
• Languages of publication: EN

Abstracts

EN

This study presents an investigation of the prediction of impact resistance of steel-fiber-reinforced concrete and ordinary concrete specimens. In the experimental part of this study, parameters identifying impact resistance of various concrete mixtures were determined using an impact test machine, in accordance with ACI Committee 544. For this aim, concrete specimens containing three different aggregates (basalt, limestone and natural aggregate) were cured in water at 20°C for 28 days. After curing impact resistance tests were performed on specimens having compressive strength values between 20 and 50 MPa, to determine the blows to initial crack and failure. The specimens were also subjected to splitting tensile strength and ultrasonic pulse velocity tests. Initially, using blows to initial crack and failure, many attempts were made to classify the impact resistance of different types of concrete in terms of the origin of used aggregate, strength properties or ultrasonic pulse velocity, however, this made no sense. The specimens could only be classified in terms of steel fiber presence. Therefore, radial basis function network, which belongs to another kind of unsupervised classifier network, was used to estimate the two above-mentioned impact resistance parameters. In this scope, independent from aggregate origin used in preparation of specimens, compressive strength, splitting tensile strength and ultrasonic pulse velocity of the specimens were used to predict the impact resistance parameters of the concrete specimens. The results revealed that three listed parameters can be used for estimation of blows to formation of initial crack and failure. Scatter plots, root mean square error and absolute value of average residual parameters were used to verify the errors in predictions, which were very low, compared with the uncertainty in test and ambiguity of data in hand.

Keywords

EN

81.70.Bt; 79.20.Ap; 07.05.Mh

Discipline

• 81.70.Bt: Mechanical testing, impact tests, static and dynamic loads(see also 62.20.M- Structural failure of materials; 46.50.+a Fracture mechanics, fatigue, and cracks)
• 07.05.Mh: Neural networks, fuzzy logic, artificial intelligence
• 79.20.Ap: Theory of impact phenomena; numerical simulation

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2017
• Volume: 132
• Issue: 3
• Pages: 1036-1040

Dates

published

2017-09

Contributors

author

S. Yazici

• Ege University, Faculty of Engineering, Department of Civil Engineering, Izmir, Turkey

author

G. Inan Sezer

• Ege University, Faculty of Engineering, Department of Civil Engineering, Izmir, Turkey

author

A. Sezer

• Ege University, Faculty of Engineering, Department of Civil Engineering, Izmir, Turkey

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Identifiers

DOI

10.12693/APhysPolA.132.1036